The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters. Electrophysiological recordings are widely used to evaluate how nervous systems process information. Whereas up to about two decades ago rather small data sets were acquired which were easy to analyse manually, a rapid development of data acquisition and storage techniques now allow to accumulate huge datasets within a relatively short time and their analysis is often highly automated. This trend tends to further accelerate with the introduction of automated electrophysiology in ion channel discovery A secondary problem with manual data inspection is the sheer effort that is needed to classify large data sets. With “easy” experimental protocols, a strategy to keep only rapidly recognizable “good” recordings can be used, but with complex experimental protocols it is often time consuming to judge each recording trace “by eye” and errors in the judgement can lead either to a loss of recordings , errors in results  or a waste of time .Many aspects of data analysis have undergone a process of automation starting from filters However, the final judgement whether to include a recording into the analysis or reject it as too low in quality or artefactual is still reserved to the human researcher. Here, we begin to challenge this established practice.To facilitate the choice of electrophysiological recording traces for further analysis, and remove subjectivity from this process, we propose an automated evaluation process based on machine learning algorithms using examples of intracellular recordings from central olfactory neurons in the insect brain.In machine learning An important ingredient for successful application of machine learning methods is feature selection Spodoptera littoralis and Agrotis ipsilon. Data set 2 was acquired by another author (“expert 2” in what follows) and contained similar recordings from A. ipsilon. Data set 1 consists of 183 recordings and data set 2 of 549 recordings.We used two data sets within the numerical analysis in this work. Data set 1 was acquired by one of the authors (“expert 1” in what follows) and combines recordings from central olfactory neurons in the antennal lobe of the noctuid moths All recording traces were obtained with attempted intracellular recordings with sharp glass electrodes of central olfactory neurons within the antennal lobe of the two moth species. Each recording trace was approximately 5 s in duration. A species-specific sex pheromone stimulus (varying doses for different traces) was applied 1.5 s after the onset of the recording for 0.5 s.For the purpose of this work on automatic data quality assessment, the ground truth for the data quality of the used recordings was established by visual inspection by two experts. They classified the recordings in 3 categories: “bad”, “intermediate” and “good” as defined in the introduction. Expert 1′s classification resulted in 29 bad, 54 intermediate and 100 good recordings for data set 1 and 90 bad, 130 intermediate and 329 good recordings for data set 2. Expert 2′s classification resulted in 20 bad, 39 intermediate, and 124 good recordings in data set 1 and 178 bad, 75 intermediate, and 296 good recordings in data set 2. Exemplary plots of the data are provided in In order to enable a machine classifier to make decisions about the quality of recordings it needs access to the relevant properties of the data. We defined 16 such properties that we call features.V(t) was multiplied by the gain factor to achieve a common scale for all recordings (e.g. mV). For the purpose of spike detection, V(t) was then filtered with a moving average of window size 3 ms. Candidate spike events were detected based on two threshold criteria on the derivative of the filtered membrane potential. Detecting spikes based on the derivative will automatically remove any occurring plateau potentials and possible recording artefacts due to current injections. In order to qualify as a candidate spike event, 3 consecutive derivatives need to be above the upward threshold θup and within tspike,max = 3 ms, 3 consecutive derivatives need to be below the downward threshold θdown. These conditions test for the sharp rise and fall of the membrane potential around a spike and are independent of the overall amplitude and baseline values. The upward and downward derivative thresholds θup and θdown were chosen as three times the 80th percentile and 3 times the 20th percentile of all observed values of the derivative, respectively. Deriving the threshold values from percentiles of observed values of the derivative helps us to take into account if a sufficient number of spikes are less steep due to current injections or other prolonged excitation. Our manual controls of spike detection showed that this strategy is vey reliable in finding all candidate spike events.The data are first pre-processed with filters and a rule-based spike detection algorithm. If recorded with different gain factors during data acquisition, the recorded membrane potential V(t)  in the intervals  and   there are points within 3 ms before and after the spike time where the membrane potential is lower than the half height of the spike, and (ii) the spike height has more than half the 95V(t) signal. They are summarized in The 16 features of a recording that we consider in the following are based on the detected spike events as well as on general properties of the full bandwidth th to the 95th percentile of observed values (i.e. excluding extreme outliers if there are any). Statistically significant differences between distributions were determined by Kolmogorov-Smirnov tests with Bonferroni correction for multiple pairwise tests at 5% and 1% significance levels.The statistical distributions of feature values are shown as histograms (20 bins) in a range that includes the 5Crossvalidation is used to assess the success of a classification method when no separate test set is available. The data set of interest is split repeatedly into a training and testing portion and the performance of the classification algorithm is assessed on these different splits. We use 10-fold crossvalidation, in which the data is split into a 90% training set and 10% left out samples for testing. The split is chosen randomly, but such that after 10 repeats, all samples have been left out once. If not stated otherwise we repeat the full 10-fold crossvalidation 50 times with independent random splits.4 steps. We checked with unlimited learning iterations (with a ceiling of 106 iterations in two rare cases where otherwise apparently infinite iterations occurred) and observed no discernible differences in the results.As classifiers we used linear support vector machines (SVMs) For the cost parameter of the linear SVM we used C = 512. Repeated runs with C = 8, 32, and 128 gave similar results.16 – 1 potential choices for the group of used features. We call a particular choice of features, e.g. features  a feature set and the number of employed features, 4 in this example, the size of the feature set. Most results will be reported separately for feature sets of different sizes.The wrapper approach to feature selection is a brute force method in which all possible choices of features are tested with respect to the performance of classification in crossvalidation based on each feature choice. Here, there are 16 possible features allowing 2The wrapper features selection was executed on the in-house computer cluster of the University of Sussex in separate processes for each feature set size. Computation times varied from 20 s for feature sets of size 16 to about 3.5 days for feature sets of size 7–9.n−1 recordings is used for a full wrapper feature selection with crossvalidation. This involves choosing feature sets and evaluating them in crossvalidation, i.e. leaving out another recording, training a classifier on the resulting training set with n−2 recordings and testing it on the left out recording. For full crossvalidation, this procedure is repeated until all n−1 recordings have been left out. We then use the top10 best feature sets (see below for a definition of the top10 group) and train corresponding classifiers on the “full” training set of n−1 recordings. The resulting classifiers are then finally used for predicting the class of the originally left out recording. This procedure is repeated until all recordings have been left out once in stage one.The normal wrapper approach of feature selection with crossvalidation is prone to the following over-fitting effect: we typically test all possible feature sets in crossvalidation and then report the best observed performance and identify the feature set that obtained this performance. If this best-performing feature set is interpreted as the optimal feature choice for the problem at hand one is exposed to the selection bias of potentially identifying a feature set where the crossvalidation procedure (which contains a random element) worked particularly well by chance. This bias is particularly strong when a large number of feature sets with very similar quality are compared. To avoid this bias and, without using truly novel test data, get a realistic estimate of how well one would do when using a wrapper method for feature selection, we devised a two-stage-leave-one-out crossvalidation procedure. In the first stage, one recording is left out and the remaining training set of We calculated the values of all 16 features on the two data sets and plotted the distribution of observed values separately for each group of bad (blue), intermediate (green) and good (red) recordings, as judged by expert 1 , 3. The The plots reveal noticeable differences between distributions of feature values for good recordings and intermediate or bad recordings, e.g. for the mean spike height or the CV of the spike height. To test this observation formally, we performed pairwise Kolmogorov-Smirnov tests with a Bonferroni correction for multiple statistical tests on a 5% significance level . The feature set with the best prediction performance in crossvalidation is chosen for the final classifier, which is then tested on a separate test set.16–1 = 65535 possible selections. As a classifier we used a linear support vector machine (SVM). We performed wrapper feature selection on all possible subsets of the 16 defined features, a total of 2For data set 1, the set of features that was performing best consisted of 6 features: , and for data set 2 of the 8 features . We note that the spike height (feature 1) and the CV of the spike width (feature 4), as well as, the maximum spike slope (feature 13) are common to both feature sets. Comparing to the statistics shown in −4 or less). For data set 2, however, we do not see such a significant effect, even though the performance of the feature sets containing all three features visually appears higher for most feature set sizes (data not shown). Finally, on their own, the three features lead to a performance of 68.1% (3 classes), 87.1% (2 classes) in data set 1 and 76.7% (3 classes), 78.3% (2 classes) in data set 2.To further test the idea that features 1, 4 and 13 are particularly useful, we inspected the performance of all feature sets that contain all three of these and compared them to the performance of feature sets that do not contain them all. We observe that for data set 1 and feature sets with more than 4 and less than 14 elements, the average performance of the sets containing the three features is significantly higher than of the sets not containing all of them . However, the distributions do have in common that for small feature sets of 2–4 features, there are no clear preferences but many combinations of features seem to work similarly well. This appears to indicate that several of the defined features are informative for the quality of the recording and there is no one golden rule deriving from only one or two central features. For larger feature sets of 5 to 8 features we notice that features  seem to  always be used but with different combinations of other features, indicating that these features seem to be the most salient for the task.Overall the wide spread of features used indicates, however, that there are less dominant features in this application of machine learning than in other domains In a practical application one would choose features and train a classifier on a reference data set to then automatically recognise the recording quality in future recordings. To investigate the performance of our machine learning system with wrapper feature selection in this situation we choose the best-performing feature sets in crossvalidation on data set 1, train a classifier for the best and the top10 feature choices on data set 1 and classify all recordings in data set 2 using the resulting classifiers.The reduced problem of only 2 classes of recording quality (with intermediate and bad pooled together) shows a somewhat different picture . Here, sn−1” recordings. We then choose features and train a classifier on the n−1 recordings to eventually predict the quality of the originally left out recording (see Methods for more details). We observe that the performance of voting classifiers based on ‘top10’ feature groups is competitive  is performed on the remaining “petitive , this supetitive , a similFrom this numerical experiment with twofold leave-one-out crossvalidation we conclude that in the three class problem, we can reasonably expect a 73% performance of fully correct predictions when remaining within the recordings of a single expert.The observed performances imply that misclassification occurs in a few cases. It is interesting to ask whether these cases are due to the classifiers being unreliable, i.e. misclassifying a given recording occasionally but getting it right as well, or whether the failures are due to particular recordings Click here for additional data file.Toolbox S1Matlab toolbox and example original data files of data used in this study. The installation and use of the Matlab tools is explained in the included README file.(ZIP)Click here for additional data file.


We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts  with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%. Combined with a body landmark detection algorithm Template-based segmentation algorithms are suitable for medical image processing, because a patient's data – mostly in the DICOM  s and a sink t to allow the computation of a s-t cut  and R is the number of rays sent out to the contour of an object template r = ), where V is a neighbor of V – in other words V and V belong to two adjacent rays To set up the graph, points are sampled along rays that are sent through the contour of a square template. The sampled points are the nodes nz ensure that all nodes below the contour in the graph are included to form a closed set . The arcs Ar between the nodes of different rays constrain the set of possible segmentations and enforce smoothness via the parameter Δr. The arcs for different delta values are presented in r = 0 (left), Δr = 1 (middle) and Δr = 2 (right). The larger this parameter Δr is, the larger is the number of possible segmentations. In r = 1 is presented. For the graphs on the left side and the middle the costs for a cut (2•∞) are the same. However, for a cut like shown on the right side of The arcs between two nodes along a ray Ar that controls the stiffness of the resulting contour. A delta value of zero ensures that the segmentation result has exactly the form of the predefined template (square) – and the position of the template depends on the best fit to the image's texture. The weights w for every arc between vT of size d centered around the user-defined seed point :After graph construction, the minimal cost closed set on the graph is computed via a polynomial time s-t cut z along the single rays, D: the r-arcs Ar between rays with a delta value of Δr = 0. E: same as D only with a delta value of Δr = 1.The principle of the graph construction for a square is shown in s and the sink t. These arcs depend on the gray values of the nodes they connect – or rather they depend on the gray value difference to an adjacent node. To integrate the user-defined template into the construction of the graph, we need the coordinates in 2D describing the object that we want to segment  and the delta value (inter column arcs) used for the graph, and there are arcs that connect the nodes to the source The interception point of one ray with the object provides the distance between the nodes for this ray, because all rays have the same number of nodes from the center of gravity point to the intersection with the contour. For intersections that are located closer to the center of gravity point we get smaller distances, and for intersections that are located farer away from the center of gravity point we get larger distances. Calculating the intersection of a ray with a 2D object is straightforward, since it is simply a line-line intersection. One line is the actual ray and the other line is one straight line between two points of the predefined template.http://www.mevislab.de, accessed: 2012 Jan 2) has been used; the algorithm has been implemented in C++ as an additional MeVisLab-module. Although the foci of the prototyping platform MeVisLab are medical applications, it is possible to process images from other fields. Even when the graph was set up with a few hundred rays and hundreds of nodes where sampled along each ray, the overall segmentation  for our implementation took only a few seconds on an Intel Core i5-750 CPU, 4×2.66 GHz, 8 GB RAM, Windows XP Professional x64 Version, Version 2003, Service Pack 2.To implement the presented segmentation algorithm, the MeVisLab-Platform  with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient of 90.97±2.2% . The Dic3) of voxels inside the binary mask, by means of counting the number of voxels, then multiplying with the voxel size. 3), number of voxels and Dice Similarity Coefficient for nine vertebrae areas. In r = 4).The maximal length of the rays that have been sent out from the user-defined see point has been 35 mm. Furthermore we used a [0.25 0.5 0.25] kernel (one iteration) to smooth the resulting nodes that have been calculated  and corners of a square template (magenta)B: intersection points where the send out rays cut the square template (green)C and D: sampled nodes for the graph (blue)E: segmentation results (red)r = 2.A 3D visualization of a MRI spine dataset with a graph that has been used to segment one vertebra is displayed in r = 4) for all segmentations have been used for both datasets.The segmentation results for several vertebrae of patients are shown in r  affect the segmentation performance. Image A in r = 0,…,Δr = 6 whereby the number of rays (30) and the number of nodes sampled per ray (40) have not been changed. In image B the delta value is zero (Δr = 0) and therefore the resulting contour is a square, because the cut has to be on the same node level. The position of the square depends only on the gray values and edges of the image. The delta value in image C was set to one (Δr = 1) and therefore the cut has more options and must not be on the same node level. As you can see in image C the resulting contour (red) already fits to the lower and upper border of the vertebra. However, the delta value is still too small – and therefore the possible resulting contours are to stiff – to segment the whole vertebra (see the left and right border of the vertebra). With a delta value of two (Δr = 2) used to get the segmentation result in image D, the flexibility is high enough to segment also the left and right border of the vertebra. For the next three images E, F and G the delta values have even been increased: Δr = 3 (E), Δr = 4 (F) and Δr = 5 (G). These higher delta values enables the cut to return a more “detailed” contour like the bulge in the upper left corner of image G. But higher delta values also increase the risk for an over-segmentation. That happened for a delta value of six (Δr = 6) in the last image H, where the upper border of the segmentation result already returns the lower border of an adjacent vertebra.N-D image segmentation method called GrowCut from Vezhnevets and Konouchine GrowCut for spine segmentation. For testing GrowCut with our datasets we used an implementation that is freely available as an module for the medical platform 3DSlicer3DSlicer – or Slicer – is a free, open source software package for visualization and image analysis primarily used in the medical domain and has been developed by the Surgical Planning Laboratory (SPL) of the Brigham and Women's Hospital in Boston. To use GrowCut for vertebra segmentation the user has to label a part of the vertebra and a part of the background with a simple brush tool.As stated in the background paragraph, there have been published several methods – like deformable models and statistic approaches – for vertebra segmentation in the literature. All papers present detailed segmentation results and in almost all cases the computational time for their algorithms is also provided, which seem both – segmentation and time – to be similar to our results. Therefore, we decided to compare and discuss our approach with an interactive multi-label GrowCut approach. The images on the left side  show examples for a manual initialization of the algorithm with the vertebrae in green and the background in yellow. The images on the right side  present the corresponding segmentation results in green. As you can see in GrowCut algorithm can provide very precise results for a careful initialization. However, for a rougher initialization it can provide not satisfactory results as you can see in Dice Similarity Coefficient for the GrowCut, because the segmentation results depend on the user initialization. But we can already tell that for someone who knows the algorithm and knows how to deal with the initialization, the DSC will be around ninety percent compared with a pure manual segmentation. A big advantage of the GrowCut – at least for the implementation we tested – is that a user doesn't have to define any parameters. In contrast, our approach has parameters which you have to deal with, but for someone who is used to the algorithm that can be handled. A disadvantage for the GrowCut is the time consuming and precise initialization you sometimes need to archive good results. In contrast, our approach only needs one centered seed point inside the vertebra.In this contribution, we have presented a template-based segmentation scheme for 2D objects. To the best of our knowledge, this is the first approach where the nodes of a graph-based algorithm have been arranged according to a predefined square template in a non-uniform and a non-equidistant manner on an image. Using this new type of segmentation algorithm, it is even possible to reconstruct missing corners in an object. In addition, the scaling of an object is irrelevant for the presented method. Experimental results for several 2D images based on Magnetic Resonance Imaging datasets consisting of vertebrae have indicated that the proposed algorithm requires very less computing time and gives already reasonable results even for a very simple cost function.There are several areas of future work. For example, the cost function for the weights can be improved. Another possibility is to increase the sampling rate for the nodes near an object's border, because with an equidistant sampling rate , there are more nodes near the user-defined seed point and less nodes going farther out. The user-defined seed point position that is located inside the object is also an issue that has to be analyzed in the future, e.g. for some images the seed point has to be chosen very carefully. In general, the presented approach provides better results if the seed point is located closer to the center of the vertebra and our method will fail or perform bad if the seed point is located very close to the border of the vertebra. One option to improve the presented algorithm is performing the whole segmentation iteratively: after segmentation has been performed, the center of gravity of the segmentation can be used as a new seed point for a new segmentation and so on. This might lead to more robustness with respect to the initialization. Furthermore, we plan to integrate our manual refinement method that takes advantage of the basic design of graph-based image segmentation algorithms


This has led to the realistic hope that targeting Aß42 immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool’s utility, we developed computer simulation models to examine Aß42 turnover and its aggregation in detail and to test the effect of immunization against Aß dimers.Alzheimer’s disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (Aß), particularly Aß42 monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of Aβ are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.Our model demonstrates for the first time that even a slight decrease in the clearance rate of AßOur model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies. The mai with AD . Levels l ageing  and havel ageing . An assoosome 21 . Hencefosyndrome .in vivo but that the inhibition of LTP did not occur if CSF samples contained only monomers [40 and Aβ42) in the CSF of AD patients compared to age-matched, neurologically-intact controls. Their study showed that the production rate of Aβ is the same in AD as in controls, but detected a slower degradation rate in AD. This suggests that Aβ degradation kinetics may be impaired in AD. The mechanisms underlying this alteration remain unknown. However, protein levels of neprilysin, a neutral endopeptidase, and one of the major Aβ-degrading enzymes located in the brain, have been shown to decline with age [The aggregation process begins with the formation of soluble dimers and larger oligomers (e.g. tetramers and octamers) which interact to form protofibrils . Protofimonomers . This stmonomers . In addimonomers . The gromonomers . Aβ monomonomers . Howevermonomers  measuredwith age ,24. Thiswith age . Other swith age  and in Awith age , althougwith age  and motiin silico the possible effectiveness of interventions. We have used dynamic modelling which means that the model can be used to examine mechanisms leading to the disease state. This has an advantage over experimental approaches which are often limited to the end time-point of the disease. Many biological processes are inherently stochastic. For example, protein aggregation involves random molecular interactions to initiate the formation of pathogenic structures [42 (hereafter referred to as Aβ) to investigate the effects of the degradation rate on Aβ levels and specifically to discover whether a slower degradation rate is itself sufficient to explain the increased level of soluble Aβ and senile plaques seen in AD-affected brains, a molecular phenomenon which is strongly believed to underlie disruption of memory function [The molecular mechanisms involved in AD are therefore complex and as yet we lack clear understanding of which part of the aggregation process would provide the best target for stopping or slowing down AD-related disease processes. In such complex scenarios where it has proved difficult to disentangle the contributing disease processes, mathematical modelling is increasingly being used to complement experimental approaches -32. For ructures . Therefofunction ,18,34. Tfunction , who recThis model showed that the difference in degradation rate has only a small effect on soluble Aβ levels. Aβ does not accumulate with time but just has a slightly higher level in AD compared to normal Figure . Howeverp = 0.00019) for the AD degradation rate  and there was a strong positive correlation between mean Aβ levels and the percentage of times that the threshold was exceeded  . The reaction contains both neprilysin and Aβ as reactants and so a decline in neprilysin with time means that there must be an increase in the number of Aβ monomers in order that the reaction rate remains constant. Therefore our model predicts that the actual activity per neprilysin molecule gradually increases with time due to higher levels of monomers. However, despite the increase in neprilysin activity, the levels of monomers are higher so that there is a greater chance of dimers forming which may then initiate the aggregation process.We carried out parameter scans for each individual parameter in Model 2 for the normal degradation rate, where we examined the effect on plaque formation. We considered the average lag time (time to when plaques first appear), plaque size and the percentage of cells associated with plaques at different ages. We summarize the parameters affecting each of these outcomes in Table kdimer increases, the percentage of cells associated with plaques increases as expected, since increasing kdimer leads to more dimers and so an increased probability of plaque formation. However, there was no effect on plaque size since kdimer only affects the initial step in plaque formation and not subsequent growth which depends on the pool of monomers rather than dimers. A plot of the percentage number of cells associated with plaques at age 100 years versus kdimer is sigmoidal so that very low values of kdimer lead to very low numbers of plaques, followed by an exponential increase and then a flattening off until eventually all cells are associated with plaques .As the value of s Figure A. It is kpf leads to an increase in the percentage of cells associated with plaques, up to a maximum of about 50%  and kpghalf (which is the plaque size at which the growth rate is 50% of the maximum rate). The rate of growth also depends on the plaque size and the availability of Aβ monomers. The model predicts that increasing kpg initially leads to an increase in the percentage of cells associated with plaques but reaches a maximum of just over 12% and then there is a sharp decline to about 10% which remains fairly constant with further increases of the parameter . Most of the parameters are very sensitive to small changes with only small regions where just a proportion of cells are associated with plaques as opposed to all or none. This suggests that Model 3 is less likely to be biologically plausible than Model 2. Therefore, our models provide strong support for the current consensus that plaque growth is mediated by monomers rather than dimers.Model 2 was modified so that plaques grow by the addition of dimers instead of monomers and so that disaggregation of plaques releases dimers. As for Model 2, we chose a parameter set such that very few plaques form under normal Aβ clearance but that at least 25% of cells are associated with plaques by age 100. The parameters in this model are much more sensitive to perturbation by small stochastic effects than in Model 2, and the model predicts that varying all the parameters apart from e Figure . These cWe have shown how a modelling approach can be used to address questions concerning the aggregation of amyloid-beta in Alzheimer’s disease. For many readers, this approach may be unfamiliar and some may consider it premature and incomplete. However, we would like to point out that this is exactly why this paper is both timely and valuable. We developed a stochastic model which could be simulated over long time periods to investigate the effects of Aβ turnover on the accumulation of plaques with age. The model showed that dimers play an essential role in seeding (or starting) the aggregation process and that targeting Aβ dimers rather than monomers is likely to be the best approach for future interventions. The motivation for the model was to examine in detail the basic steps in the process of amyloid aggregation and to use the model to make predictions about the effects of proposed interventions. There are many different hypotheses concerning the aggregation process in AD and great efforts are being made to find effective interventions. Modelling is a useful tool for testing ideas and examining the effects of proposed interventions being able to make predictions which can be tested experimentally. For example, our model predicted that very early interventions could prevent the formation of aggregates at later ages. This could be tested experimentally in an animal model of AD and/or an animal model of trisomy 21.We kept the model as simple as possible and so all the processes that are included in the model are essential to reproduce the results shown here. Stochastic simulation was necessary in order to investigate the cellular variability in plaque formation. Each simulation run represents the outcome for one brain cell and we carried out 500 simulations for each parameter set. The simulation output for 500 cells usually contain cells that do not develop any association with plaques over a 100-year period (depending on the parameter set) and in cells where plaques form, the time of formation is very variable. Our models predict that dimerisation of Aβ, which is the first step in the aggregation process, has a large effect on both the timing of plaque formation and the probability of a plaque forming.We used the model to examine the effects of immunization against dimers by simulating the addition of antibodies at different ages followed by continued provision of antibodies. This more closely resembles passive immunization with continual provision of antibodies. Ideally, a model of active immunization would be preferable as this procedure would act much better clinically. However, active immunization is a complicated process and would require a more complex model than we have attempted here. Although we used a simple method to mimic the process, the findings are still valuable and show that it would be worthwhile to modify the model for active immunization. An important finding from the simulations is that the use of antibodies against dimers could greatly reduce plaque burden and that they would be much more beneficial if used in mid-life rather than in old age, but that benefit is provided even if administered at later ages provided the patient did not already have a high burden of plaques. An exciting possibility is the development of a vaccine for babies with Down’s syndrome to prevent the early onset of AD. The current model has focussed on the role of amyloid plaques in AD although the actual role of plaques in the disease process is still not clear. On the one hand some studies have shown a lack of correlation between plaque burden and the duration or severity of disease ,41. ConvOur models investigated only the role of monomers and dimers in the aggregation process but could be extended to include further steps such as larger oligomers and protofibrils. This could be achieved by adding further species to the model and replacing the reaction of two dimers forming a plaque with a series of reactions. This would allow the model to be used to examine the effects of interventions on soluble oligomers. Since soluble oligomers may be more toxic than the fibrillar form of amyloid, this would be very desirable in order to use the model to test interventions prior to clinical trials. However, it should be noted that this will make the model more complex and slower to simulate, and we would need further information on how the different amyloid components adversely affect cellular processes. As the model is encoded in SBML, any future developments will be straightforward. Although we focus here on the role of amyloid in AD, we have previously modelled the aggregation of both Aβ and tau in a more complex model . HoweverProgressive ageing of populations around the world, has been accompanied by alarming growth in the numbers of patients diagnosed with AD and other dementia-like illnesses. The large number of studies showing a correlation between synaptotoxic amyloid species, synapse loss and cognitive deterioration which clinically characterises AD patients, strongly suggests that targeting abnormal levels of Aβ in the brain might provide a potentially powerful preventative strategy. Nevertheless, there remain important questions about how such interventions might act upon the normal mechanisms for formation and degradation of protein aggregates within the brain. It is important to understand the kinetics of these processes, which are significantly influenced by the actions of chance at the levels of the underlying molecular interactions. By employing stochastic mathematical modelling, the present study served to provide evidence for the validity of following such an approach. Moreover, Aß dimer formation was highlighted as a particularly important contributing molecular element underlying AD pathology, in agreement with recently published experimental studies. In conclusion, the study served to provide better insight into the kinetic processes underlying eventual fibril/plaque formation, which might help pinpoint where and at which time-point in such processes is the most therapeutically promising targets for therapeutic intervention.We began by building a model of Aβ turnover, which includes reactions for Aβ production and degradation but does not consider the aggregation process. The aim of this model, which we refer to as Model 1, was to see the effect of decreased clearance on levels of Aβ over time. We then extended this model to include details of Aβ aggregation (Model 2) to examine the effects of decreased clearance of Aβ on the aggregation process. We also examined a variation of Model 2 in which a different assumption was made concerning the growth of plaques. Details of all three models are given below.This model contained just one molecular species, Abeta, and two reactions, synthesis and degradation:kprod is the hazard of this reaction. The second reaction equation is of first-order and the hazard of this reaction is kdeg Abeta. Details of how the model was coded and simulated are given later in the Methods section under the heading “Tools used for model construction and simulation”.We used mass action kinetics which means that the rate of a reaction is directly proportional to the number of molecules  of each reactant raised to the power of its stoichiometry. In the first reaction equation, Source is a constant dummy variable and so this is a zeroth-order reaction with a constant production rate and kprodAbeta = 1.86e-5 molecules s-1) from Mawuenyega and colleagues [kdegAbeta * Abeta). Therefore we need to know the steady state level of Aβ. The concentration of Aβ42 was measured in CSF and was found to be 500pg/ml for patients with normal Aβ clearance rates [42 at the cell surface where it is produced. Usually, Aβ is rapidly cleared and therefore we would expect the level of Aβ in the vicinity of the membrane to be low, especially as APP is found throughout the cellular membrane and is not localised to a particular position. Therefore we assume that the local level of Aβ is initially given by Abeta = 1, and assume kdegAbeta = 2.1e-5 s-1 for the normal degradation rate and kdegAbeta = 1.5e-5 s-1 for the degradation rate in AD. The model was simulated 500 times for a particular set of parameters over a time period of 100 years and the level of Aβ was output to a file (500 time-points were chosen). The results were analysed and plotted in R .5. Aggregates may disaggregate to release a monomer.6. When aggregates reach a certain threshold size, they continue to grow until they reach a maximum size. Once the aggregate has passed the threshold size we consider it to be a plaque.7. Since the actual size of the threshold is not fixed, i.e. it varies in a stochastic fashion, we do not have separate species for small aggregates and plaques and use the name AbP for all aggregates. Therefore, in our model output analysis we assume that if AbP is greater than a certain level then it is counted as a plaque. We chose 30 for this threshold size as plaques of this size, or larger, rarely completely disaggregated.kdegNep, for the degradation rate of neprilysin. We chose the value for this so that neprilysin values declined by about 30% by age 60 which resulted in Aβ degradation rates similar to those observed in AD patients at late ages.We used the same values for Aβ production and degradation as in Model 1. However, once we added the aggregation steps, we found that the model predicted plaques could occur at early ages when we set the degradation rate at the AD level. Originally we assumed that Aβ clearance was impaired throughout life. However, with this assumption it was not possible to find a parameter set where levels of plaques are very low early in life but increase only after the age of 60 years Figure B. There In vitro experiments show that once plaques start to form, they grow rapidly (within about 24 hours) and then stop growing, i.e. they reach a maximum size. Since Aβ is continually produced, this observation suggests that degradation of plaques takes place so that the process is not irreversible and that at a certain size disaggregation balances plaque growth. It has been shown that plaques have a porous structure and so the rate of disaggregation is likely to be proportional to the volume of the plaque [kpg) at a particular plaque size which is determined by the parameter kpghalf , as our model output shows that once aggregates reach this size, they are unlikely to disaggregate completely but continue to grow in size. Therefore, our use of the term “plaque” incorporates all aggregates of Aβ which have exceeded a critical threshold size and have a high probability of forming the amyloid deposits which are termed “plaques” in the disease state.kpf and kpg based on these measurements. We do not have data for the rates of dimerisation, disassociation of dimers or disaggregation of plaques and so we used a global parameter scan in order to find parameter sets which predict very low levels of plaques before the age of 50 years and then a gradual increase in the number of plaques up to age 100 years (the maximum age used in our simulations). In addition, we assumed that the parameters need to satisfy the condition that levels of plaques do not exceed 10% by age 100 years when Aβ clearance is normal, but that for impaired Aβ clearance, levels exceed 25% by the age of 100 calendar years. We do not have data on the percentage of cells associated with plaques in normal ageing and AD, but these values seemed reasonable. In future, these values may be measurable and then it would be possible to fit parameter sets to clinical data.The rate of fibril formation and fibril growth has been measured experimentally and it has been reported that fibrils grow at a rate of 0.5 monomers per minute and that new fibril nucleation takes place approximately once in 5 days per micelle ,50. We ckprod and kdeg are fixed, and constrained kpf and kpg to be within half and twice the values given by experimental data [kdeg declines with age) and eliminated any sets which either produced less than 10% of cells associated with plaques by age 100 or produced too many plaques (more than 15%) before the age of 50 years. This resulted in 10 possible sets. We did further simulation runs for all these sets and finally chose the set which gave the best difference between plaque levels for normal and decreased clearance of Aβ. Details of the parameter sets used in this analysis are given in Additional file 2Since we need to use stochastic simulation, it is not practical to do a full global parameter scan. Instead we used Latin hypercube sampling to generate 100 parameter sets. We assumed that the values of tal data . The othkpf all the parameters can take values that result in no cells associated with plaques, all cells associated with plaques or an intermediate level of plaques , a computer-readable format for network models . The tooThe slopes of the regression lines in Figure AD, Alzheimer’s disease; APP, Amyloid precursor protein; APOE, Apolipoprotein E; CSF, Cerebrospinal fluid; IDE, Insulin-degrading enzyme; LTP, Long-term potentiation; SBML, Systems biology markup language; SDS, Sodium dodecyl sulphate.The authors declare that they have no competing interests.This file contains details of parameters used in the global parameter scan for Model 2 showing how the best parameter set was chosen.Click here for fileThis file contains the SBML code for Model 2 with Aβ degradation rate decreasing with time.Click here for file


Clinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires the matching of a large volume of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, some of the patient’s information necessary to determine the status of the eligibility criteria may not be available at the time of pre-screening.We showed that the classic approach based on negation as failure over-estimates rejection when confronted with partially-known information about the eligibility criteria because it ignores the distinction between a trial for which patient eligibility should be rejected and trials for which patient eligibility cannot be asserted. We have also shown that 58.64% of the values were unknown in the 286 prostate cancer cases examined during the weekly urology multidisciplinary meetings at Rennes’ university hospital between October 2008 and March 2009.We propose an OWL design pattern for modeling eligibility criteria based on the open world assumption to address the missing information problem. We validate our model on a fictitious clinical trial and evaluate it on two real clinical trials. Our approach successfully distinguished clinical trials for which the patient is eligible, clinical trials for which we know that the patient is not eligible and clinical trials for which the patient may be eligible provided that further pieces of information (which we can identify) can be obtained.OWL-based reasoning based on the open world assumption provides an adequate framework for distinguishing those patients who can confidently be rejected from those whose status cannot be determined. The expected benefits are a reduction of the workload of the physicians and a higher efficiency by allowing them to focus on the patients whose eligibility actually require expertise. Patient recruitment is a major focus in all clinical trials. Adequate enrollment provides a base for projected participant retention, resulting in evaluative patient data. Identification of eligible patients for clinical trials  or identification of clinical trials in which the patient can be enrolled (from the patient’s perspective) is an essential phase of clinical research and an active area of medical informatics research. The National Cancer Institute has identified several barriers that health care professionals claim in regard to clinical trial participation. Among tAutomated tools that help perform a systematic screening either of the potential clinical trials for a patient, or of the potential patients for a clinical trial could overcome this barrier. EffortsThe ASTEC  project aims at automating the search of prostate cancer clinical trials to which patients could be enrolled to. It featThe EHR4CR project aims at facilitating clinical trial design and patient recruitment by developing tools and services that reuse data from heterogeneous electronic health records. The TRAAll these studies on data and criteria representation, integration and reasoning are motivated by the requirement to have the necessary information available at the time of processing the patient’s data, and assume that somehow, that will be the case.Missing information that is required for deciding whether a criterion is met leads to recruitment being underestimated.Solutions for circumventing this difficulty consist either in making assumptions about the undecided criteria, or in having a pre-screening phase considering a subset of the criteria for which patient’s data are assumed to be available.Bayesian belief networks have been used to address the former but requThe latter leaves most of the decision task to human expertise, which provides little added value  and is still susceptible to the problem of missing information for the pre-screening criteria.We propose an OWL design pattern for modeling clinical trial eligibility criteria. This design pattern is based on the open world assumption for handling missing information. It infers whether a patient is eligible or not for a clinical trial, or if no definitive conclusion can be reached.Ck cannot be infered from the combined values of other criteria). Each criterion can be modeled as an unary predicate C(p), where the variable p represents all the information available for the patient. C(p) is true if and only if the criterion is met.A clinical trial can be modeled as a pairall the inclusion criteria and none of the exclusion criteria are met.A patient is deemed eligible for a clinical trial if Before making the final decision on the list of clinical trials for which a patient is eligible for, there are intermediate pre-screening phases where only the main eligibility criteria of each clinical trial are considered. Such pre-screening sessions rely on subsets ofI0 and I1, and two exclusion criteria E0 and E1.For the sake of clarity, in addition to the general case, we will consider a simple clinical trial with two inclusion criteria For example, these criteria could be: I0: evidence of a prostate adenocarcinoma;•I1: absence of metastasis;•E0: patient older than 70 years old;•E1: evidence of diabetes.•According to equation 2, a patient would be eligible for the clinical trial if and only if he has a prostate adenocarcinoma and has no metastasis and is neither older than 70 years old nor suffers from diabetes.Because of De Morgan’s laws, equation 1 is equivalent to:I0 or of diabetes for E1) or an asserted absence (e.g. metastasis for I1).Even though equation 1 and equation 3 are logically equivalent, the latter is often preferred because it is an uniform conjunction of criteria. Note that the negations in front of the exclusion criteria are purely formal, as both inclusion and exclusion criteria can represent an asserted presence .True”, “False” or “Unknown” for the inclusion criterion I1 and the exclusion criterion E1. Tablep2, p5, p6, p7 and p8) for which at least a part of the information is unknown, three  illustrate a conflict between the value of equation 4 and expected inclusion decision. A strict interpretation of equation 4 leads to the exclusion of the eight patients: For our fictitious clinical trial, we consider a population of nine patients covering all the combinations of “p0, p3 and p4), all the information is available;•for three of them (p5 and p6), some information is unknown, but the available information is sufficient to conclude that the patients are not eligible;•for two of them , however, the cause of rejection is either because one of the inclusion criteria cannot be proven (I1 for p7 and p8) or because one of the exclusion criteria cannot be proven to be false (E1 for p2 and p8).•for the three others  and those we do not know if they are eligible (the third category). This is a problem because patients from the first two categories should be excluded from the clinical trial, whereas those from the third category should be considered for inclusion.Currently, the case of each patient diagnosed with cancer is examined in a multidisciplinary meeting (MDM) gathering experts . The goal is to determine collectively the best therapeutic strategy for the patient, including consideration of potential inclusion into clinical trials. This preliminary stage is called pre-screening because it takes place before obtaining the patient’s informed consent . It mainly relies on retrospective data coming from the patient health record. At this point, all the information necessary for determining the status of each inclusion and exclusion criteria may not be available, but the rationale is to focus on the clinical trials for which the patient may be eligible for. It should be noted that the missing items may differ between patients. One solution could be to assume the values of the unknown criteria in order to go back to a situation where inclusion or exclusion could be computed using equation 3.In this case: •inclusion criteria for which the available information is not sufficient to compute the status are considered to be met;•exclusion criteria for which the available information is not sufficient to compute the status are considered not to be met.Therefore, in the case where the available information is not sufficient to compute the status of a criterion, a different status is assumed depending on whether the criterion determines inclusion or exclusion.I1 is true, whereas the lack of information about diabetes would lead to the assumption that E1 is false.Referring to our fictitious clinical trial, the lack of information about the absence of metastasis would lead to the assumption that This situation raises several issues: •a different status is assumed depending on whether the criterion determines inclusion or exclusion;•the assumed status depends on the nature of the criterion (i.e. inclusion or exclusion) and not on its probability;p0 or p1 vs “under the assumption that...” for p2, p7 and p8);•one has to remember that the value for at least a criterion has been assumed in order to qualify the inferred eligibility .•this qualification can be difficult to compute .Overall, 11,323 values (60.90%) were not specified. On average, for each case studied in a MDM, 39.6 fields (among 65) had an unknown value.All of the 286 cases studied had at least some of the 65 fields with an unknown value. Indeed, the case with the most fields filled still missed 22 of them.59 fields (90.77% of 65) had a missing value in at least one of the 286 cases. The six fields that were systematically filled were: the patient identifier, the MDM date, the patient’s birth date, the patient’s gender, the tumor anatomic site and the primary histological type.During this period, 4 clinical trials related to prostate cancer running at Rennes Comprehensive Cancer Centre were considered during the MDM. TableWe propose an OWL design pattern for modeling clinical trial eligibility criteria. We then explain how the reasoning unfolds using the fictitious clinical trial from TableWe reused anonymized data from the patients’ medical records and did not conduct any experimental study. The study was approved by Rennes’ Hospital ethics evaluation committee institutional review board under the reference 13-26 (2013).C_i  and possibly add a necessary and sufficient definition representing the criterion itself (or use SWRL);•for each criterion, create a class Not_C_i defined as Not_C_i ≡Criterion ⊓¬C_i. This process can be automated;•for each criterion, create a class Ct_k (placeholder);•for each clinical trial, create a class Ct_k_include as a subclass of Ct_k with a necessary and sufficient definition representing the conjunction of the inclusion criteria and of the exclusion criteria (cf. equation 3) (Ct_k_includeI_iNot_E_j);•for each clinical trial, create a class Ct_k_exclude (placeholder) as a subclass of Ct_k;•for each clinical trial, create a class Ct_k_exclude_at_least_one_exclusion_criterion as a subclass of Ct_k_exclude with a necessary and sufficient definition representing the disjunction of the exclusion criteria (Ct_k_exclude_at_least_one_exclusion_criterionE_j);•for each clinical trial, create a class Ct_k_exclude_at_least_one_failed_inclusion_criterion as a subclass of Ct_k_exclude with a necessary and sufficient definition representing the disjunction of the negated inclusion criteria (Ct_k_exclude_at_least_one_failed_ incl_criterionNot_I_i);•for each clinical trial, create a class C_i as we know he matches criteria, and as many Not_C_j classes as we know he does not match criteria, even if this is ontologically questionable (a patient is not an instance of a criterion). How the patient’s data are reconciled with the criteria by making the patient an instance of the criteria is not specified here: it can be manually, or automatically with OWL necessary and sufficient definitions or SWRL rules for the C_i and Not_C_j classes.•represent the patient’s data with instances Figures and2. FC_i or Not_C_i, and therefore will also be instantiated as either Ct_k_include .If all the required information is available, after classification, for each criterion the patient will be an instance of each C_i nor of Not_C_i , so he will not be classified as an instance of Ct_k_include either. However, the patient may be classified as an instance of Ct_k_exclude_at_least_one_exclusion_criterion or of Ct_k_exclude_at_least_one_failed_inclusion_criterion. As both are subclasses of Ct_k_exclude, we will conclude that the patient is not eligible for the clinical trial. We will even know if it is because he matched an exclusion criterion .If not all the information is available, because of the open world assumption, there will be some criteria for which the patient will neither be classified as an instance of Ct_k_include nor of Ct_k_exclude (or its subclasses), then we will conclude that the patient can be considered for the clinical trial, assuming the missing information will not prevent it .a posteriori analysis of the 119 cases proposed by our model but not by the MDM revealed that several were not proposed even if they formally met the eligibility criteria because their Gleason score was deemed too low. We added an inclusion criterion requiring patients to have a Gleason score superior or equal to 7. This resulted in 67 cases potentially eligible, among which were 24 of the 30 actually eligible . The six false negative cases had a Gleason score of 6. Among the 43 false positive, at least 15 were rejected during the MDM because of additional information not available at the time of pre-screening: 8 because new results indicated that they did not have cancer, 3 because too much information was missing and 4 because other elements such as a relatively young age resulted in proposing a surgical treatment instead of the clinical trial.It should be noted that the According to our system, among the 286 cases, 0 were formally eligible, 34 were potentially eligible, and 252 were not eligible. The 6 cases that were identified as eligible by the experts during the multidisciplinary meetings were all among the 34 proposed by our system . Among the 28 false positive, 6 cases were rejected during the MDM because of additional information not available at the time of pre-screening, 5 were rejected on the basis of information present in their report but erroneously missing in the database, 15 were rejected because there was no evidence of recurring cancer , and 2 cases were rejected because too much information was missing.Adding implicit inclusion criteria for performing the same post-processing as the first clinical trial resulted in only 17 potentially eligible cases, among which were 3 of the 6 identified by the experts . This shows that this strategy is not relevant for this clinical trial.The observed proportion of missing information is compatible with results from other studies. Köpcke The analysis of the first clinical trial demonstrates that missing information would have led to the rejection of all the patients proposed as eligible by the experts during the multidisciplinary meetings. Our approach identified potentially eligible patients , among which were all the patients deemed eligible by the experts .This shows that our system confidently rejects non-eligible cases, which leaves more time to examine the others during the multidisciplinary meetings. Moreover, in the first clinical trial, precision can be significatively improved by adding pragmatic criteria that further discriminate the patients who would not be considered as eligible even if they meet the pre-screening criteria. Note that this second step can be kept separate from the formal determination of eligibility but is useful both for the acceptance of the system by the experts and for maintaining the efficiency of the multidisciplinary meetings.ad hoc conversion between inclusion and exclusion criteria. For example, the inclusion criterion “absence of ischemic heart disease” can be converted into the exclusion criterion “presence of ischemic heart disease”. The former will probably never be met because a patient’s record only mentions ischemic heart disease when they are present, whereas the latter will (correctly) only exclude those patients having evidence of ischemic heart disease. The problem is that if “absence of ischemic heart disease” had been an exclusion criterion, it would likewise have been converted into the inclusion criterion “presence of ischemic heart disease” and the system would have  rejected patients whose record does not mention the presence nor the absence of ischemic heart disease. Moreover, a criterion can be an inclusion criterion for a clinical trial and an exclusion criterion for another trial, so this strategy is not a general solution to the problem of missing information.Missing information can partially be handled even with reasoning based on negation as failure using Reasoning about the conjunction of the eligibility criteria should be handled by OWL, which supports the open world assumption, rather than by related technologies such as SWRL which do not. It would be possible to write a SWRL rule that represents the conjunction of criteria (cf. formula 3). However, it is impossible to distinguish situations where we know that one criterion is not met from those where we cannot determine if it is met, because in both cases the rule will not fire.Applying our criteria modeling design pattern to real clinical trials and real patients’ data was a manual process. The reasoning part of our contribution focused on combining the status of the eligibility criteria when some of then can not be determined, not on determining the statuses themselves. However, both points are of importance. Our design pattern consisted in modeling each criterion by two classes representing the certain presence and the certain absence of the criterion for a patient. As we have seen in this article, this first modeling part was easy, can be automated, and addressed the problem of missing information as one of the causes of patient recruitment underestimation. When evaluating our system on real clinical trials and real patients’ data, we had to determine for each patient whether each criterion was met. This required both the occasional decomposition of complex criteria into logical combinations of simpler conditions, and the binding with the patients’ data representation in the local EHR. The first step is generic and rather straightforward. It only has to be done once, and can be reused shared between hospitals or reused if a criterion appears in several clinical trials. The second step is clearly dependent on the local representation of patients’ data, and was more difficult and labor-intensive. It also required to write the functions that process the data, which took a couple of days for each clinical trial (a portion of the code written for the first CT could be reused for the second one).The standardization of data elements would provide a significant help to the challenge of connecting the patients’ data with the eligibility criteria. The main standard organizations  and CDISC (for cliThe use of RDF-based (Resource Description Framework) Semantic Web formats (hopefully standardized) data elements and eligibility criteria would also make their integration easier. RDF proved to be a key elements for data integration in more general contexts. Associated querying and reasoning techniques based on SPARQL (SPARQL Protocol and RDF Query Language) and SPIN (SPARQL Inference Notation) for determining the status of eligibility criteria would have the advantage of having the rules represented in the same language as the schema and data to which those rules are attached, as well as having sustainable computation performances. On the other hand, these strategies usually rely on closed-world reasoning. Future work should focus on studying the benefits of such an approach and on determining how well it can address the problem of missing information.Potential applications of our approach are not limited to clinical trials. They coWe have shown that ignoring the missing information problem for automatic determination of clinical trial eligibility led to over-estimate rejection. Systems based on negation as failure infer that the patient is not eligible if it cannot be proved that he is eligible, whereas the situations where it cannot be determined that the patient is eligible nor that he is not eligible should be identified and treated separately. A retrospective analysis of 252 patients with prostate cancer showed that for the four clinical trials of interest, all the patients had at least one missing value that resulted in their rejection whereas 62 of them were actually eligible for at least one of the clinical trials.We proposed a modeling strategy of eligibility criteria in OWL that leveraged the open world assumption to address the missing information problem. Our approach was able to distinguish a clinical trial for which the patient is eligible, a clinical trial for which we know that the patient is not eligible and a clinical trial for which the patient may be eligible provided that further pieces of information (which we can identify) can be obtained.By confidently rejecting some of the non-eligible cases, our approach leaves more time to examine those requiring medical expertise during the multidisciplinary meetings.The authors declare that they have no competing interests.OD designed the study, implemented the reasoning mechanism and drafted the manuscript. PB and AB implemented the patient’s data repository. OZ collected the patient’s data and provided medical expertise. AB and MC revised the manuscript. All authors read and approved the final manuscript.OWL files for the validation set. The file clinicalTrial-validation.tgz is a zipped tarball containing a readme.txt and the OWL files modeling the criteria and the patients’ data from the validation set.Click here for fileOWL files for the first clinical trial of evalution set. The file clinicalTrial-getug14.tgz is a zipped tarball containing a readme.txt and the OWL files modeling the criteria and the patients’ data for the first clinical trial from the evaluation set.Click here for fileOWL files for the third clinical trial of evalution set. The file clinicalTrial-getug16.tgz is a zipped tarball containing a readme.txt and the OWL files modeling the criteria and the patients’ data for the first clinical trial from the evaluation set.Click here for file


We studied 54 patients with ankylosing spondylitis with questionnaire in order to determine their view on threat to quality of their life related to the disease. We have show that pain and significant disability are the main threats associated with the disease in view of the patients. Social aspects (losing of job or decreasing of income) are also important for the patients, while management of the disease is not considered as arduous. The results of patients’ opinion may be helpful in designing of educational programs for them. Ankylosing spondylitis (AS) is a chronic, incurable, inflammatory disease affecting mainly the axial spine and sacroiliac joints . The disThe aim of the present study was to obtain patients’ opinion on factors associated with AS that may potentially influence their quality of life. In other words, we evaluated threats to patients’ life associated with AS, which were considered by the patients as important risk factors.The study was based on 54 patients  with definite AS. The patients were associated with “the Pro Rheumate Group of AS patients,” a patient association in Katowice. In order to investigate the patients’ opinion, we created a questionnaire consisting of 12 questions. All questions had a form: “How important is to you a possible impact on your life of ….?” The patients were able to choose one of five answers, from “very important” to “not important at all.” The questions were presented in a mixed order but were focused on ability to work (and obtain income), family and social life as well as outcome of the disease . We also asked about importance of pain, frequent admissions to a hospital or impediments associated with management of AS. The questionnaire was anonymous and voluntary and was distributed at the patients’ group meeting. We limited demographic questions to sex of the patient, age  and duration of AS symptoms  in order to keep anonymity.Fifty-four patients returned the questionnaire, and we scored the answers as following: very important = 4, important = 3, moderate important = 2, rather not important = 1, not important at all = 0.Results are presented in Fig. Management of AS seems to be rather comfortable to the patients. Potential frequent hospitalizations were not considered as a significant factor impairing patients’ life. Some patients seem to have problems with performing home-based exercises everyday . Going iThe main difference between our study and previous investigations is that we did not focus on quality of life of AS patients but on there views how disease may affect their life. Thus, our results reflect level of knowledge and understanding of the disease by the patients. It is important especially in chronic diseases. The questionnaire used for this study was created also for practical reasons. We would like to know more how the AS patients consider their “life with AS.” This knowledge is important for designing of the patient educational program.Our study was based on 54 patients only. Due to low size of the investigated group, we did not analyze influence on patients’ opinion of such factors as age or duration of AS symptoms. It should be mentioned that some differences were found. Young male patients were more threaded by risk of losing of their jobs, while an impairment of sexual life due to the disease was more important to male patients than to female ones, but there was no relationship of the significance of the impairment with age of the patient. We do not collect data for analysis of relationship of education, place of living (city vs. country) and other factors to the patients’ view. Almost all our patients were living in a city, and all were treated with biologics. The other interesting aspect of the study is relationship of the patients’ view to their psychological state, and the threads may reflect not only patients’ knowledge of the disease but also their depression , 7.Further studies in this field are needed to facilitate designing of educational programs for the AS patients.


Pyrus Commmunis) is established. The results demonstrate that with only intensity, this relationship has a potential for determining the angle of incidence with the leaves surface with a precision of ±5° for an angle of incidence smaller than 60°, whereas it is more variable for an angle of incidence larger than 60°. It appears that TLS beam footprint, leaf curvatures and leaf wrinkles have an impact on the relationship between intensity and angle of incidence, though, this analysis shows that the intensity of scanned leaves has a potential to eliminate ghost points and to improve their meshing.Light Detection and Ranging (LiDAR) technology can be a valuable tool for describing and quantifying vegetation structure. However, because of their size, extraction of leaf geometries remains complicated. In this study, the intensity data produced by the Terrestrial Laser System (TLS) FARO LS880 is corrected for the distance effect and its relationship with the angle of incidence between the laser beam and the surface of the leaf of a Conference Pear tree ( The can2 56], authTen leaves were randomly picked from 30 2-year old Conference pear trees on June 16th 2010. Those trees are planted in two rows of 15 trees in an East-West direction with a distance of 30 cm between the trees and 360 cm between the rows. They are located in Heverlee, Belgium . Since the leaves are curled [In a second experiment, from each of the 15 pear trees of the first row  a secondI(x), the angle of incidence computed with the intensity and angle of incidence relationship at a point x on the scanned leaf and ωLSR the angle of incidence provided by the LSR on the entire leaf. The distributions  of those Δω values are shown. The difference between the angle of incidence provided by the LSR and the one deduced by the average of the corrected intensity on the leaf is calculated.In this case, ghost points as wrinkles and curvatures are also selected. Then, the differences (Δω) between the angle of incidence found by this LSR and the angle of incidence provided by the intensity for each hit of the TLS beam are mapped for each of those seven leaves:2.3.First, a manual sub-selection of the point cloud and their corresponding intensity is made. A second sub-selection is made based on an intensity and distance threshold . To do s2.3.1.99%).A first study is made to establish the relationship between the intensity and the distance with the set up described in Section 2.2.1. In , the relref. One has cref = f99%(dref) − I (dref) with I (dref) the recorded intensity at an arbitrary reference distance. For each intensity I (d) for this same target at a distance d, the calibrated intensity, Ic (which is now independent of distance) is calculated as:Once done, a constant value c for a target material at a given ω is determined by the difference between its intensity value and the intensity value of the 99%-Spectralon® at a fixed reference distance d99% and the corrected intensity is calculated for each distance. Finally, as the value f(dref) is unknown, the distance effect on the intensity is corrected with the following formula [99% (d) − I (d).To know the quality of the distance correction, a Root Mean Square Error (RMSE) between the value of the piecewise polynomial interpolation f formula :(7)Ic=fFurther investigation on the intensity correction, reflectance relationship and radiometric calibration could be done. In , the aut2.3.2.i to the fitted plane. Finally, the normal angle to the plane is given by the coefficient of the plane equation. The angle of incidence ω with the surface equals:To obtain the angle of incidence with a surface (flat by assumption) represented by a selection from the point cloud, a Least Square Regression (LSR) is proceed on the sub-selection . As ther2, slope and intercept of a linear regression of the angle determined by the LSR and the goniometric platform angle are given. The targeted platform is placed at approximately 2.05 m from the TLS beam aperture. Knowing the angle of incidence provided by the LSR, it can finally be related to the intensity averaged over the cloud of points selected  is 3.56 m and f99%(dref) = 1781.45 intensity units. The LS880 logarithm filter effect is clear as can show relationships between the various reflectances measurements. The distance effect correction with the piecewise polynomial is valuable for a distance larger than 1 m, especially for materials with a reflectance larger than 48%, while the 22%-reflectance Canson® paper yields results of inferior quality. This result is analogous to the FARO LS HE80 used in [® and paint) have the worth quality and the graph shows unexpected differences in terms of reflectance that have not been detected by the spectroradiometer. Similarly, the difference between the 80% and the 83% Canson® papers is not clear. The logarithmic correction suggested in [ used in . The disested in  is not pThe RMSE between the translated 99%-spectralon piecewise polynomial interpolation used as reference at a distance of 3.56 m  and the 3.2.2 of 1, a slope of 1 for both horizontal and vertical rotation and an intercept of 1° for vertical and 2.8° for horizontal rotation.The angle of incidence provided by the LSR provides acceptable results with the goniometric platform. The regression of the correlation graph between the angle of incidence calculated manually and the one given by the LSR provides an r3.3.3.3.1.The angles of incidence provided by the LSR approximate the ones given by the protractors of the goniometric platform. No clear difference appears between the azimuthal rotation of the goniometric platform and the elevation rotation. Given with a resolution of 5°, the curve of relationship between the corrected intensity and angle of incidence for the two different rotations are similar and the maximal absolute difference for the intensity is 20 units (corrected intensity) for an angle of incidence of 10°. This is negligible compared to the intensity variation as a function of angle of incidence. We get similar results in the comparison of the abaxial and adaxial sides of the leaves where the maximal absolute intensity difference 38 units (corrected intensity) for an angle of incidence of 10°, which is also negligible. Because of those two results, both cases are not taken into account in this study (graphs not shown).Because of the size of the beam diameter and divergence, its footprint diameter could become larger than the leaf itself. It ranges from 0.046 m for an angle of incidence of 85° to 0.004 m for an angle of incidence of 0°. At this distance, the TLS beam footprint diameter is 20% of the leaf width for an angle of incidence smaller than 20°, it is 45% of the leaf width for an angle of incidence greater than 65° and it exceeds the leaf width for an angle of incidence greater than 80°. Though the type of the laser sensor is unknown, the weight of the goniometric platform intensity could be lower than suggested in In conclusion, retrieving the angle of incidence with the intensity would have a precision of ±5° and because of the diameter of the TLS beam footprint, it is not possible to measure the angle of incidence with the intensity for angle larger than 55–60°.1 and b2). The intensity and angle of incidence relationship can be expressed as:At a first sight, a logarithmic or cosine fitting could be made as it is insinuated in - the logarithmic function that has not been corrected,- a cosine function, and- the reflectance value as a function of angle of incidence with the leaf surface ρ(ω).As one can see, three functions appear:As the optical properties of the leaves are unknown (they are not Lambertian ) and the3.3.2.The test shows a vertical and positive translation in the intensity values for angles smaller than 60°  comparedAs in the previous experiment, the intensity increases with an angle of incidence decrease, but the measured intensity values are higher. It could be interpreted in two ways: (i) it is higher in terms of intensity and is vertically translated to +50 units (corrected intensity) or (ii) it is larger in terms of angle of incidence and is horizontally translated to +10°.In addition, the precision to find an angle of incidence from the corrected intensity for angles of incidence smaller than 60° is larger than in the previous experiment: (i) ±10° for angles of incidence smaller than 30° and (ii) ±15° for angles of incidence ranging from 60° to 30°.Curvatures: It is possible that in the selection, undesirable parts of the point cloud are selected. Their intensity have a varying impact in the average intensity depending on the quantity of these undesirable points, whereas those points can easily have an impact in the LSR and thus on the angle of incidence. For instance:if a leaf that is perpendicular to the beam is selected and if this selection includes a sub-selection which forms a plane which is almost parallel to the beam, then, the LSR on this selection will provide an angle of incidence larger than expected and with a higher intensity (depending on the quantity of undesired points that are selected). That would be the reason why With a similar reasoning, the selection of a leaf including a zone which has a large angle of incidence with the TLS beam and a curved zone could present a smaller angle of incidence than expected.Wrinkles: In the case where a leaf that has many wrinkling is selected, then the impact of these on the intensity is significant whereas the LSR will not consider them.Footprint and point cloud quality: As the leaves are not placed onto on a larger flat surface such as the goniometric platform, the quality of the point cloud representing those leaves is lower especially for large angle of incidence and for increasing distances. This is the reason why there is a lack of data for the two groups of trees 1–7 and 12–15, especially for angle of incidences ranging from 90° to 60°  as leaves n° 3 to 7, have a skewed normal distribution for their Δω. For leaves n° 3 to 5, this distribution is translated to respectively + 13°, + 5° and + 9°. It means that their average intensity represents well the angle of incidence provided by the LSR as it has been previously seen.At the opposite, the average intensity for leaves n° 6 and 7 is translated to respectively −10° and −8°. For leaf n° 6 it could be explained by the case i.a) of the Section 3.3.2. as the side of the leaf forms a large angle of incidence with the beam. This provides a larger angle of incidence  which isWrinkles: Leaves n° 1 and n° 2 illustrate well the impact of wrinkles on the intensity. Both show a multimodal distribution.Footprint and point cloud quality: In general, ghost points are presents for surface with large angle of incidence as it is also shown by the other leaves. Thus, one can consider that a point with a low intensity has a higher probability to be a ghost point. Leaf n° 2 shows a large surface that has a large angle of incidence with the beam but those points does not look like ghost points. In fact, this leaf seems to be lengthened. In this case, it would be more efficient not to delete those point, but to correct them, depending on their positions on the leaf.Footprint and intensity mixing, physiology, multiple scattering and TLS radiometric calibration: those factors are not tested since the scan used in this last study is the same than in the previous one.Some of the assumptions of the previous section are confirmed by these measurements:4.- for the goniometric platform only,- for flattened leaves placed on the goniometric platform,- for some parts of the leaves that are fixed on the tree,- for entire leaves that are fixed on the tree.In this study, Conference pear tree leaves are scanned and the intensity data provided by the TLS is analyzed with a particular focus on its properties for describing geometry of leaves. Prior to that, the intensity is corrected for the distance effect and the angle of incidence provided by the LSR is tested on the goniometric platform. Then the relationship between the corrected intensity and the angle of incidence is determined with flattened leaves placed on a goniometric platform. Next, this relationship is tested on flat part of the leaves that are still attached to the pear trees. Finally, the angle of incidence is determined using a LSR on an entire leaf, and this notwithstanding leaf curvatures and wrinkles. The Δω see  is mappe- the LSR conditions (a low RMSE with a limited number of points that are away of the LSR plane) and the leaf curvatures and its wrinkles,- the impact of the footprint diameter of the TLS beam,- the physiological state of the plant, the radiometric calibration of the TLS or even a multiple scattering occurring in the canopy.It appears that the flattened leaves on goniometric platform provide a good precision (±5°) but maybe a poor accuracy in terms of finding the angle of incidence with the intensity and because of the incomplete radiometric calibration of the TLS intensity. In the case where this radiometric calibration is sufficient, the test made on the partial selection of leaves on the tree would provide a more accurate result (+50 units of corrected intensity). Still, this last test brings a lower precision in the definition of the relationship (from ±10° to ±15° depending on the angle of incidence). This shift in the accuracy could be explained either by:In the last test, it is clear that wrinkles and undulations are playing a large role in the precision. It is also shown that angles of incidence larger than 60° with pear tree leaves will provide bad results in term of accuracy and precision. Even the measurement on the goniometric platform could not provide better information because of the 3%-reflectance painting surrounding the leaves and so in the mixing of their intensity in the point cloud. As previously seen, scanning larger leaves could reduce this angle of incidence limit. In addition, if the second experiment shows a consistency in the distance correction, it appears that distance plays a great role in the capacity to extract a good point cloud and this to make a LSR with enough points. That probably depends on leaf size, and one might expect that the measurements should be extended to a wider range and with larger leaves. In general, distance, angle of incidence and leaves dimensions should be taken into account for the set-ups of scanning that aim at extracting leaf geometry. The measurement set-ups suggested in  could beIn addition, it would be also recommended to test the relationship between intensity and angle of incidence for trees with flat leaves to study the multi scattering effect and/or to change the conditions of selection for the LSR set in point 2.3.2. In the future, a complete radiometric calibration should be set to guaranty the consistency of accuracy of the relationship.- The points with a low intensity have a higher probability to be ghost points because they are on the part of the leaves having a large angle of incidence. Those points could be directly deleted or corrected.- In the case where points with a low intensity constitute a large zone on the leaf, it is more difficult to determine whether they are ghost points or not. This zone appears larger than in the reality. In conclusion, closer is the point to the leaf border, higher is the probability that this one is a ghost point. In those cases, the points should be only corrected as their deletion would diminish the size of the leaf.Notwithstanding the aforementioned issues, different potential uses for the intensity can be envisaged. First, the third experiment emphasized the fact that intensity could help in determining the points having a higher probability to be a ghost point:Those two points could be used to eliminate ghost point and view as an improvement of the pre-processing methods for point cloud . In addAlternatively, using the difference between the angle of incidence provided by the LSR and comparing it to the incidence angle provided by the intensity for each point of the selected leaves (Δω) would give an estimation of the curving and the wrinkling of the leaves and this thanks to the Δω distribution. This could be used as a wrinkle indicator as the amplitude of those ones might be not large enough compared to the distance precision of the TLS. Finally, a promising future in the use of the intensity is given by its use as representing the normal of the surface of leaves.Finally, new opportunities exist to use the intensity to detect physiological aspects of the leaf such as the chlorophyll content with a LEICA ScanStation2 532 nm) . Lastly,2 nm . La5.We have investigated the properties of intensity in relation to distance and angle of incidence with leaf surfaces. The distance effect on intensity has been corrected to set a constituent relationship between the intensity and the angle of incidence. The variation of the intensity through angle of incidence seems to be a good indicator to help in the extraction of leaves geometries from TLS point cloud.Results show that one can expect a precision of ±5° to derive the angle of incidence from intensity data in the case of flat leaves. The results with curved leaves have clearly shown that the curvatures and the wrinkles are the reason for the degradation of the precision in the relation between the intensity and the angle of incidence. Therefore, we could expect to use the intensity to determine angle of incidence with a precision of ±5°.- Knowing the size, orientation and distance of the leaves, the scanning set-up can be improved for intensity and point cloud quality.- Intensity could help eliminating/correcting the ghost points, it may help to derive the surface of the leaves and it could be translate to an indicator (Δω distribution) helping to know the geometry of the leaves .Two general applications are emphasized in this study:Finally, the corrected intensity could be used to reduce the impact on the point cloud of the factors (i)–(vi) of the introduction.