Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments.The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic. Sensitivity analysis is the study of the contribution of different parameters to the uncertainty present in the outcome of a system In this study, we perform sensitivity analysis on a complex individual-based stochastic epidemiology model for the study of influenza epidemics. Individual-based models are increasingly used in the study of the dynamics of infectious diseases and evaluation of methods for controlling the spread In several studies, the sensitivity analysis of individual-based models of epidemic dynamics have been used to evaluate the effect of disease parameters on public policy related questions. For example, for a model aimed at simulating Smallpox epidemics, the sensitivity analysis could focus on model assumptions relating to how changes in individual behavior after infection might influence the observed outcomes such as in the study by Burke et al. Susceptible-Exposed-Infectious-Recovered (SEIR) disease model. The four disease states in the SEIR model  and full factorial designs to study the effects of each parameter and joint effects due to interactions between parameters. We also use principal components clustering, analysis of variance, and Pearson correlation to determine the level of sensitivity in the model The complexities of this study lie in quantifying the sensitivity of an individual-based model to changes in parameters which are discrete probability distributions. As shown in The process of developing the individual-based model is described briefly since it is not a novel contribution of this work. A more detailed description of the individual-based model can be found in The synthetic population is created using demographic information, survey and land use data. Synthetic individuals are defined with specific sets of demographic variables and assigned to households based on US census data provided in SF3 and PUMA (Public Use Microdata Area) files de facto standard for travel demand models in transportation science In addition to having specific demographic information, each node in the synthetic population is also allocated activities based on thousands of responses to a time-use survey for a specific geographical region. As expected, there will be differences in the time-use survey collected in different geographical regions such as New York City (and surrounding metropolitan regions) and Montgomery County in Virginia due to demographic differences (hereafter referred to as New York and Montgomery County). The National Household Transportation survey is used in this model to create the activity templates. Activities can include shopping, work, daycare, etc. Individuals in the synthetic populations are matched to the survey households using decision trees based on demographic variables. Activities are then assigned realistic geographical locations using a gravity model based on land-use data The social contact network results from interactions between individuals in the synthetic population based on their activity schedules. Individuals are represented as vertices in a graph and edges are used to describe interactions between individuals . The modUsing a computational model such as the basic SEIR model , diseasei infects a susceptible person j is given by:i and jw is the contact duration between i and j measured in seconds. The SEIR model is one of the simplest disease models which can be used for this individual-based model. Additional information can be added to the disease model to better capture different infectious diseases. In addition, intervention options such as vaccination, antiviral and social distancing are included in the model to control disease spread. Single interventions or a combination of interventions can be introduced either at the start or during a simulated epidemic.For the disease model used in this study, the probability that an infectious person Simulations are run by randomly selecting a number of people to introduce the disease into the population. During each simulation, we keep track of information on all contacts, duration of contact, and which contacts result in infection. Information on the contacts resulting in disease transmission, the vulnerability of the nodes, the epidemic size and epidemic curve are some of the information used in studying the dynamics of an epidemic.The following formulation is used in defining the problem. Given a distribution d to perturb one There are several possible techniques for generating new distributions depending on the aim of the study. A likely procedure would involve placing a probability distribution such as the beta density s and random generation around the same mean. We selected these procedures based on their simplicity and results from preliminary studies which indicated that these techniques are sufficient for investigating the aims in this study.An alternative to the previously described procedure would involve defining the mean duration of incubation or infectiousness while randomly generating new distributions with different variances. This technique would enable the study of the effects of the variance of the incubation and infectious period distributions on the dynamics of the epidemics. In addition, the random generation of distributions would result in distributions with different shapes which might not be epidemiologically relevant for influenza. However, this allows for a mathematically exploration of the parameter space. It also enables the applicability of this procedure to models of other infectious diseases with parameters that are distinct from those based on the natural history of influenza. In this study, we use both perturbations of the The above procedures are for the incubation and infectious period distributions only. For the transmissibility, which is a real number, values are selected to simulate epidemics similar to seasonal influenza, previous pandemics and more extreme epidemics.To perform sensitivity analysis on these three quantities, we explore the mapping Sensitivity analysis involving non-parameterized discrete distributions present special problems that inhibit the uses of standard techniques such as Latin Hypercube Sampling We used statistical methods and tests in the design and analysis of the experiments in this study. Since no statistical methods are universally accepted as infallible, we chose methods based on their applicability to the study attack rate = infected-counts/population size. The peak infected proportion indicates the point of maximum demand of public health resources such as nurses and hospitals during an epidemic. The peak time suggests the available time for implementing control measures such as vaccines, antivirals, and sequestration to achieve optimal effectiveness. Lastly, the total attack rate can be used to quantify the disease's effect based on the morbidity and mortality rates As earlier stated, the epidemic curves were used as model outcomes Analysis of variance (ANOVA) was used to evaluate differences in the mean of the public health measures given changes in the parameters. In its simplest form, ANOVA is an extension of the t-test. ANOVA is typically used to test whether the means of several groups are equal. The groups in this study were the different sets of epidemics/experiments resulting from changes in the parameters. We tested whether the means of the various peak infected proportions, peak times and attack rates for the different sets of epidemics were equal. If a statistically significant difference was observed, we used a pairwise t-test with Bonferroni correction to find which pairs of experiments were statistically different.http://ndssl.vbi.vt.edu/opendata/index.php. The same disease model parameters were used in simulating epidemics over the synthetic social networks. As previously mentioned, random sampling was used in the assignment of the incubation and infectious period to the nodes in the network. Random assignment was selected instead of targeted assignment because it is simpler to implement, and increases the flexibility of designing and running experiments. In addition, random assignment of these parameters has been used in most studies We used two synthetic social networks in order to compare possible effects of demographic and rural-urban differences. The demographic information for the synthetic populations in the social networks were based on the population of Montgomery County and New York with approximately 76000 and 20 million synthetic individuals respectively. The network for Montgomery County is available at In the mono-factorial design, we varied a single parameter and held the others fixed. In the first set of experiments, we altered the incubation and infectious period parameters by shifting the mean by a single day. The main reason for doing this was to evaluate how changes in the mean infectious and incubation period distribution would affect the dynamics of simulated epidemics. This was investigated for two reasons: (i) the natural history of infection for the 2009 H1N1(A) pandemic was similar to that for seasonal influenza We varied each of the parameters over five values and studied the effects of these changes to the dynamics of the simulated epidemics. First, using five transmissibility values, one incubation period distribution and one infectious period distribution, we simulated five sets of epidemics. The five transmissibility values were in the range: In the second set of experiments, we further assessed the effects of the mean and variance of the incubation and infectious period distributions on the dynamics of an epidemic. To evaluate the effects of the variance of the infectious period duration on the dynamics of the simulated epidemics, we randomly created thirteen infectious period and twelve incubation period distributions. The infectious period distributions were defined with possible infectious durations between two and six days. We randomly assigned probabilities to observing each day such that the mean infectious period was four days. Similarly, we also defined incubation period distributions with possible incubation durations between one and three days. The probabilities of observing each of these days in the synthetic population was defined such that the mean incubation period was two for all distributions.We simulated twenty-six epidemics using each of the infectious period distributions, two similar incubation period distributions and one transmissibility. Each epidemic was replicated twenty-five times. These experiments are labeled Exp.5 in Using a full factorial design, we varied each of the parameters across three levels, resulting in twenty-seven combinations/experiments. The parameters used in the full factorial experiments were generated by shifting the mean of the base case distributions by a single day. This resulted in infectious period distributions with means of three, four and five days. The incubation period distributions had a mean of one, two and three days. This variation in the parameters was done for the same reason as the first set of experiments. Each parameter was defined at three levels as shown in Exp.6 in As previously stated, due to observations made during the 2009 H1N1 pandemic, we also evaluated the sensitivity of the model based on disease spread within age groups. The four age groups were: pre-schoolers, school-agers, adults and seniors. The epidemics were simulated using the parameters for Exp.3 in All experiments were implemented in the individual-based model under the base case scenario: no interventions were introduced to control the spread of the epidemic. To capture the heterogeneity present in the model and elucidate the influence of the social networks, each epidemic was replicated between 25 to 200 times. See To uncover the parameter with the most significant influence on the structure of the epidemic curves, we used principal components clustering to find underlying groupings within the 1350 epidemic curves from the full factorial experiments. Typically, replicates of the same epidemic tend to have similar characteristics. Therefore, one would expect epidemics with the same parameters to fall into the same groups if clustered. However, this is not always the case due to the stochastic nature of the model and imperfection of clustering algorithms. In this study, we expected that by clustering the epidemic curves, we would observe patterns in the distribution of epidemics into different clusters based on the levels of the parameters. The parameter having the strongest influence on the structure of the epidemic curves should result in groupings based on different levels.Epidemic curves can be viewed as time series since infected-counts are collected over fixed time intervals (e.g. on a daily or weekly basis). The daily infected-counts can be represented as a vector. The process of clustering based on principal component analysis was carried out as follows: first we estimated principal components based on the variance-covariance matrix of the vectors representing the epidemic curves. Next, we fit a linear regression equation to centered daily infected-counts for each epidemic curve to the nonlinear principal components. Lastly, the regression coefficients were clustered into groups using k-medoids, which is a robust version of the k-means algorithm. For additional information on this process see We present the results by outcome. Recall that the aims of the study are to: (i) evaluate individual and joint effects, and rank parameters based on influence on simulated epidemics and (ii) compare the sensitivity of the model across age groups and social networks with demographic differences. The results are presented only for New York since the observations are similar to that for Montgomery County as discussed in a later section.The results for the peak time and peak infected proportion are displayed in Furthermore, changes in the infectious period distributions affects the peak time and peak infected proportion in a similar manner as the transmissibility. The peak infected proportion rises with an increase in the mean infectious period. Also, epidemics with higher mean infectious periods tend to peak earlier compared to epidemics with a lower mean infectious period. A pairwise comparison using the t-test indicates that the peak infected proportion, peak time and attack rates are statistically significantly different Observations on the transmissibility and mean infectious period are not surprising. The basic reproduction number These observations suggest that the mean infectious and incubation durations might have a role on the dynamics of the simulated epidemics. Therefore, we further investigate the effects of fixing the mean infectious and incubation periods as discussed in the next section.r of −0.289. A similar observation is also made between the variance of the incubation period distribution, mean peak time (r = −0.263) and mean peak infected proportions (r = 0.146)  . This suThe results based on comparing epidemics resulting from infectious period distributions with the same mean values Exp.5 in  are descr = −0.99 and −0.99). On the contrary, the peak time increases with a raise in the variance of the infectious period distribution (r = 0.95)  . These o(P = 0.871).The epidemics simulated based on the full factorial design Exp.6 in  fail to An ANOVA on the total attack rates, peak infected proportions, and peak times indicates that the mean of at least one of the experiments is statistically significantly different from the others Per To reaffirm the findings based on the ANOVA, we use principal components clustering to find underlying groupings within epidemic curves from all experiments. Epidemic curves from the twenty-seven experiments appear to be randomly distributed across clusters, irrespective of parameter combinations . Except The clustering in We also visualized the clustering based on the levels of the transmissibility and incubation period distributions  and S6. The same trends are observed across all experiments for both Montgomery County and New York. Although Montgomery County and New York have both demographic and rural-urban differences, these differences are not apparent in the epidemic outcomes. In The lowest correlations are observed for Exp.4, where we study the effect of the mean and variance of the incubation period distribution on the three public health measures. The conclusions drawn from this set of analysis indicate that the incubation period distributions result in epidemics with similar peak proportion infected and total attack rates. There is no trend observed between the three public health measures and changes in the variances; therefore the lack of significant correlation. However, the overall observations are similar across the social networks. This therefore suggests that the results observed in this sensitivity study are not restricted to a particular network but can easily be applicable to others under certain assumptions.In 90%, 65%, 47% and 35% respectively. Similar attack rates are observed under the other two scenarios. Allowing school-age children to have a higher susceptibility compared to other age groups does not result in higher attack rates compared to the case in which all age groups have the same susceptibility. These findings suggest that neither increasing the mean infectious period nor changing the susceptibility affects the trend of disease spread observed across age groups. The high attack rates observed for school-age children could be attributed to the network degree and average contact time distributions. Children have a mean degree of School-age children have the highest age-specific total attack rates for all epidemics across all scenarios. Preschoolers have the second highest age-specific total attack rates. Adults have third highest age-specific total attack rates and the elderly have the lowest attack rates. For example, under the first scenario, epidemics for which the population have a mean infectious period of five days, school-agers, preschoolers, adults and elderly have a mean age-specific total attack rate of approximately 0.16 respectively. For epidemics with mean infectious duration of four days, we do not observe statistical significant differences in the following age group pairs: ,  and  with P = 0.183, 1.00 and 0.094 respectively. The same pairs do not have statistically different peak times (P = 1.00) for epidemics with mean infectious periods of five and six days. Note that the school-age population is not included in any of the pairs with similar peak times. On the contrary, all pairwise comparisons of the total attack rates resulted in statistically significant differences across all age groups and epidemics  minute changes in the disease parameters significantly increased the peak proportion infected, total attack rate and time to peak. (ii) Knowing the mean of the incubation period distribution appeared to be sufficient in predicting its effects on the dynamics of a simulated epidemic. (iii) The characteristics of the infectious period distribution affected the total attack rate and structure of the epidemic curves. (iv) The sensitivity of the individual-based model was independent of the demographical aspects of the social networks. (v) Differences in age-group susceptibility did not influence the overall trend of disease severity observed within the population. These observations can aid in improving real-time epidemic modeling using similar models.Two important measures to predict during an epidemic are the time to peak and the total attack rate The uncertainty in real-time epidemic modeling and predictions in the early stages of an outbreak are partly due to the imperfection of incidence data However, this study is not without limitations. The space explored for the parameters in the experiments was limited since in most cases, differences in the mean incubation and infectious period distributions lay between 1–3 days. More extreme differences might be observed if more extensive distributions were used. However, one can argue that using parameters which are similar provides a more strenuous analysis on the system, since parameters with significant influences would be more distinguishable from those with minute influences. In addition, assumptions about the disease model were simplified for this study. Additional layers can be included to describe different compartments of the population, such as infected asymptomatic and infected symptomatic individuals. Furthermore, future studies can also evaluate sensitivity in the presence of various intervention strategies and also with additional information such as school opening dates by region In addition, since network structure has been shown to influence disease dynamics The results in this study add to the growing literature of real-time modeling of epidemics Figure S1Results from mono-factorial experiments (Exp.4 in ) focused on changes in the variance of the incubation period distribution. (A) Plot of the variance of the incubation period distribution against the peak proportion infected and (B) plot of variance of the incubation period distribution against the peak time. No obvious pattern is observed in the relationship between the two measures and the incubation period variance.(EPS)Click here for additional data file.Figure S2Results from mono-factorial experiments (Exp.5 in ) focused on changes in the variance of the infectious period distributions. (A) Plot of the variance of the infectious period distribution against the peak proportion infected and (B) figure of the variance of the infectious period distribution against the peak time. Note, the linear trend between the two measures and the variance of the infectious period.(EPS)Click here for additional data file.Figure S3Mean epidemic curves from 50 replicates of each full factorial experiment (Exp.6 in ). Each outbreak is simulated for a duration of 350 days so as to accommodate outbreaks that are similar to previous influenza pandemics, seasonal epidemics and more extreme outbreaks. (A) shows the total proportion of the population infected over time. (B) is the daily proportion of infected individuals. Although the parameters used in producing the epidemics are different, the resulting dynamics are similar for some of the epidemics.(EPS)Click here for additional data file.Figure S4The epidemic curves from all 27 factorial experiments are grouped using principal components cluster analysis. Note that curves in each cluster are grouped by peak time and spread. Different colors are used to differentiate curves resulting from replicates of each of the 27 epidemics. Epidemic curves from all experiments appear to be randomly distributed across clusters, irrespective of parameter combinations.(EPS)Click here for additional data file.Figure S5The epidemic curves from all 27 factorial experiments are grouped using principal components cluster analysis. Different colors are used to differentiate curves from different transmissibility levels. Unlike the groupings observed in the levels of the infectious period distribution Click here for additional data file.Figure S6The epidemic curves from all 27 factorial experiments are grouped using principal components cluster analysis. Different colors are used to differentiate curves from different incubation period distributions. Unlike the groupings observed in the levels of the infectious period distribution Click here for additional data file.Figure S7The effective reproduction number R(t) for mono-factorial experiments for each of the factors: (A) transmissibility, (B) incubation period distribution and (C) infectious period distribution. Each curve represents the mean effective reproduction number based on 25 replicates of each experiment. (A) shows the effective reproduction number given changes in the transmissibility from (EPS)Click here for additional data file.Figure S8Degree distribution for children (A) and adults (B) in the NY social network.The long tails are consistent with what one would expect in the real world. The higher mean degree observed in children might be the reason for the higher age-specific attack rates observed in children.(EPS)Click here for additional data file.Model S1Description of the individual-based model used in this study.(TEX)Click here for additional data file.Table S1Analysis of contributions of the parameters to the variance observed in the total attack rate, peak proportion infected, and peak time. The first row shows the total variance in each of the three outcome measures, while the rows beneath display the fraction of the total variance explained by each factor. t is used to represent transmissibility, inc abbreviates incubation period distribution and infc represents the infectious period distribution. The infectious period distribution explains the highest proportion of the variation observed in all three measures.(TEX)Click here for additional data file.


The WHO recognises pesticide poisoning to be the single most important means of suicide globally. Pesticide self-poisoning is a major public health and clinical problem in rural Asia, where it has led to case fatality ratios 20-30 times higher than self-poisoning in the developed world. One approach to reducing access to pesticides is for households to store pesticides in lockable "safe-storage" containers. However, before this approach can be promoted, evidence is required on its effectiveness and safety.A community-based cluster randomised controlled trial has been set up in 44,000 households in the North Central Province, Sri Lanka. A census is being performed, collecting baseline demographic data, socio-economic status, pesticide usage, self-harm and alcohol. Participating villages are then randomised and eligible households in the intervention arm given a lockable safe storage container for agrochemicals.The primary outcome will be incidence of pesticide self-poisoning over three years amongst individuals aged 14 years and over. 217,944 person years of follow-up are required in each arm to detect a 33% reduction in pesticide self-poisoning with 80% power at the 5% significance level. Secondary outcomes will include the incidence of all pesticide poisoning and total self-harm.This paper describes a large effectiveness study of a community intervention to reduce the burden of intentional poisoning in rural Sri Lanka. The study builds on a strong partnership between provincial health services, local and international researchers, and local communities. We discuss issues in relation to randomisation and contamination, engaging control villages, the intervention, and strategies to improve adherence.NCT1146496 .The trial is registered on ClinicalTrials.gov ref:  Pesticide poisoning is a major public health in rural Asia  and a siRestricting easy access to pesticides in rural households to prevent their use in impulsive self-harm has become a popular recommendation -13. TherPesticide self-poisoning is prevalent in Sri Lanka; it is the most common method of suicide , is highThe pesticide industry has long argued for secure storage and the use of locked boxes to prevent pesticide poisoning, and has started projects testing and scaling up the use of safe storage boxes . It has There have been several small scale pilot projects with high levels of community acceptability Table  23-25].-25.23-25Although in one of the studies 82% of containers were locked securely at 7 months, by 24 months this had dropped to 55% .In additThe main study hypothesis is that the introduction of safe storage boxes to households who use or store pesticides will reduce the incidence of pesticide self-poisoning.This is a community-based; cluster randomised controlled trial (RCT) of safe storage containers.We have set up the community-based cluster randomised control trial in the Anuradhapura district of Sri Lanka's North Central Province . We plan to recruit approximately 162 villages primarily from the Mahaweli H region, including the divisional secretariats of Thambuthegama, Talawa, Galnewa, Rajanganaya and Nochchiyagama,  in the South East area of the district , allowing on-going quality assurance, monitoring of survey coverage, and recording of GPS coordinates for later mapping using a geographical information system. If a household refuses to participate only their location is recorded to ensure they are excluded from further follow up.A census is being performed, collecting baseline demographic , socio-economic status, pesticide usage, previous self-reported self-harm and alcohol use data from each participating household. The questionnaire was tested in surrounding villages to ensure the questions were easily understood and the responses captured the relevant options in the local context. The data is being collected using a Juno Trimble survey device  are validated by data collection managers revisiting a randomly selected (2%) sample of households in each village. The questionnaire is administered in Sinhala by a team of data collectors (high school graduates) recruited from the surrounding areas and trained in field research techniques and use of the questionnaire. Supervisors are present in the field to address any issues when administering the questionnaire or other issues arising from the fieldwork. Subsequent retraining is undertaken on a monthly basis to ensure compliance with the study protocol. Data is managed independently of the field team in Colombo and will be checked using a number of queries in Microsoft Access to improve accuracy.In this cluster randomised trial, villages  are the randomisation unit and are allocated to either the intervention or comparison group. Large imbalances between study arms in the number of allocated villages, the number of villagers in households eligible for a box, and the previous history of pesticide self-harm in the village, are avoided by the method of minimisation. Prior to allocation the village is added to each arm in turn and the differences on the three minimisation variables calculated. The odds of randomly allocating that village to one arm rather than the other is then modified to give an improved chance of the allocation, which achieves better balance between the two arms.Steps have been taken to avoid selection bias during random allocation: (i) Villages are only randomised once household demographic data has been collected from all villages in the study administrative band; after this has been done villages cannot be withdrawn from or added to the study; (ii) The UK-based study statistician, who does not come into contact with study villages or data collectors, uploads data on the minimisation variables to an automated computer randomisation programme. Hence allocation concealment is ensured, it neither being possible to predict allocations, nor to withdraw or recruit villages or households once the allocation is known.Following several pilot studies -25 of thFollowing randomisation the GN official (lowest level of civil administration) in the villages in the intervention arm is informed and arrangements made for distribution of the devices to eligible households. A small demonstration is given and a range of materials are utilised to ensure the box is installed and used correctly. Efforts are made to ensure that the boxes are installed in field, compound or house and members of the distribution team follow up in the village to ensure all eligible people receive a box. Where the distribution team is uncertain about the eligibility of households they are required to bury the device to reduce the likelihood of contamination. Further promotion after distribution is limited to six monthly activities of limited scale in the villages.As an evaluation of intervention adherence, a small sub-study in approximately five villages will be undertaken to examine the use of the safe storage device. Household opinions on the usefulness of the devices will be elicited through a survey and focus groups. In addition, the demographic survey will be repeated in each village at the end of the study to better estimate person years of follow-up and to observe whether the containers are still being used.Our primary and secondary outcome measures are objective. The primary outcome is the incidence of pesticide self-poisoning, both fatal and non-fatal, amongst villagers aged 14 years or older, over the three-year study period. Secondary outcomes will include the incidence of: pesticide poisoning in general , self-harm , self-poisoning  and pesticide poisoning in children (younger than 14 years).Data on cases of fatal and non-fatal pesticide self-poisoning, accidental poisoning and self-harm will be prospectively collected from several sources:Hospitals: Patients admitted to hospital will be identified routinely at Anuradhapura Teaching Hospital (where SACTRC have an ongoing cohort study). Clinical research assistants have been employed to attend daily ward rounds in the four medical wards and weekly checks of admission books in Surgical, Pediatric, Intensive Care wards and the Morgue at Anuradhapura Teaching Hospital; they will be unaware of the village allocation. All rural peripheral hospitals in the district have been visited at least every month by researchers in a previous project  to identify poisoning admissions; this will be continued in 13 hospitals in our study area. Field research officers will visit every peripheral hospital in the study area and neighboring hospitals to identify cases through checks of the admission and transfer books and discussion with relevant staff.• Community: We will visit a sample of villages (10%) at least every two months to interview local key informants including the GN official, public health midwife and local health committee members (Suwasahana). These local committees will assist in linking cases to their households. If this approach improves the linking of cases to their households, we will seek to work with further community health committees during follow-up.• Coroners: Deaths that occur before hospital presentation will be identified through review of the district coroners' and magistrates' records, as has been documented in another study [• er study .Community-based studies in Sri Lanka indicate that practically all patients with poisoning are taken to hospital  and about 80% are transferred to the tertiary hospital . Health The linking of individual cases of self-poisoning and self-harm back to the census data collected from households is complex due to imprecise demographic information. There are complex naming practices, poor recollection of birth dates and no unique identification numbers. Therefore a computer algorithm will match cases to census data using partial names and homophones, approximate ages and partial addresses to generate a list of possible matches that will then be checked manually.The primary analysis will follow the intention-to-treat principle, comparing the observed incidence of self-harm using pesticides between individuals in villages allocated to the intervention, and individuals in villages allocated to the control arm. A Poisson regression model will be employed, with the standard errors inflated to accommodate the clustered design. This analysis will be adjusted for minimisation variables used in the random allocation and for seasonal variation in the incidence of pesticide self-poisoning. This same approach will also be used for the secondary outcome measures.Pre-specified subgroup analyses will investigate whether the effectiveness of the safe storage intervention is modified by the village-level historical rate of self-poisoning (established in the baseline survey) and proportion of households provided with a locked box. We will perform a sensitivity analysis excluding the five intervention villages where sub-studies are undertaken as their participation may increase household compliance with safe storage.Our previous research has found the incidence of self-poisoning in the district to be approximately 350 per 100,000 per year [The sample size for this study needs to be increased to allow for variation in self-harm rates between villages (i.e. the randomisation unit). Using our data on pesticide poisonings in 189 Sri Lankan villages , we estiHowever, some households within intervention villages will not use pesticides (and therefore not be offered a container), while others will not use their container (non-compliance). Some households within control villages may acquire a lockable container (contamination). If 20% of individuals in the intervention arm live in households not using a lockable container, and 5% of individuals in the control arm live in a household using a lockable container, then 217,944 person years of follow-up are required in each arm of the trial to compensate . Our assumptions about the number of households per village, the average number of household members, and the proportion of eligible households in each village, will be monitored as the baseline survey proceeds, and compensatory action (increasing the sample size) will be taken if deviations from those assumptions are likely to challenge the statistical power achieved.Ethics approval was granted from the University of Peradeniya, Sri Lanka in March 2008, with amendments in January and July 2011. The Provincial Department of Health Services and national Ministry of Health have given their support to the study.An independent Data Monitoring Committee (DMC) has been established for the trial. Interim analyses will be supplied by the trial statistician, in strict confidence, to the DMC, together with any other analyses the DMC may request. The DMC will recommend (i) continuation, (ii) modification, or (iii) cessation. Adverse events such as becoming aware of cases of poisoning using pesticides taken from a storage device within the home compound will be reported to the DMC immediately.This study is a large community-based cluster randomised control trial of safe storage devices for agricultural pesticides. The results of the trial will establish the effectiveness of providing 'safe storage' containers for agrochemicals to rural Asian households to reduce intentional and accidental poisoning. They will be highly relevant for other parts of rural Asia and should allow policy makers to judge the usefulness of implementing this intervention.The trial is only possible because a long-term collaboration between the Provincial Department of Health and SACTRC researchers has provided a platform on which to base this large community study. However, there are many issues with conducting the study, some of which are discussed below.To reduce contamination (i.e. use of secure storage in the control arm) due to the exchange of boxes between households, especially those with family connections, this is a cluster randomised trial with village as the unit of randomisation. We had initially planned to use GN boundaries as a proxy for villages, but we found that in some areas there was considerable communication between households in neighbouring GN divisions, and household allegiances which reflected historical rather than current boundaries. In such cases we will combine several GN divisions into a single randomization unit to further reduce the scope for contamination. Where contamination does occur, it could result in the public health impact of the intervention being underestimated.At the end of the study, unless there is evidence that the containers are harmful, households in control villages will be offered a safe storage container. Focus groups were carried out before the study started to determine whether this offer is sufficient to encourage villages to enter the study despite the risk of being randomised to no intervention. These focus groups indicated that villagers consider that the offer of a free safe storage container, together with the great importance of pesticide poisoning to their communities, to be sufficient to encourage villages to enrol.The original protocol included the choice of two devices, an in-ground device for the home-garden or field and a second device to be attached to a wall within the home. However, it was considered a safety risk to bring pesticides into the home where they had previously been stored outside, so it was decided not to distribute the second device. Participants are encouraged to install their device in the field or the furthest corner of their home garden. However, they can choose to install the device in their home.The current study design is an effectiveness study which will assess the effect of the intervention (safe storage device) in a near 'real-life' situation to ensure that such an intervention can be extrapolated to rural communities elsewhere in Asia. In order to ensure that the device is properly used the field team provide village level demonstrations of installation and use of the device. The field team will also audit installation within 2 months to confirm the device has been installed and record its location. Help will be made available to those householders who find installation difficult, e.g. the elderly. However, to improve device use with minimal resources, we are planning to provide community level meetings every quarter in all villages; intervention and control, to encourage the local population to keep their pesticides stored safely.KH received financial support from Syngenta for a pilot study of safer storage of pesticides. ME has received financial support to attend a scientific meeting of a study funded by Syngenta. NAB and AHD have received travel expenses from Syngenta (a manufacturer of paraquat and some other pesticides) to attend meetings of a scientific advisory group in relation to studies of new paraquat formulations.ME, FK and AHD conceived the study based on FK's pilot work. ME, FK, DG, CM, AHD, KH, RP, and MW contributed to the original proposal from which this protocol was developed. MP, FK, ME, AHD, DG, CM, KH, SJ, RW, IG, WA, PB, DD, AR and FM are responsible for the conduct of the trial with ME as principal investigator and FK as chief scientist. NB is the chair of the DMC and has reviewed the protocol and the grant application from an early stage. MP is responsible for the day to day management of the trial and RP, MW and DK lead the fieldwork teams. MP and DK drafted the first version of the manuscript based on the grant application written by ME; MP collated contributions and subsequently redrafted the manuscript. All authors read and approved the final version.The work is supported by the Wellcome Trust (GR090958). ME is a Scottish Senior Clinical Research Fellow (Scottish Chief Scientist Office/Scottish Funding Council) and Lister Research Prize Fellow (Lister Institute for Preventative Medicine). DG and KH are both National Institute for Health Research Senior Investigators.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/879/prepub


Higher brain functions that rely on attention and reward seeking depend on the integrity of neuromodulatory systems using dopamine (DA) and noradrenaline (NA). Despite the importance for these neuromodulators, there have been no direct experimental comparisons of the selectivity of DA and NA neurons, so making statements about the differences in functionality are speculative. We have recorded activity from dopamine neurons of the substantia nigra pars compacta and noradraline neurons of the locus coeruleus in monkeys while the monkeys carried out a visually-cued reward schedule task.In this task, the monkeys perform schedules of 1, 2, or 3 red-to-green sequential color discrimination trials to earn a reward. A cue presented at the beginning of each trial shows how many trials must yet be completed to obtain a reward. Error rates increase in this simple task as the number of trials yet to be performed before a reward increase, even though the red-green discrimination is identical in every trial, indicating that the reward value is discounted as a function of trials remaining before reward . We also find lip movements that occur when cue appears and at bar release (bar release is the correct response to a red->green color change). The lipping at the cue occurs whenever a new schedule begins, and is not related to the number of trials to be carried out. At bar release, lipping is strongest in rewarded trials. We interpret the lipping as reflecting the value of the event at that instant. Both DA and NA neurons seem to reflect the lipping response, with responses occurring when the cue appears in first trials, and at the time of bar-release. The neuronal responses that occur when the cue appears are similar, with both DA and NA neurons showing activations in the first trial of a schedule. At bar release, however, the activity furthest from reward is largest for NA neurons, whereas for DA neurons the greatest activity occurs in the rewarded trials.Our data lead us to suggest that both systems are simultaneously evaluating the subjective or perceived value of the current state, but in different ways. We suggest that the noraderaline system reflects the cost of the current behavior, the cost being higher when the reward is more distant, and the dopamine system reflects the current expected benefit. In a neuroeconomic framework, these two catecholamine systems, almost certainly evolved from a common ancestor, have diverged to represent costs and benefits, for NA and DA, respectively. We speculate that the interaction between these two neuromodulatory systems is critical to motivate value-based behavior, and intimate knowledge of the dynamic balance between them is required to model the motivation underlying higher reward seeking activity.


Estimates of the per-contact probability of transmission between farms of Highly Pathogenic Avian Influenza virus of H7N7 subtype during the 2003 epidemic in the Netherlands are important for the design of better control and biosecurity strategies. We used standardized data collected during the epidemic and a model to extract data for untraced contacts based on the daily number of infectious farms within a given distance of a susceptible farm. With these data, we used a maximum likelihood estimation approach to estimate the transmission probabilities by the individual contact types, both traced and untraced. The estimated conditional probabilities, conditional on the contact originating from an infectious farm, of virus transmission were: 0.000057 per infectious farm within 1 km per day, 0.000413 per infectious farm between 1 and 3 km per day, 0.0000895 per infectious farm between 3 and 10 km per day, 0.0011 per crisis organisation contact, 0.0414 per feed delivery contact, 0.308 per egg transport contact, 0.133 per other-professional contact and, 0.246 per rendering contact. We validate these outcomes against literature data on virus genetic sequences for outbreak farms. These estimates can be used to inform further studies on the role that improved biosecurity between contacts and/or contact frequency reduction can play in eliminating between-farm spread of the virus during future epidemics. The findings also highlight the need to; 1) understand the routes underlying the infections without traced contacts and, 2) to review whether the contact-tracing protocol is exhaustive in relation to all the farm’s day-to-day activities and practices. Highly Pathogenic Avian Influenza (HPAI) is one of the OIE listed poultry diseases. Several epidemics involving these viruses have occurred world-wide since its first description in northern Italy in 1878 Following the detection of the first outbreak, a control programme, as stipulated by the European Union, was implemented. This programme consisted of stamping out of infected flocks, movement restrictions and establishment of protection and surveillance zones. Despite additional control measures such as pre-emptive culling of flocks within a radius of 1 km of an outbreak and establishment of buffer zones between defined areas by complete depopulation of poultry flocks in these zones, there was a continued spread of the virus by mechanisms which are not clearly understood Plausible mechanisms include movements of humans , vehicular traffic , other fomites  and other vectors such as wind, rodents and insects Following potentially infectious contacts i.e. exposures, the probability of HPAI virus transmission may be contact-specific but will also depend on the contact patterns: i.e., the frequency of contacts and the contact network During the Dutch HPAI epidemic in 2003, the National Inspection Service for Livestock and Meat (RVV), responsible for the implementation of animal disease legislation and eradication of outbreaks of OIE listed diseases, was tasked with collecting epidemiological data and tracing of upward and downward contacts to and from infected farms. Using this data, Thomas and co-workers Our analysis aims to give quantitative insight into the role of the different between-farm contacts in the spread of the virus during an epidemic. We focus on the specific contacts that occurred during the HPAI (H7N7) epidemic in the Netherlands and estimate the probability of HPAI virus transmission attributable to each type of contact. Using published genetic data obtained by sequencing most of the samples collected during the epidemic We used two sets of data collected during the Dutch 2003 HPAI epidemic. One of the datasets was collected via a standardized field epidemiology investigation form of the RVV In both datasets we could also find HPAI-related details such as the status and dates of clinical suspicion and stamping out for both the infected source farm and receiving farms. Since we could not identify a potentially infectious traced visit for all the ultimately infected farms, we introduced a category of ‘unknown’ contacts over different distance ranges. A farm was assigned one unknown contact per day when it was in the vicinity of an infectious farm. We chose three distance ranges (and hence three different unknown contact types) namely, 0–1 km, 1–3 km and 3–10 km of an infectious farm and assigned the unknown contacts accordingly. Details of these and all the other visits are given in For each farm (infected or not) in the dataset, we extracted (and tabulated) all its exposures. In the summary table for the analysis, we indicated, for each contacted farm, the type and number of exposures as well as its ultimate status. A farm was deemed exposed if the visit occurred during the period when the virus was likely to have been introduced onto the receiving farm, here referred to as the potential virus-introduction period. Due to the uncertainty about the actual day of virus introduction, both the potential virus-introduction and infectious periods were assumed to begin seven days prior to the day of clinical suspicion, corresponding to the estimated farm infectious periods during the epidemic  for the period after epidemic detection If To estimate these probabilities, we used a maximum-likelihood approach. The likelihood function was given byIn this analysis, we assumed that: 1) the ‘exposure’ period started seven days prior to and lasted until the eve of clinical suspicion, 2) the infectious period began seven days prior to the day of clinical suspicion and lasted up to seven days after stamping out, 3) the conditional probability of infection was fully dependent on the contacts indicated in We used Mathematica 8  to perform the maximisation procedure. The 95% Confidence Intervals (CI) for the maximum likelihood estimates were computed using the likelihood ratio test. We quantified the contribution of the different contacts to the epidemic in terms of the number of new infections that they may have caused. This was obtained by multiplying their estimated per-contact probability with their frequency.As an introduction can only occur on one day, we can only allow for the uncertainty about when this day was by giving weights to each of the possible introduction days with these weights adding up to one. For the base model, we used a uniform distribution to obtain We performed a sensitivity analysis to ascertain the effect, on the probability estimates, of the possible uncertainty in defining the distribution underlying the actual day of virus introduction over the assumed period. We performed this analysis by re-running the calculation with different distributions underlying the estimation of the weighting factor We hypothesized that, during the epidemic, the tracing process may have been more rigorous on case farms compared to the non-case farms. We explored the effect of this possibility by considering a scenario where an under-representation of the contacts to the escaping farms – for example due to a more lax attitude of the tracing teams when on non-case farms – could have occurred. We estimated the maximum effect that this would have on the estimated probabilities in the following 3 steps: 1) if we let In order to validate the estimated per-contact probabilities, we used the genetic data obtained by sequencing the majority of the samples collected for outbreak farms during the epidemic With the contact inclusion criteria described under Data section, we extracted traced contact pairs, i.e. farm pairs  in which at least one contact originating from a then deemed infectious farm A to a hitherto susceptible (but ultimately infected) farm B, and occurring within the exposure period of farm B, was traced. We then used the genetic information generated from the majority of the samples taken from the affected farms during the epidemic as reported by Bataille et al. We considered four different (sets of) criteria for determining whether a contact farm pair  represents a genetic match. These (sets of) criteria differ in the level of genetic overlap required between the sequences from farm A and farm B to qualify as a genetic match. The most liberal criterion we considered was that all mutations in the virus of farm A compared to farm 1  were also found on farm B, i.e. when going from A to B no mutations are lost. This criterion is necessary because it is highly unlikely for the virus to lose mutations (i.e. undergo backward mutation) between source and receiving farms. In the other three, in addition to having no lost mutations, we permitted only a specific number/range of additional mutations: allowing no additional mutations at all, allowing ≤3 and, With our selection criteria applied to the first dataset i.e., the data from the epidemiological investigation by the RVV, we were able to extract at least one traced exposure for 36 (i.e. 15%) ultimately infected farms and the number increased to 44 (i.e. 18%) upon including the crisis organisation contacts. With the complete dataset , 227 (i.e. 94%) ultimately infected farms had been exposed. Thus with all the available and modelled data, all but 14 infected farms had either a traced exposure or it was in the neighbourhood of an infectious farm (unknown contacts).In Apart from the unknown and crisis organisation contacts, feed deliveries had the lowest per-contact probability of virus transmission of 0.0414 and potentially caused 2.63% of the new case farms while the egg transports had the highest per-contact probability of 0.308 and may have potentially caused 2.04% of the new case farms. The probability of virus transmission per crisis organisation contact was estimated to be 0.0011 and these visits may have caused 0.13% of the new case farms. The majority (92.54%) of the new cases were caused by the unknown contacts within the distance bands of 1–3 km and 3–10 km.dw assigned by the unimodal distribution to the days on which these contacts occurred. Three of the five contacts to ultimately infected farms occurred seven days prior to the day of clinical suspicion while the remaining two occurred four days and one day prior to the day of clinical suspicion.Analysing the sensitivity of the estimated probabilities to the assumed distribution underlying the actual day of virus introduction over the 7-day period, the outcomes from using the two alternative distributions  were compared with those of the default distribution . The estimates were very similar for most of the exposure types. The only differences found, but these were small, were in the per-contact probabilities for the crisis organisation contacts for both alternative distributions and the other-professional contacts for only the unimodal distribution see . For botWith respect to the effect of the potential difference in tracing efforts on case and non-case farms – hence a possibility of under-representation of the contacts to non-case farms, we found that, with the worst tracing efforts, the contacts to case farms would be twice as likely to be traced as those to non-case farms. This implies that, at worst, the estimated probabilities could be double their ‘unbiased’ counterparts.There were 56 traced contact pairs in which virus transmission may have occurred i.e. contacts from an infected farm to a newly infected farm. From the genetic data of the same outbreak In The mechanisms of HPAI virus spread between farms are poorly understood; it has been hypothesized that the indirect between-farm contacts play a role The estimated conditional probabilities of virus transmission are presented in Generally, most exposure-types  made a substantial contribution to virus transmission during the epidemic. We note that the estimated per-contact probability of virus transmission by the crisis organisation contacts is 0.0011 and may have caused 0.13% of the infections. We note that when ignoring all other exposure types, i.e. considering the crisis organisation contacts alone in a separate analysis, we estimated a probability of 0.0327 per contact corresponding to 3.92% of the infections. This probability estimate is in agreement with the estimated maximum probability of virus transmission by a ‘control-person’ per visit of 0.037 reported by te Beest et al. We hypothesize that the lower probability of infection per crisis organisation contact compared to that of the other-professional contacts which are almost of the same nature indicates that the epidemic control teams have better biosecurity than other visitors. The lower per-contact probability of infection per feed delivery compared to egg transport may be due to the difference in degree of contact and the re-use of egg trays. Unlike egg pick-up where the eggs have to be picked from the egg room, feed delivery may not involve accessing storage rooms or poultry houses. In most cases, the feed truck’s delivery tube is directly connected to the feed storage from the outside thereby reducing the risk of farm contamination.In the sensitivity analysis, we find that the majority of the estimates are robust to the assumed distribution of the most likely day (among the seven days) of virus introduction. For the few sensitive (but less contributing) contact types, we concentrate on the results obtained using the uniform distribution as this assumes the least prior knowledge on the actual moment of disease introduction on the farm. Regarding the effect of a possible difference in tracing efforts on case and non-case farms, we have argued that an under-representation of the contacts to non-case farms may have at most doubled our probability estimates i.e., compared to the ‘ideal’ situation where the tracing efforts are the same for the case and non-case farms.The pairwise comparison of the genetic information of the contact pairs  and S2 sStegeman and co-workers With our contact inclusion criteria, 44 infected farms have at least one traced exposure i.e., excluding the ‘unknown’ contacts. The outbreaks that could not be linked to any known potentially infectious contact may not only be attributed to the inability to trace all targeted contacts. Rather, they may serve as a hint about the presence of other (un-targeted and hence untraced or even untraceable) mechanisms. This highlights the need to better understand the possible mechanisms of untraced transmission.It is important to realize that the probabilities estimated are conditional on the contact originating from an infectious farm and do not represent the actual risk of HPAI virus transmission by these contacts during the epidemic. We also emphasize that care should be taken when interpreting the per-contact probability estimate for the rendering contacts due to the possible correlation between this contact-type and the increased mortality which could have occurred during the silent spread period of the virus on the farm i.e., the virus could have already been circulating undetected on the receiving farms. Nevertheless, the probability estimates together with the risk-based ranking for the different contacts obtained in this study can help design better control strategies against HPAI virus transmission between-farms by these contacts.All in all, after estimating the per-contact probability of virus transmission for the different contacts, we conclude that all the identified contacts made a substantial contribution to the risk of virus transmission between farms. Therefore, any measures to reduce on their frequency and to improve biosecurity during all these contacts are potentially worthwhile. The fact that the ‘unknown’ contacts contributed the most (causing 93.24% of the infections among themselves) emphasizes the need for a better understanding of the mechanisms underlying virus transmission.The findings of this study contribute to the greatly desired understanding of the mechanisms of indirect transmission of HPAI virus between farms. Our results suggest that, apart from the unknown contacts, egg delivery contacts are interesting targets for improvements in biosecurity due to their high per-contact probability (31%) in infecting the receiving farms. They further suggest that the biosecurity applied to the crisis organisation contacts seems to be adequate at least for preventing the persons themselves from becoming important fomites between registered visits. Overall, these findings provide a scientific basis to conduct further studies, epidemiological or otherwise, to evaluate the impact of improved biosecurity and minimized contact-frequency in controlling the between-farm spread of HPAI virus during epidemics. The knowledge gained in this study can further be supplemented by research aimed at disentangling the ambiguous category of ‘unknown’ contacts defined in this study.Table S1Summary of genetic differences between isolates from the source and receiving farms for the traced contact pairs.(DOC)Click here for additional data file.Table S2The observed number of genetically matching pairs , within 28 pairs of outbreak farms linked by traced contacts, for different criteria of defining a genetic match.(DOC)Click here for additional data file.Text S1Further details on the validation against genetic data.(DOC)Click here for additional data file.


Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments. A multitude of diagnostic medical imaging modalities are used to probe the human body. Interpretation of the resulting images requires sophisticated image processing methods that enhance visual interpretation, and image analysis methods that provide automated or semi-automated tissue detection, measurement and characterisation. In general, multiple transformations will be needed in order to extract the data of interest from an image, and a hierarchy in the processing steps will be evident, e.g. enhancement will precede restoration, which will precede analysis, feature extraction and classification. Often these are performed sequentially, but more sophisticated tasks will require feedback of parameters back to preceding steps so that the processing includes a number of iterative loops.Several ongoing areas of research have been selected to highlight novel developments in analysis and display, in the hope that the methodologies may be transferred to other applications.Mammography is the single most important technique in the investigation of breast cancer, the most common malignancy in women. It can detect disease at an early stage when therapy or surgery is most effective. However, the interpretation of screening mammograms is a repetitive task involving subtle signs, and suffers from a high rate of false negatives 10%–30% ), and fa0% , and ductal carcinoma in situ (DCIS). Once the tumour extends beyond the lining of the ducts it is termed invasive, and can metastasise to other sites in the body. Radiographic indications fall mainly into two categories, microcalcifications and lesions (masses) (in situ carcinomas (i.e. those within the milk ducts); they are typically in the order of several hundred microns or smaller in diameter, and tend to occur in clusters. Most lesions are ill-defined in shape, often with tissue strands or spiculations radiating out from them, and similar in radio-opacity to the surrounding normal tissue  . Microcal tissue . The imaEuclidian distance map, mapping distances to the skin line in a smoothed version of the mammogram [CAD performance and reliability depends on a number of factors including the optimisation of lesion segmentation, feature selection, reference database size, computational efficiency, and the relationship between the clinical relevance and the visual similarity of the CAD results. Segmentation of the breast region serves to limit the search area for lesions and microcalcifications. It is also useful to adjust the grey values of the image to compensate for varying tissue thickness; one way to do this is to add grey values according to the ammogram . Noise iammogram  combinesTo improve the accuracy and reliability of mass region segmentation, a large number of computing algorithms have been proposed, developed and tested, including multi-layer topographic region growth algorithms , 8, 9, aFeatures which are useful for characterising lesions include their degree of spiculation, shape and texture . Spiculaetc.) of individual calcifications, and the spatial distribution and heterogeneity of individual calcifications within a cluster. They can be enhanced by thresholding the image, and morphologically opening it using a structuring element to eliminate very small objects while preserving the size and shape of the calcifications [Microcalcifications can be described by the morphology  even at the cost of some acceptable number of false positives .Computer assisted diagnosis (CAD) systems do not have to be perfect since they are used with a radiologist and not alone. Since the cost of a missed cancer is much greater than the misclassification of benign findings, they should be developed to reduce false negatives  of the scanner . HoweverLacunarity measures the distribution of gap sizes in data: the greater the heterogeneity the greater the lacunarity. An efficient algorithm for estimating lacunarity analyses deviations from translational invariance of an image’s brightness distribution using gliding-box sampling . Lacunarr) and var(r) are the mean and variance of the pixel values, respectively, for a neighbourhood size r. An average lacunarity value can be calculated across the scale range of the bone image to indicate the average gap (marrow) size and its degree of heterogeneity.where mean from CT scans of sixteen patients whose bone strength had been previously assessed . The datLinear discriminant analysis showed that the patients could be correctly classified into four classes , and the tortuosity of retinal blood vessels can be an early indicator of systemic diseases.M). Another metric is based on the root-mean-square curvature of a unit speed curve, obtained by an approximating polynomial spline fitting to the mid-line data points [K). Although these metrics are not strictly dimensionless, they are often referred to the length of the vessel in pixels and their units omitted: they can then be treated as numbers which rank the tortuosity of vessels obtained under the same imaging conditions.Vessel tortuosity does not have a formal clinical definition but a tortuosity metric should be additive and invariant to affine transformations of a vessel  –54 if ita points , 56. TheThese analyses are construed directly in three dimensions (3-D) and their clinical validity has been established  using clThese tortuosity metrics are able to distinguish between normal vessels and some retinal pathologies in retinal fundus images , 59, witScoliosis is a complicated condition characterised by a lateral curvature of the spine and accompanied by rotation of the vertebrae on its axis . DespiteDue to the errors associated with manual Cobb angle measurement from plain radiographs, a number of authors have developed computer-assisted techniques using digitised radiographs –69. SeveM and K) can be used to characterise the curvature of the spine in patients with idiopathic scoliosis by iteratively fitting piece-wise polynomial splines to the geometric centres of the vertebrae as seen in 2-D A-P radiographs [The tortuosity metrics  is a progressive debilitating disease that results from degradation of the cartilage matrix that provides a low friction surface covering the ends of bones in joints . DegradeThe initial stages of OA involve changes in water and proteoglycan content and in the orientation of the collagen fibre bundles in the surface of the cartilage . RecentlDiffusion MRI, using a pair of de-phasing and re-phasing gradient pulses with a spin echo MRI sequence , 75, chaThe principal eigenvector  can be represented by a “quiver” plot, where each quiver represents the projection of the principal diffusion eigenvector on to the image plane . The autAlternately the orientation of the principal eigenvector  can be mapped using a colour scale , as can Experiments aimed at better understanding the mechanisms involved in cartilage degradation will continue. Early detection of these changes, when they may still be reversible, is key to the development of new approaches to treatment.


One of the major breakthroughs in neuroscience is the emerging understanding of how signals from the external environment are extracted and represented in the primary sensory cortices of the mammalian brain. The operational principles of the rest of the cortex, however, have essentially remained in the dark. The discovery of grid cells, and their functional organization, opens the door to some of the first insights into the workings of the association cortices, at a stage of neural processing where firing properties are shaped not primarily by the nature of incoming sensory signals but rather by internal self-organizing principles. Grid cells are place-modulated neurons whose firing locations define a periodic triangular array overlaid on the entire space available to a moving animal. The unclouded firing pattern of these cells is rare within the association cortices. In this paper, we shall review recent advances in our understanding of the mechanisms of grid-cell formation which suggest that the pattern originates by competitive network interactions, and we shall relate these ideas to new insights regarding the organization of grid cells into functionally segregated modules. The urid axes . Grid cerid axes ,5.Grid cells can be distinguished from hippocampal place cells, which also fire at specific locations ,6. PlaceThe aim of this paper is to review advances in our understanding of the neural network operations underlying the grid pattern. In the first part, we shall demonstrate how converging evidence points to a mechanism for grid patterns where grids emerge through competitive inhibitory interactions between grid cells with similar scale properties. In the second part, we shall review recent work suggesting that the MEC consists of multiple grid networks or maps operating on a partly independent basis. We shall argue that independently operating grid modules may form the basis for high-capacity memory downstream in the hippocampus.2.A number of models have been proposed to explain the formation of grid patterns in MEC cells. In nearly all of these models, periodic firing locations are determined by path integration of velocity-modulated input signals, with other sensory inputs contributing only to setting the initial phase and orientation of the grid ,3. HowevIn one class of models, referred to as the oscillatory interference model, the spatially periodic firing of a grid cell emerges within the cell as an interference pattern between a relatively constant theta oscillation and other internal or external velocity-controlled theta oscillators –20. The Oscillatory interference-based models have successfully explained some of the temporal firing properties of grid cells, such as theta phase precession –23, but In addition, when it comes to experimental testing of whether the ingredients of the model exist, studies have failed to verify two of its key assumptions. One of these is that grid cells require theta oscillations. Grid cells have now been demonstrated in Egyptian fruit bats, which lack the persistent theta oscillations characteristic of hippocampal and parahippocampal regions in rodents . In batsAn alternative class of models suggests that grid patterns are formed in local circuits with attractor properties. Common to this class of models is the suggestion that hexagonally patterned activity emerges as a consequence of interactions between recurrently connected neurons, and that neural activity moves across the connection matrix in accordance with the animal's movements through the environment –30. An iet al. [a,b). On a sheet of neurons arranged according to spatial phase, activity emerged at maximally spaced positions, i.e. at the vertices of a hexagonal grid. The activity could then be translated by the same mechanism as in the previous attractor models. These findings are in agreement with independent results from another study [In the earliest attractor models for grid cells, periodic firing fields were proposed to result from Mexican-hat connectivity, i.e. strong excitatory connections between cells with similar grid phases and progressively weaker connections between cells with increasingly different phases ,29. A suet al.  demonstrer study , which aet al. [a = 0 in their eqn 3) and the negative Gaussian has steep flanks. In any case, the hexagonal pattern emerges as an optimally packed arrangement of inhibition circles surrounding activity bumps. Although no fundamental differences exist between the two models in terms of the mechanism that they employ for the generation of grid cells, the attractive feature of the connectivity used by Couey et al. is that it would be easier to generate such connectivity developmentally. Taken together, these sets of studies suggest that connections do not have to be Mexican-hat type and that all-or-none inhibitory connectivity is sufficient for grid formation.The fact that a purely inhibitory network can generate grid cells has already been noted by Burak & Fiete ; howeveret al.  could bec,d). In the absence of intrinsic inhibitory influences, the former grid cells settled in a state where they responded primarily to external directional inputs. The excitatory drive may also come from other sources such as the medial septum, whose activity is necessary for maintenance of grid activity in the MEC [A potential problem is that a purely inhibitory network depends on tonic external excitatory drive for keeping cells sufficiently near firing threshold to fire. Such excitatory input can come from a variety of sources, one of which could be the hippocampus, which, via the deep MEC layers, has strong projections back to the superficial layers of the MEC ,36. Recefigure 1 the MEC ,38. A si the MEC ,29.It is worth noting that if the periodic firing of the grid cells gets disrupted by blockade of external excitatory drive, it may still be possible, under certain conditions, to observe short periods of grid-like activity on the neuronal sheet. In particular, grid patterns should be detectable when the animal moves in a constant direction. For cells whose preferred direction is along the animal's movement, the head-directional input may then momentarily compensate for the missing external drive. Because the activated population changes as the animal changes direction, a grid-like firing pattern would not be apparent over time (supplementary movie in ). HoweveThe main problem with the attractor framework in our view is that of the sensitivity to noise in recurrent connectivity. Static noise in the recurrent connectivity is known to result in unwanted drift in the activity of continuous attractors . Such drThe progress recently made in understanding the circuitry of the grid cell network has paved the way for answering an important question that so far has been largely neglected, which is how grid cells are formed during maturation of the nervous system. Since nearby grid cells do not seem to have nearby phases, a connectivity formed topographically during development is not likely to provide the right answer. At the moment, the only explicit attempt to offer a different mechanism for the development of grid cells is the work by Kropff & Treves , where t3.Early studies of grid cells suggested that the grid network operates as a single unified network in which cells responded coherently to changes in the environment ,9. The eUntil recently, low numbers of simultaneously recorded grid cells have prevented a clear answer to the question of whether the grid-cell system operates as one integrated network or as a collection of discretized modules. We have recently been able to increase the number of grid cells recorded in a single animal by an order of magnitude, from around 10–20 to almost 200 . This alHow many grid modules are there? So far, large numbers of grid cells have only been recorded from the dorsomedial one-third of the MEC. Yet, by extrapolation, the data suggest that the total number of grid modules is small, probably less than 10. The existing data generally clustered into three to four values with large gaps between each step. In one animal, a fifth module was probably present but the firing fields of these cells were too large to determine conclusively whether they were part of a grid pattern. Modules with small values for grid spacing predominated at the dorsal-most recording positions. Then as the recording electrodes were moved in the ventral direction, successively larger grids were recruited. No gradients were observed in the mediolateral direction, suggesting that the modules are organized as horizontal bands parallel to the dorsal border of the MEC. There was considerable anatomical overlap between the clusters, particularly at more ventral locations, suggesting that grid modules are entangled, quite unlike the graded topography of many maps for continuous functions in the primary sensory cortices ,48.The scale relationship of successive grid modules shows some interesting features that may be relevant to how effectively the spatial environment is represented. On average, grid spacing increased from one module to the next by a factor of 1.42, or the square root of 2. The geometric progression defined by this scale relationship enables the inner ring of the grid hexagon to exactly double from one module to the next. Interestingly, theoretical analysis suggests that in order to estimate the position from the response of grid cells with high resolution, such a modular organization with geometric scaling of grid spacing is required .How does grid modularity come about? In the attractor networks, there are two factors that control field size and grid spacing. The first one is the strength of the velocity-dependent head-directional signal: increasing this signal results in faster translation of the grid pattern on the sheet as the animal moves and thus makes the spacing between single cell grid fields smaller. If this is how grid scales become different across modules, the MEC may have multiple path-integrating attractor networks with similar recurrent connectivity patterns but each operating with a different amplification of the incoming speed signal. Another way to change grid spacing and field size is to alter the strength or the extent of recurrent connections. As shown in The existing data suggest that grid modules can operate independently. Changes in grid scale coincide not only with changes in grid orientation but also with differences in elliptic distortions of the grid, as well as variations in theta-frequency modulation of the grid cells . MoreoveThe organization of grid cells into independent modules may have consequences not only for representation of the animal's current location in the MEC but also for the network's ability to store memory based on grid maps. Hippocampal place cells are likely to receive convergent input from multiple grid modules. If these converging modules respond independently during transitions to a different environment, or when an individual environment is sufficiently altered, the altered co-activity of entorhinal grid cells will likely activate a new subset of place cells at each location in the new environment ,46,50. EKavli Foundation, a Centre of Excellence grant from the Research Council of Norway, an Advanced Investigator Grant to E.I.M. from the European Research Council , and a grant from the European Commission's 7th Framework 2011 ICT Programme for Future Emerging Technologies .The work was supported by the