Yufei Han
email: yufei.han@mines-paristech.fr

Fabien Moutarde
email: fabien.moutarde@mines-paristech.fr

Clustering Clustering Clustering Clustering and and and and M M M Modeling odeling odeling odeling of of of of Network Network Network Network level level level level Traffic Traffic Traffic Traffic States States States States based based based based on on on on Locality Locality Locality Locality Preservative Preservative Preservative Preservative Non-negative Non-negative Non-negative Non-negative Matrix Matrix Matrix Matrix Factorization Factorization Factorization Factorization

In this paper, we propose to cluster and model network-level traffic states based on a geometrical weighted similarity measure of network-level traffic states and locality preservative non-negative matrix factorization. The geometrical weighted similarity measure makes use of correlation between neighboring roads to describe spatial configurations of global traffic patterns. Based on it, we project original high-dimensional network-level traffic information into a feature space of much less dimensionality through the matrix factorization method. With the obtained low-dimensional representation of global traffic information, we can describe global traffic patterns and the evolution of global traffic states in a flexible way. The experiments prove validity of our method for the case of large-scale traffic network.

INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION

Most of previous studies on intelligent traffic focus on mining temporal patterns of traffic data measured on individual links [START_REF] Herring | Using mobile phones to forecast arterial traffic through statistical learning[END_REF][2] [START_REF] Kanoh | Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network[END_REF]. These works only analyze temporal properties of local link level traffic states. In fact, in a typical urban traffic scenario, traffic states of one link are correlated with neighboring areas. Network-level traffic states can be regarded as complementary knowledge and constraints in predicting or analyzing link level traffic patterns. Therefore, in recent years, with improvement of intelligent transportation systems, it becomes necessary to unveil global traffic patterns at network level. Global traffic information provides overall descriptions of spatial configurations of traffic states over the whole road network, which can improve performances of traffic guidance or control systems [START_REF] Kanoh | Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network[END_REF].

In large-scale traffic networks, like urban traffic systems, network level traffic information is often represented in a high-dimensional feature space, which makes it difficult to extract characteristics of global traffic states. In our work, we firstly adopt a geometrical weighted distance to evaluate similarity between network-level traffic patterns, which is described in the second section. Then, we make use of a matrix factorization method with a topological reguliarization term to obtain a low-dimensional representation model of global traffic states, as described in the third section. In a further step, we perform clustering of global traffic states based on the learned low dimensional representation, in order to extract typical spatial patterns of network-level traffic states. In the final part of the paper, we present clustering structures of network-level traffic patterns with respect to a large-scale link network and make conclusions of the whole paper.

GLOBAL GLOBAL GLOBAL GLOBAL TRAFFIC TRAFFIC TRAFFIC TRAFFIC STATUS STATUS STATUS STATUS SIMILARITY SIMILARITY SIMILARITY SIMILARITY MEASURE MEASURE MEASURE MEASURE

A network level traffic state is defined by a sequence of link level traffic states with respect to each individual link in the road network, which is normally represented in a n-dimensional vector, with n being the number of links in the network. Besides simply concatenating all link level traffic information into the high dimensional representation, network-level traffic states also contain spatial configurations of link level traffic states in the network, which form different global traffic state patterns. In a typical traffic network, the traffic state of one specific link is closely correlated with its up-stream or down-stream nearest neighbors in most cases. For example, in Figure .1, the links u i j and d i m are up-stream and down-steam nearest neighbors of the link i respectively. Assuming link i fell into a heavy traffic jam, its neighboring links u i j and d i m are more likely to be congested with vehicles than those which are located far from the link i and vice versa. Motivated by the property, we propose to adopt a weighted fusion scheme among traffic states with respect to geometrical neighborhoods of links during evaluating similarity between network level traffic states observed at different time. As we can see in Figure .1, we firstly calculate differences between traffic states of corresponding links. For each link i, we then obtain a weighted sum of the link-wise difference values with respect to the link i and its up-stream and down-stream neighbors, which is defined to be local variation v i of traffic states around the current link, as denoted in Figure .1 and Eq.1:

) ( ) ( ) ( i a w d a w u a w v i m m i d m j j i u j i + + = ∑ ∑ (1)
a is the link-wise difference between traffic states of the corresponding link. w j u , w m d and w i is the weights attached to up-stream neighbors, down-stream neighbors and the current link i respectively. After that, we map the L1 norm of { v i } into [0, 1] using a Gaussian kernel in Eq.2 as the final similarity measure between two network-level traffic states:

2 2 ) ( σ ∑ = -i i v e s (2)
To normalize range of the weighted sum, the sum of all weights is required to be 1.

The weight w i corresponding to the link i should be the largest one. Weights of the neighboring links can be designed to be proportional to degrees of traffic state correlation between one specific neighboring link and the current link i. In this paper, we just treat all neighboring links are of equal importance. Therefore, all neighboring links take the same weight value. By performing weighted fusion among local neighborhoods in the network, the derived similarity measure not only represents traffic state variations between corresponding links but also indicates the spatial correlations between local neighborhoods.

MODELING MODELING MODELING MODELING LOW-DIMENSIONAL LOW-DIMENSIONAL LOW-DIMENSIONAL LOW-DIMENSIONAL REPRESENTATION REPRESENTATION REPRESENTATION REPRESENTATION OF OF OF OF NETWORK-LEVEL NETWORK-LEVEL NETWORK-LEVEL NETWORK-LEVEL TRAFFIC TRAFFIC TRAFFIC TRAFFIC STATUS STATUS STATUS STATUS

Dimensionality of network-level traffic status representation is directly proportional to the number of links in the network. Given a large-scale network, which is common in applications of urban traffic control, the resultant high-dimensional traffic state representation is difficult to store or use for traffic prediction / classification due to the curse of dimensionality. To attack this issue, we propose to use locality preserving non-negative matrix factorization (LPNMF) [START_REF] Cai | Non-negative Matrix Factorization on Manifold[END_REF][5] to obtain low-dimensional representation of global traffic states. Assuming that k samples of n-dimensional global traffic states are stored as n*k matrix X, LPNMF factorize X into the non-negative n*s matrix M and s*k matrix V T , which minimizes the following objective function:

) ( 2 LV V Tr MV X O T F T λ + - = (3)
The first term is the Frobenius reconstruction error with respect to M and V. Each sample is approximated by a linear combination of the columns of M, weighted by the rows of V. Therefore, M can be regarded as containing a learned basis of the global traffic states, while V are s-dimensional representations of original samples in the given basis. s is set to be much less than the original dimensionality n most of time. Therefore, we actually obtain a much lower dimensional representation of network-level traffic state after factorization, which removes redundancy in the original high-dimensional space and makes it flexible to implement statistical analysis on the manifold V. In contrast with SVD decomposition, derived manifold space is not necessarily orthogonal in NMF. It is also required that each data sample takes positive coordinates in the low-dimensional feature space. The above two properties makes NMF more suitable to describe the latent distribution structures, especially when overlap exists among different clusters of data samples. In the second term of the object function, L is called Graph Laplacian [START_REF] Chung | Spectral Graph Theory[END_REF], defined as D-W. In the matrix W, w ij is the pair-wise geometrical weighted similarity measure matrix between the i-th and j-th k global traffic state sample. Due to symmetry of the distance measure, W is also a symmetric matrix. D is a diagonal matrix whose entries are column sums of W, defined as Eq.4:

∑ = j ij ii w D (4)
By adding the Graph Laplacian based constraints, the obtained low-dimensional representation V are calibrated to have similar topological structures as original samples X, which means that two close samples x i and x j should also be close in the low-dimensional manifold V. With this property, we can analyze global traffic states easily in the low-dimensional manifold V instead without loss of intrinsic data distribution of original samples X.

Each element v ij of matrix V represents to which degree the i-th original sample is associated with the j-th expanding basis in matrix M. If the i-th sample could be represented solely using the j-th basis, then v ij will take the largest value in the i-th row of V [START_REF] Xu | Document Clustering Based on Non-negative Matrix Factorization[END_REF]. Therefore, we simply use V to determine the cluster labels of the network-level traffic states. For each x i , we examine the i-th row of V and assign xi to the j-th cluster, j = arg max j v ij . Simply as it is, we can still find out the intrinsic distributional properties based on the NMF factorization.

EXPERIMENTAL EXPERIMENTAL EXPERIMENTAL EXPERIMENTAL RESULTS RESULTS RESULTS RESULTS

Experimental Experimental Experimental Experimental settings settings settings settings

To verify validity of the proposed method in clustering and modeling network-level traffic states, we firstly simulate real traffic scenes of the large-scale traffic network of Paris and its suburb regions using Metropolis [START_REF]Contribution to dynamic transportation models[END_REF], in order to generate a benchmark traffic database. Metropolis is a planning software [START_REF]Contribution to dynamic transportation models[END_REF] that is designed to model transportation systems. It contains a complete environment to handle dynamic simulations of daily traffic in one specific traffic network, which allows the user to study impacts of transportation management policies in a large-scale urban traffic network in a time-dependent manner. The built traffic database is composed by 4660 road intersections and 13627 links in the network, as we can see in the Figure .2. Each simulated traffic scene is generated to cover 8 hours of traffic data observations, including congestion in morning rush hours. Different traffic situations are obtained by adding random events and fluctuation in the O-D matrix (Origin-Destination) and capacity of network flow. There are totally 108 simulated traffic scenarios in our benchmark data set. Each one contains 48 time steps, corresponding to 15-minute bins over which the network traffic flow are aggregated. To represent traffic states, we propose to use traffic index [START_REF] Furtlehner | Spatial and Temporal Analysis of Traffic States on Large Scale Networks[END_REF] [START_REF]TRAVESTI Project Wiki[END_REF] in each link at a specific time, as in Eq.5.

] 1 , 0 [ 0 ∈ ∆ ∆ = lt l lt t t x (5)
The denominator is the observed travel time in link l at time t , the nominator is the free-flow travel time on this link. The smaller the traffic index is, the corresponding link is more congested. To perform clustering analysis, we concatenate all observations of traffic states into a 13627*5184 matrix. Each column corresponds to a network-level traffic status obtained at each time step, which is a 13627-dimensional vector. In the experiment, the number of centroids in clustering is set to be 3 and 5 respectively. For the convenience of visualization, we project all the column vectors into 3-dimensional PCA space to illustrate structures of the obtained clusters.

Figure.2 Traffic network of Paris and suburb regions

Results Results Results Results of of of of network-level network-level network-level network-level traffic traffic traffic traffic state state state state clustering clustering clustering clustering

In the 3D PCA space, as shown in three different viewpoints in Figure .3, the samples corresponding to the free-flowing network-level states are concentrated within a small region in the PCA space. By contrast, samples corresponding to network-level congestion are distributed sparsely and far from the region of the free-flowing state. Notably, with increasing degrees of traffic jam in the network, variations of network-level traffic patterns become larger and larger. In fact, spatial configurations of global traffic states keep the same if the whole network is free-flowing everywhere. On the contrary, congestion occurred at different parts of the network change the spatial configurations in different ways, which introduces variations in global traffic patterns. .5. This may be partly caused by existence of sparsely distributed data points that can't get stable cluster assignment. More importantly, it is actually smooth and continuous variations for network-level traffic status to evolve from the free-flowing state to congestion. There is no clear boundary between the two states. Therefore, cluster assignments are fuzzy for the samples of network-level status lying around the boundary between the two clusters. In a typical traffic scenario simulation, the whole traffic network is free-flow at the beginning. Subsequently, congestion emerges and becomes heavier and heavier until reaching the peak of traffic jam. Finally, network-level traffic states recover gradually. Therefore, network-level traffic states evolve in circular trajectories in the PCA space, as shown in Figure .7. Along the trajectories, transitions from the free-flow state to clusters corresponding to different network-level congestion patterns imply totally different temporal traffic dynamics in the network. To make it intuitive, we select two kinds of trajectories. Both start from the free-flowing state but achieve peaks of traffic congestions in the clusters labeled by pink and black legends respectively. Each trajectory is composed by observations of network-level traffic states at 48 time steps in our benchmark database. For each time step, we take the average traffic index value of all 13627 links in the network as a crude measure of global traffic state at the current time. The lower it is, the heavier congestion occurs in the network. Within each type of trajectories, we calculate average of the mean traffic index at each time step of each trajectory, which results in a 48-D sequence of average values as a general temporal dynamic pattern of global traffic states in the corresponding type of trajectories. In Figure .8, we compare both dynamic patterns. Trajectories with its peak of congestion located in the cluster labeled by black legends contain traffic jams with longer durations and heavier congestion at their peak points. These two different temporal evolution processe represent different settings of requirements and supplies of traffic resources in the network. Crude as it is, the analysis gives us a hint that we could analyse evolution of network-level traffic states based on the clustering results.

CONCLUSION CONCLUSION CONCLUSION CONCLUSION

In this paper, we propose a geometrical weighted similarity measure to evaluate both link-level differences of traffic states and spatial constraints between traffic states of neighboring incoming and out-coming links, which is important in describing spatial configurations of network-level states. Using it, we adopt a topology preservative non-negative matrix factoirzation method to obtain a low dimensional model of the high-dimensional network-level traffic states, which keeps the similarity information between original network-level traffic patterns. Thanks to the low-dimensional representation, we can describe or predict the network-level traffic states much more easily. In our work, we focus on clustering of the network-level states. Experimental results not only indicate characteristics of spatial configuration patterns with respect to traffic states in the whole network, but also denote promising applications of the low dimensional representation and clustering results in describing temporal dynamic patterns of the network-level traffic states.

Figure. 1

 1 Figure.1 Geometrical weighted similarity measure

Figure. 4

 4 illustrates the three clusters derived according to the proposed clustering algorithm. The cluster labeled by blue legends represents that almost all links are fluid in the network. Both red and dark green clusters indicate that traffic jam occurs in the network.Figure.4 also shows spatial configurations of the most congested network-level traffic states in each cluster, which have the least average value of traffic indices among the corresponding clusters. They are used here as representative exemplars of global traffic patterns contained in each cluster. In the exemplars, red color is used to label jammed links whose traffic indices are less than a specified threshold, while green color used for fluid links. It can be seen that the exemplar of the red cluster contains much less busy links than the dark green one. It denotes that network-level traffic states of the two clusters contain different degrees of traffic congestions. Traffic network states in the green cluster contain much heavier congestions. Furthermore, according to the exemplars, most of jammed links are concentrate in the central region of the network. It denotes that most traffic congestion occurs inside Paris. Suburb regions are free-flowing most of the times.

Figure. 3 A

 3 Figure.3 A three-views diagram of network-level traffic state representation in 3D PCA space

Figure. 4

 4 Figure.4 Three clusters and exemplars of network-level traffic states

Figure. 6

 6 Figure.6 Exemplars of sub-clusters. (a) and (b) are exemplars of sub-clusters labeled by pink and purple legends respectively. (c) and (d) are exemplars of sub-clusters labeled by black and light green legends respectively.

Figure. 7

 7 Figure.7 The evolution trajectory of network-level states

ACKNOWLEDGEMENT ACKNOWLEDGEMENT ACKNOWLEDGEMENT ACKNOWLEDGEMENT

This work was supported by the grant ANR-08-SYSC-017 from the French National Research Agency. The author specially thanks Cyril Furtlehner and Jean-Marc Lasgouttes for providing advice and the benchmark database used in this paper.

REFERENCE REFERENCE REFERENCE REFERENCE


Fabien Moutarde
email: fabien.moutarde@mines-paristech.fr

Yufei Han

A New Traffic-Mining Approach for Unveiling Typical Global Evolutions of Large-Scale Road Networks

In this paper, we present a new traffic-mining approach for automatic unveiling of typical global evolution of large-scale road networks. Our method uses as input a history of continuous traffic states (typically measured by travel times) of *all* links of the road graph. This historical data concatenated in a link/time matrix is then approximated with a locality-preserving Non-negative Matrix Factorization (NMF) method. The network-level traffic state similarity takes into account the graph topology by systematically combining link-wise comparisons with same measure on adjacent links. Based on the obtained matrix factorization, we project original high-dimensional network-level traffic information into a feature space (that of NMF components) of much lower dimensionality than original data. Importantly, because we use a modified NMF ensuring locality-preserving property (LP-NMF), the proximity of data-points in low-dim projected space correspond to proximity also in original high-dim space. We can therefore apply standard clustering methods easily in low-dim space, and directly deduce from its output pertinent categorization of global network traffic states and dynamics. Experimentations on simulated data with a large realistic network of more than 13000 links have been done, and show that our method allows to easily obtain meaningful partition of the attained global traffic states, and to deduce a categorization of the global daily evolution.

INTRODUCTION

The majority of published work on traffic data analysis and prediction focus on mining temporal patterns of traffic data measured on individual links [START_REF] Herring | Using mobile phones to forecast arterial traffic through statistical learning[END_REF][2] [START_REF] Kanoh | Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network[END_REF]. These works only analyze temporal properties of local link level traffic states. In fact, in a typical urban traffic scenario, traffic states of one link are correlated with neighboring areas. Network-level traffic states can be regarded as complementary knowledge and constraints in predicting or analyzing link level traffic patterns. Therefore, in recent years, with improvement of intelligent transportation systems, it becomes necessary to unveil global traffic patterns at network level. Global traffic information provides overall descriptions of spatial configurations of traffic states over the whole road network, which can improve performances of traffic guidance or control systems [START_REF] Kanoh | Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network[END_REF].

In large-scale traffic networks, like urban traffic systems, network level traffic information is often represented in a high-dimensional feature space, which makes it difficult to extract characteristics of global traffic states. In our work, we firstly adopt a geometrical weighted distance to evaluate similarity between network-level traffic patterns, which is described in the second section. Then, we make use of a matrix factorization method with topological regulation item to obtain a low-dimensional representation model of global traffic states, as described in the third section. In a further step, we perform clustering of global traffic states based on the learned low dimensional representation, in order to extract typical spatial patterns of network-level traffic states. In the final part of the paper, we present clustering structures of network-level traffic patterns with respect to a large-scale link network and make conclusions of the whole paper.

LOCALITY-PRESERVING NON-NEGATIVE MATRIX FACTORIZATION APPLIED TO TRAFFIC DATA

A network level traffic state is defined by a sequence of link level traffic states with respect to each individual link in the road network, which is normally represented in a n-dimensional vector, with n being the number of links in the network. Dimensionality of network-level traffic status representation is directly proportional to the number of links in the network. Given a large-scale network, which is common in applications of urban traffic control, the resultant high-dimensional traffic state representation is difficult to store or use for traffic prediction / classification due to the curse of dimensionality.

To attack this issue, we propose to use locality preserving non-negative matrix factorization (LP-NMF) [START_REF] Cai | Non-negative Matrix Factorization on Manifold[END_REF] [START_REF] Cai | Locality Preserving Nonnegative Matrix Factorization[END_REF] to obtain low-dimensional representation of global traffic states. Assuming that k samples of n-dimensional global traffic states are stored as n*k matrix X, LP-NMF factorizes X into the non-negative n*s matrix M and s*k matrix V T , which minimizes the following objective function:

) ( 2 LV V Tr MV X O T F T λ + - = (1)
The first term is the Frobenius reconstruction error with respect to M and V. Each sample is approximated by a linear combination of the columns of M, weighted by the rows of V. Therefore, M can be regarded as containing a learned basis of the global traffic states, while V concatenates s-dimensional representations of original samples in the given basis. The number s of NMF components is usually set to be much less than the original dimensionality n. Therefore, we actually obtain a much lower dimensional representation of network-level traffic state after factorization, which removes redundancy in the original high-dimensional space and makes it flexible to implement statistical analysis on the manifold V. In contrast with SVD decomposition, derived manifold space is not necessarily orthogonal in NMF. It is also required that each data sample takes positive coordinates in the low-dimensional feature space. The above two properties makes NMF more suitable to describe the latent distribution structures, especially when overlap exists among different clusters of data samples. In the second term of the object function, L is called Graph Laplacian [START_REF] Chung | Spectral Graph Theory[END_REF], defined as D-W. In the matrix W, w ij is the pair-wise geometrical weighted similarity measure matrix between i-th and j-th k global traffic state sample. Due to symmetry of the distance measure, W is a symmetric matrix.

In a typical traffic network, the traffic state of one specific link is closely correlated with its up-stream or down-stream nearest neighbors in most cases. Therefore, we use a similarity measure that takes into account the road graph topology, by merging together differences between traffic states of each link with link-wise difference values of its up-stream and down-stream neighbors. D is a diagonal matrix whose entries are column sums of W, defined as Eq.2:

∑ = j ij ii w D (2)
By adding the Graph Laplacian based constraints, the obtained low-dimensional representation V are calibrated to have similar topological structures as original samples X, which means that two close samples x i and x j are also close in the low-dimensional manifold V. With this property, we can analyze global traffic states easily in the low-dimensional manifold V instead of high-dim original space, without loss of intrinsic data distribution of original samples X.

Each element v ij of matrix V represents to which degree i-th original sample is associated with the j-th expanding basis in matrix M. If i-th sample could be represented solely using the j-th basis, then v ij would take the largest value in the i-th row of V [START_REF] Xu | Document Clustering Based on Non-negative Matrix Factorization[END_REF]. Therefore, we simply use V to determine the cluster labels of the network-level traffic states. For each x i , we examine the i-th row of V and assign xi to the j-th cluster, j = argmax

j v ij .

EXPERIMENTAL RESULTS

SIMULATED TRAFFIC DATA

The traffic-mining method we propose makes sense essentially for analyzing data of a large network with many streets/roads, such as city-wide or city+suburbs. Real-world traffic data of this type are not easily accessible, therefore we choose to first apply our approach on realistic simulated data. To this end, we use Metropolis simulation software [START_REF]Contribution to dynamic transportation models[END_REF], which was designed to model transportation systems. It contains a complete environment to handle dynamic simulations of daily traffic in one specific traffic network. It allows the user to study the impacts of transportation management policies in a large-scale urban traffic network in a time-dependent manner. We use Metropolis to build the benchmark traffic database of Paris and suburb regions, which is composed by 4660 road intersections and 13627 links in the network, as we can see in the Figure 1. Each simulated traffic scene is generated to cover 8 hours of observations, including congestion in morning rush hours. Different traffic situations are obtained by adding random events and fluctuation in the O-D matrix (Origin-Destination) and capacity of network flow. There are totally 108 simulated traffic scenarios in our benchmark data set. Each one contains 48 time steps, corresponding to 10-minute bins over which the network traffic flow is aggregated, covering 4:00 a.m. till noon. To represent traffic states in each link, we propose to use traffic index [9][10] in each link at a specific time, which is defined as follows in Eq.3.

] 1 , 0 [ 0 ∈ ∆ ∆ = lt l lt t t x (3)
The denominator is the observed travel time in link l at time t , the numerator is the free-flow travel time on this link. The smaller the traffic index is, the corresponding link is more congested. To perform clustering analysis, we concatenate all the observations of global traffic states into a 13627*5184 matrix. Each column corresponds to a network-level traffic status obtained at each time step, which is a 13627-dimensional vector. In the experiment, the number of clusters is set to be 3 and 5 respectively. For the convenience of visualization, we project all the samples into 3-dimensional PCA space to illustrate the structures of the obtained clusters.

NETWORK-LEVEL TRAFFIC-STATE CLUSTERING

In the 3D PCA space, as shown on Figure 2, the samples corresponding to the free-flowing network level states are concentrated within a small region in the PCA space. By contrast, samples corresponding to network-level congestion are distributed sparsely and far from the region of the free-flowing state. Notably, with increasing degrees of traffic jam in the network, variations of network-level traffic patterns become larger and larger. In fact, spatial configurations of global traffic states keep the same if the whole network is free-flowing everywhere. On the contrary, congestion occurred at different parts of the network change the spatial configurations in different ways, which introduces variations in global traffic patterns. We apply our locality-preserving NMF method to the data, setting the number s of learnt basis to be the desired number of clusters. Figure 3 illustrates, projected in 3D-PCA space, the partition of states obtained with three and five clusters. In the 3-clusters result, the one labeled by blue legends represents traffic states in which almost all links are fluid in the network, so we shall refer to this cluster as "Free-Flow cluster". Both red and green clusters include states with some degree of congestion in the network, respectively "Light congestion" and "Heavy congestion". When increasing the number of clusters to 5 instead of 3, we can find more detailed distribution structures of network-level traffic states, as shown on lower part of Figure 3. The "Light congestion" cluster (labeled with red on top of Figure .3), is further split into two sub-parts, labeled by pink and purple respectively. These two sub-clusters form elongated shapes oriented to different directions in 3D-PCA space, which implies different kinds of congestion distribution in the network. Exemplars of these two clusters make the difference more clear, as shown in the Figure 4, which illustrates exemplars of clusters following the same settings of color legends as in Figure 3. The exemplars show spatial configurations of the most congested network-level traffic states in each cluster, used here as representative exemplars of global traffic patterns contained in each cluster. In the exemplars, red color is used to label congested links (i.e. with traffic indices less than a specified threshold), while green color used for fluid links. In the exemplar of the sub-cluster labeled by pink legends (1 st sub-cluster of "light congestion"), illustrated in Figure 4(a), busy links tend to be more close to the central region than the exemplar of the sub-cluster labeled with purple legends (2 nd sub-cluster of "light congestion"), as shown in Figure 4(b). Despite of similar degrees of network-level congestion in both two exemplars, they indicate different spatial configurations of traffic states in the network, which is consistent with the difference of orientations of the two elongated sub-clusters. Similar split of the cluster can also be found in the cluster corresponding to heavy traffic congestion on top of Figure 3. As we can see on bottom of same figure, this cluster is split to two sub-clusters labeled green cross and black star. Due to large variations of spatial configurations of traffic congestions, both of two sub-clusters have sparse structures in the 3D-PCA space. However, they differ in degrees and spatial layout of network-level traffic congestion. In Figure 4(c) and (d), we compare the exemplars of the two sub-clusters labeled by black and green legends in Figure 3 respectively. Generally, the exemplar in Figure 4(d) contains more busy links. Furthermore, although the central region of the network is highly congested in both exemplars, the area to which network-level traffic congestion extend is wider in the exemplar shown in the Figure 4(d), especially in suburb regions. This implies a different setting of traffic scenarios in the simulation.

FROM STATE CLUSTERING TO CATEGORIZING DAILY EVOLUTION

In a typical setting of traffic scenario simulation, the whole traffic network is free-flowing at the beginning. Subsequently, traffic congestion emerges and becomes heavier and heavier until reaching the peak of traffic jam in rush hours. Finally, the network-level states return to free-flowing gradually. Therefore, network-level traffic states evolve in circular trajectories in the PCA space, as shown in Figure 5. Along the trajectories, transitions from the free-flow state to clusters corresponding to different types of network-level congestion patterns imply totally different temporal dynamic patterns of network-level traffic states. Comparing Figures 5 and3, it is clear that the partition of global traffic states obtained with our method also corresponds to a categorization of the daily evolution of traffic, with green, pink and purple sub-clusters each corresponding to a subset of the possible daily dynamics. In order to verify this, we select two kinds of daily trajectories: both start from the free-flowing state but reach peaks of traffic congestions in the clusters labeled by pink (one of the 2 "light congestion" sub-clusters) and black legends respectively. Each trajectory is composed by observations of network-level traffic states at 48 time steps in our benchmark database. For each time step, we take the mean traffic index value of all 13627 links in the network as a crude measure of global traffic state at the current time. The lower it is, the heavier congestion occurs in the network. Within each type of trajectories, we calculate average of the mean traffic index at each time step, which results in a 48-D sequence of average values as a general temporal dynamic pattern of global traffic states in the corresponding type of trajectories. Figure 6 compare the two dynamic patterns. As we can see, trajectories with its peak of congestion located in the cluster labeled by black legends contain traffic jams that have longer durations and heavier congestion at their peak points. These two different temporal evolution processes of network-level traffic states represent different settings of requirements and supplies of traffic resources in the network. Crude as it is, this analysis provides us a hint that we could find typical temporal patterns of network-level traffic patterns based on the clustering results. Examples of daily temporal evolutions, respectively for a day with peak congestion inside the "pink" sub-cluster and for another day with peak into "black sub-cluster"

CONCLUSION

In this paper, we have presented a new traffic-mining approach for automatic unveiling of typical global evolution of large-scale road networks. Our method is based on locality-preserving non-negative matrix factorization of the traffic historical data. We use the obtained matrix factorization to project original high-dimensional network-level traffic information into a feature space of much lower dimensionality, into which we can easily clusterize the global network states and dynamics. Our first experimentations, on simulated data with a large realistic network of more than 13000 links, indeed show that our method allows to obtain meaningful partition of the attained global traffic states, and to deduce a categorization of the global daily evolution.

Perspectives include application to real traffic data, and using the low dimensional representation and clustering results for modeling the temporal dynamic patterns of the network-level traffic states.

Figure 1 .

 1 Figure 1. Traffic network of Paris and suburb regions used in Metropolis simulation software

Figure 2 .

 2 Figure 2. Plot of network-level traffic state representation in 3D PCA space

Figure 3 .

 3 Figure 3. Visualization in 3D-PCA space of network-level traffic state clusters, obtained by associating one cluster with each component obtained by applying locality-preserving NMF: on upper plot 3 clusters with 3 components, and on lower plot 5 clusters with 5 components.

Figure 4 .

 4 Figure 4. Congestion patterns of the identified clusters: (a) and (b) on left are respective "most-congested exemplars" of the 2 pink/purple sub-clusters of "light congestion"; (c) and (d) on right are respective "most-congested exemplars" for black/green sub-clusters of "heavy congestion".

Figure 5 .

 5 Figure 5. The evolution trajectory of network-level traffic states, projected in 3D-PCA space

Figure 6 .

 6 Figure 6.Examples of daily temporal evolutions, respectively for a day with peak congestion inside the "pink" sub-cluster and for another day with peak into "black sub-cluster"

ACKNOWLEDGEMENT

This work was supported by the grant ANR-08-SYSC-017 from the French National Research Agency. The authors specially thank Cyril Furtlehner and Jean-Marc Lasgouttes for providing advice and the benchmark database used in this paper.


Ayet Shaiek
email: ayet.shaiek@mines-paristech.fr

Fabien Moutarde
email: fabien.moutarde@mines-paristech.fr

3D keypoint detectors and descriptors for 3D objects recognition with TOF camera

Keywords: 3D Keypoint Detector, 3D Keypoint Desciptor, Shape Index, Histogram Of Normals, Point Clouds, Time-Of-Flight Camera, Depth Image, 3D Objects Recognition

The goal of this work is to evaluate 3D keypoints detectors and descriptors, which could be used for quasi real time 3D object recognition. The work presented has three main objectives: extracting descriptors from real depth images, obtaining an accurate degree of invariance and robustness to scale and viewpoints, and maintaining the computation time as low as possible. Using a 3D time-of-flight (ToF) depth camera, we record a sequence for several objects at 3 different distances and from 5 viewpoints. 3D salient points are then extracted using 2 different curvatures-based detectors. For each point, two local surface descriptors are computed by combining the shape index histogram and the normalized histogram of angles between the normal of reference feature point and the normals of its neighbours. A comparison of the two detectors and descriptors was conducted on 4 different objects. Experimentations show that both detectors and descriptors are rather invariant to variations of scale and viewpoint. We also find that the new 3D keypoints detector proposed by us is more stable than a previously proposed Shape Index based detector.

INTRODUCTION

3D object recognition, an important research field, has been successfully studied in the case of a single viewpoint. Robustness to pose and viewpoint variations remains a challenging problem for objects recognition applications. Meanwhile, using new devices, such as time-off-light (TOF) 3D cameras, may be a step forward to provide robust geometric information about objects.

In this context, two components of an object recognition system are necessary: descriptions extraction phase where an interpretation of the image data is given, and the matching phase which consists of assigning an identity to the extracted descriptions.

The existing approaches for solving this issue can be classified in two ways:

-A group of 3D methods which suggest the use of the entire 3D model and base the recognition on the comparison of estimated model with reference models [START_REF] Johnson | Using spin images for efficient object recognition in cluttered 3Dscenes[END_REF] , and a group of 2D/3D approaches that project the 3D model into different 2D images [START_REF] Samir | Reconnaissance de Visages 3D Utilisant l'Analyse de Formes des Courbes Faciales[END_REF][START_REF] De Diego | Face verification with a kernel fusion method[END_REF] and compute 2D features. A survey of 3D and multi-modal 3D+ 2D approaches has been done in [START_REF] Bowyer | A survey of 3D and multi-modal 3D+ 2D face recognition[END_REF] .

-A class of global methods , like the work of [START_REF] Medioni | 3-D structures for generic object recognition[END_REF] which suggest to use volumetric part-based descriptions, and a class of local ones which describe local regions, as for example in [START_REF] Li | Multi-scale features for approximate alignment of point-based surfaces[END_REF] , and in [START_REF] Jones | Face recognition using boosted local features[END_REF] where Viola and Jones propose a set of "rectangle" features.

Local representations using keypoint have been proven to perform well in 2D recognition [START_REF] David | Object recognition from local scale-invariant features[END_REF] . Therefore, many recent researches have investigated in finding 3D detector and descriptor (eg: SIFT 2.5 [START_REF] Lo | SIFT keypoint descriptors for range image analysis[END_REF] ).

The proposed approach is in the line of Chen and Bhanu's work [START_REF] Chen | 3D free-form object recognition in range images using local surface patches[END_REF] who presented a local surface descriptor for 3D object recognition. In the work presented here, we use a depth camera "ZCam" (Figure 1) to capture four real objects (Figure 2) from different distances and viewpoints in order to characterize their 3D shape. In the following, we will focus on the 3D keypoint detection and the 3D descriptor extraction.

Figure 1. The time-of-flight (TOF) camera used produces depth video with the following principle: measure of time delay between infrared pulse emission and the reception of its reflection.

Figure 2. The range images of the four objects of our dataset.

METHODOLOGY 2.1 General scheme of the method

Pre-processing

Our methodology is the following: objects are placed on a turning tray which is pivoted to 5 positions (0°, 25°, 50°, 75° and 100°) in front of the camera during each record. Then, we repeat this for 3different distances of the camera to objects (at 50cm, 80cm and 110cm). The 3D camera produces 30 depth images/s, and the typical total recording time is 3.3 seconds, therefore, the total number of frames is fixed to 100 for each record. The camera's output is a depth grayscale image that we convert into a cloud of 3D points. The computation of the 3D points from the depth image is straightforward, as illustrated in Figure 3: for each pixel at line i and column j, we first compute (using the actual pixel width 0.0112 mm on the sensor), its (x,y) position in the focal plane; from this and the focal length f, we can deduce the normalized directing vector = ( , , ) ; the true 3D point (X,Y,Z) is then obtained as ( , , ) = . where the distance D depends directly on the grayscale value g of the depth image by = + .

; where are respectively the primary distance (i.e. minimal range distance) and the primary width (i.e. difference between maximal and minimal range distance), which can both be tuned manually on the camera.

One of the problems with TOF depth cameras is the rather high level of noise of the output data. The absolute precision of each depth pixel is ~1cm only (and quite dependant on the reflectance of the object material), and there can be an offset of absolute distance as high as 6cm, according to our tests. We partly overcome the noise problem by averaging the 100 frames recorded during 3s into one depth image that we crop after to remove outliers in boundaries.

Method

Once the 3D points cloud have been generated from the mean frame, we select salient 3D points before computing a 3D descriptor of regions around those detected points. Our method is based on differential geometry to describe the shape of objects. Particularly, we consider surface normals and curvatures which measure how the surface bends in different directions at one point.

In prectice, a uniform n x n lattice (grid) is used to sample the 2D cropped depth map (where n = 10). Then, each cell is subdivided into r x r sub-regions (r = 3). Using points belonging to this window, we fit a quadratic surfaces to the r x r patchs, of the form f(x,y) = ax 2 + by 2 + cxy + dx + ey + f, and estimate the parameters of the quadratic surface with the least square method. That allows us to compute differential geometry and extract the surface normal, Gaussian curvature and principal curvatures at each patch. Using a factor quality based on curvatures, we select feature points with the largest shape variation. Then, the shape index values are cumulated and the histogram of angles between the normal of reference feature point and that of its neighbouring regions is computed. Hence, our descriptor is the combination of the two histograms forming a 17+34=51 dimensional vector. To evaluate the proposed detector and descriptor, comparison with Chen's detector and descriptor in term of stability and invariance to the 5 viewpoints and the 3 scales of the same object has been done.

Keypoint detectors

The first detector is based on a keypoint quality measure introduced by Mian et al. [START_REF] Mian | On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes[END_REF] . After the sampling step (100 cells), we associate at each cell k a quality measure Q k is given by: where k 1 p and k 2 p are maximum and minimum principal curvatures, respectively. Summation, maximum and minimum values are calculated over the r x r sub-regions. Absolute values are taken so that positive and negative curvatures do not cancel each other; positive and negative values of curvatures are equally descriptive. Keypoints are ranked according to this measure and a threshold is chosen to select the best ones.

The second detector is the one proposed in [START_REF] Chen | 3D free-form object recognition in range images using local surface patches[END_REF] , and uses the shape index (I p ) for feature point extraction. It is a quantitative measure of the surface shape at a point p, and defined by Eq. ( 2)

1 / = ! - ! 3 4567 8 9 8 8 9 8 (2)
With this definition, all shapes are mapped into the interval [0, 1] 12 . Larger shape index values represent convex surfaces and smaller shape index values represent concave surfaces.

The central point is marked as a feature point if its shape index I p satisfies Eq. ( 3) within an r x r window I p = max of shape indexes and I p >= (1+:) * µ;

or I p = min of shape indexes and I p <= (1-; ; ; ;) * µ;

where µ is the mean of shape index over the l*l values and 0 <= : , ; ; ; ; <= 1

In Eq. (3) :, ; ; ; ; parameters control the selection of feature points.

Keypoint descriptors

Around each selected keypoint P, a local patch R is constituted of the r x r neighbours. For every point R i belonging to R, we compute its shape index value and the angle θ between the surface normals at the feature point P and N i . Then, we form, first, a 2D histogram by accumulating points in particular bins along the two axes based on Eq. ( 4) which relates the shape index value and the angle to the 2D histogram bin (h x , v y ). One axis of this histogram is the shape index which is in the range [0, 1]; the other is the cosine of the angle (cos θ) between the surface normal vectors at P and one of its neighbours in R. It is equal to the dot product of the two vectors and it is in the range [-1, 1]. In Eq.( 4), (h x , v y ) are the indexes along the horizontal and vertical axes respectively and (b x , b y ) are the number of bins along the horizontal and vertical axes, respectively.

ℎ = = > × @ ; A = (BCD E !)×F G (4)
This descriptor encodes the occurrence frequency of shape index values vs. the cosine of the angle between the normal of reference feature point and that of its neighbours.

The second proposed descriptor concatenates the histogram of the cosines of angles between normals and the histogram of the shape index into 1D vector. The unique axis of the histogram is composed of b x + b y bins. (cf. Figure 4).

EXPERIMENTAL RESULTS

We performed our experiments on our own database constituted of 4 objects (horse, car, dinosaur, man doll) captured with the TOF camera which has a spatial resolution of 320 x 240. The computation time of keypoint detection and description phases is about 2.5s for 50 keypoints. In the following, we propose to evaluate our detector and descriptor in terms of stability and descriptiveness.

Keypoint stability

In order to measure the repeatability of detected keypoints between different views/scales, we compute the distance of every keypoint in the rotated/scaled point cloud of view 1 to the nearest neighbor keypoint detected in view 2. Figure 5 illustrates the two plots of keypoint repeatability between the four initial views of the four objects and their respective scaled and rotated views with the quality factor based detector (FQD) and the shape index based detector (SID). The yaxis shows the percentage keypoints of the transformed views which could find a corresponding keypoint in the initial view within the distance shown on the x-axis. Results show that the percentage of keypoints repeatability is more important for the FQD than the SID and repeatability reaches 100% at a nearest neighbor error of ~7.5 mm for FQD and at ~10mm for SID. This result suggests that quality factor detector has slightly higher repeatability than the shape index detector. Figure 6 illustrates the relative stability of keypoint's positions when varying scale for the same object.

In addition to the keypoint detection role, the quality measure provides a means of selecting the best required keypoints.

A threshold is used to keep the keypoints with Q k greater than the threshold. Figure 7 shows keypoints detected on one view of horse at different cutoff thresholds of the quality Q k . Notice that as the threshold is decreased, more and more keypoints appear at less curved parts of the model.

Descriptor stability

We employ a rank distribution metric for evaluating the stability of a keypoint descriptor model [START_REF] Bosse | Keypoint design and evaluation for place recognition in 2D lidar maps[END_REF] . This measure gauges the probability of finding a matching descriptor in the set of k-nearest neighbors as a function of k. To compute the rank distribution, we fix our match set M of descriptors between descriptors in view 1 and those of view 2. Then, we consider every pair of descriptors (i, j) in the match set M, and count the number of descriptors k in descriptor set of view 1 such

that || i -k ||L2 < ||i -j ||L2.
Results are performed on the point cloud of the four objects at view angles 0°, 25°, 50° and 100° and at scales 50, 80 and 110.

After selecting n points and computing their descriptor in view 1, rotation of the n points is applied in order to recover their position in view 2 and compute their descriptor. Thus, the descriptor stability could be evaluated by counting the number of points in view 2 which the closest descriptor distances corresponds to the initial point in view 1.The same process is done for scale. We compute the positions of keypoints in scale 2 corresponding to the detected keypoint in scale 1 by applying a homothetic transformation. We plot the mean of curves representing the evolution of the number of correct matching keypoint descriptor as a function of the number of K nearest descriptors.

As illustrated in figure 8, according to this metric, the combined model slightly outperforms the concatenate one for most values of K, including small ones which are the most relevant.

Figure 8. Rank distribution for each of the two descriptor models (concatenate, combined). The plot shows the probability of finding the correct match for a descriptor within the group of the k nearest neighbors in the initial keypoint set. Right plot is a zoom on region for small K values.

Descriptiveness of the descriptors

In order to recognize one object appearing at different orientations or scales, the used descriptors must keep quite similar values whatever the considered view. Let H I be the vector formed with values J !,I , J ,I , J K,I , J L,I , where J >,I is the standard deviation of the jth descriptor values obtained for the ith object. To obtain close values for the same object, the mean value , H M of H I , must be minimized. In order to discriminate objects, the used descriptors must present quite different values to describe separate objects. Let N I be the vector formed with values O !,I , O ,I , O K,I , O L,I , where O >,I the mean value of the jth descriptor values obtained for the ith object. To discriminate objects, the standard deviation of N I , J P Q , must be maximized. Consequently, the quotient J P Q / H M , which have been used in [START_REF] Choksuriwong | A Comparative Study of Objects Invariant Descriptor[END_REF] , is a criterion which can characterize the descriptor performance.

Table 1 presents the concatenate and the combined descriptor performance regarding to the previously mentioned criterion for four objects of the database. This quotient is slightly more important when using the combined descriptor. The other criterion proposed to compare the distinctive power of the first representation with the second one is the coefficient of variation which is equal to the standard deviation divided by the mean 15 . The coefficient of variation encodes variability relatively to the mean and is used to compare the relative dispersion in one type of data with the relative dispersion in another type of data.

The diagrams of Figure 9 compare the dispersion of the two tested descriptors. We note that in high values of coefficient of variation the combined descriptor curve is above the concatenate descriptor curve, which supports the conclusion that the combined descriptor is more descriptive than the concatenated one.

One reason to explain this result is the resolution of histogram bins in the combined descriptor which is higher (17*34 = 578) than the concatenate descriptor one (17+34=51).Note that increasing too much this resolution will not necessary improve the results; the models will just fit more noise if each dimension is not supported by a reasonable portion of data points.

Figure 9. Average of the coefficient of variation for the two representations (concatenate and combined) for the four objects.

CONCLUSIONS AND PERSPECTIVES

We originally assumed that computing curvatures on the point would have allowed us to obtain stable 3D keypoints which describes parts of object with noticeable shape variation. Experimentations reported here showed this assumption as relevant. We initially believed that using the shape index would make it possible to encode the convexity and concavity of the surface. We therefore proceeded with our tests to confirm this assumption. It has often been suggested that combining shape index and angles of normals would make it possible to form an invariant descriptor. Trials carried out to test this assumption proved such to be the case.

The experiments presented here indicate higher stability of 3D keypoints selected with a new quality criteria based on curvature under viewpoint variation. Undergoing experiments also indicate higher stability under scale variations.

Regarding the descriptor, our analysis suggest that combined ShapeIndex-NormalAngles has more stability and descriptiveness than the concatenate version.

This new proposed 3D keypoint detector and the combined ShapeIndex-NormalAngles 3D descriptor, therefore, has the required properties to allow correct recognition of 3D objects whatever their pose, and distance, thus helping to provide semantic meaning to a complex scene. As we have seen, this result can be explained by the use of differential geometry which permits us to describe the local variation of the surface. It is also likely that if we try combining other 3D descriptors, descriptiveness will be improved. Nevertheless, attention should be paid to the computation time cost for best match searching that could be induced by high dimensionality of descriptor. Forthcoming investigations include tests of our approach on more objects, including public databases, and verification of the performance of these 3D keypoints as a tool for object recognition and categorization.

Figure 3 :

 3 Figure 3: Relation between true 3D point (X,Y,Z), and the (x,y) position of depth pixel in the focal plane.

  (100&) + |min (100&)| + max(10 . / ! ) + |min (10. / )| ; & = . / ! . / (1)

  Figure 4. (a) Concatenate descriptor, (b) Combined descriptor

Figure 5 .Figure 6 .

 56 Figure 5. Keypoint identification repeatability between different scales and views for the two detectors: quality factor based detector (FQD) and shape index based detector (SID).

Figure 7 .

 7 Figure 7. (a)Pointcloud of the horse, Q k > 0, KpNb (the number of keypoints) = 53, (b) For Q k > 300, KpNb = 44 (c) For Q k > 7000, KpNb = 21 (d) Q k > 17000, KpNb = 11

Table 1 .

 1 Descriptor performance

		Concatenate descriptor	Combined descriptor
	Quotient (J P Q / H M )	5.4148e-005	6.3784e-005


Anita ; Sainsbury

David S Sanders

Dr Alexander Ford

D Floor

Keywords: Coeliac Disease and Hypertransaminasaemia Coeliac Disease and Hypertransaminasaemia Coeliac disease < Disease-based, Liver function tests < Hepatology, Meta-analyses < Topics, X Coeliac Disease and Hypertransaminasaemia EMA endomysial antibody Ig immunoglobulin tTG tissue transglutaminase Systematic review Meta-analysis Coeliac disease Hypertransaminasaemia

published or not. The documents may come

INTRODUCTION

Coeliac disease is a chronic immune-mediated enteropathy, characterised by T cell sensitisation to gluten in genetically predisposed individuals. [START_REF] Marsh | Clinical and pathological spectrum of coeliac disease[END_REF] The disorder is common, with a population prevalence of around 1% in genetically susceptible populations. [START_REF] Sanders | A primary care cross-sectional study of undiagnosed adult coeliac disease[END_REF][START_REF] Mustalahti | The prevalence of celiac disease in Europe: results of a centralized, international mass screening project[END_REF][START_REF] Bahari | Prevalence of celiac disease among blood donors in Sistan and Balouchestan Province, Southeastern Iran[END_REF][START_REF] Katz | Screening for celiac disease in a North American population: sequential serology and gastrointestinal symptoms[END_REF] The spectrum of presentation ranges from asymptomatic disease detected incidentally to severe intestinal malabsorption, with complications that may include osteoporosis, iron deficiency anaemia, and infertility. [START_REF] Sanders | Changing face of adult coeliac disease: experience of a single university hospital in south Yorkshire[END_REF] A number of hepatic abnormalities have been described in association with coeliac disease. These include auto-immune hepatitis, primary biliary cirrhosis, and primary sclerosing cholangitis. [START_REF] Sjöberg | Frequent occurrence of non-specific gliadin antibodies in chronic liver disease. Endomysial but not gliadin antibodies predict coeliac disease in patients with chronic liver disease[END_REF] For over three decades, an isolated elevation of hepatic transaminases has also been noted in coeliac disease, often as a presenting feature of the condition. [START_REF] Hagander | Hepatic injury in adult coeliac disease[END_REF][START_REF] Lindberg | Liver damage in coeliac disease or other food intolerance in childhood[END_REF] Such cases have been associated with a mild non-specific hepatitis on liver biopsy. [START_REF] Hagander | Hepatic injury in adult coeliac disease[END_REF] This so-called 'coeliac hepatitis' [START_REF] Volta | Liver dysfunction in celiac disease[END_REF] may normalise on gluten withdrawal, [START_REF] Hagander | Hepatic injury in adult coeliac disease[END_REF] but the pathogenesis of liver injury remains obscure. It has been suggested that the metabolic sequelae of malnutrition in coeliac disease lead to hepatic dysfunction and steatosis. [START_REF] Naschitz | Massive hepatic steatosis complicating adult celiac disease: report of a case and review of the literature[END_REF] However, coeliac disease presenting with malnutrition is now uncommon.

Intestinal mucosal permeability is increased in coeliac disease, [START_REF] Clemente | Early effects of gliadin on enterocyte intracellular signalling involved in intestinal barrier function[END_REF] and it has been hypothesised that this facilitates absorption of antigens and cytokines directly into the hepatic portal venous system, leading to hepatic inflammation. [START_REF] Volta | Coeliac disease hidden by cryptogenic hypertransaminasaemia[END_REF] Despite this, severity of intestinal histological changes does not appear to correlate with transaminase levels. [START_REF] Bardella | Prevalence of hypertransaminasaemia in adult celiac patients and effect of gluten-free diet[END_REF] Furthermore, hepatic involvement is not a feature of tropical sprue, a condition which produces a similar histological picture to coeliac disease. [START_REF] Floch | The structural and functional status of the liver in tropical sprue[END_REF] More recently, it has been suggested that tissue transglutaminase (tTG), the target antigen recognised by anti-endomysial antibody (EMA), may play a role in hepatic dysfunction. [START_REF] Peláez-Luna | Intestinal involvement is not sufficient to explain hypertransaminasemia in celiac disease?[END_REF] This is supported by the finding of extracellular immunoglobulin (Ig) A-class tTG antibodies in liver biopsy specimens from patients with coeliac hepatitis. [START_REF] Korponay-Szabó | In vivo targeting of intestinal and extraintestinal transglutaminase 2 by coeliac autoantibodies[END_REF] Despite the well-recognised association between coeliac disease and hepatobiliary disorders, screening for coeliac disease in patients with deranged liver biochemistry is not advocated routinely. [START_REF] Cobbold | Investigating mildly abnormal serum aminotransferase values[END_REF] Furthermore, studies investigating the prevalence of coeliac disease in patients with cryptogenic hypertransaminasaemia have reported conflicting results. To date, there has been no systematic evaluation of the relationship between hypertransaminasaemia and coeliac disease. We therefore conducted a systematic review and meta-analysis to determine, firstly, the prevalence of coeliac disease in adults presenting with cryptogenic hypertransaminasaemia, and secondly, the prevalence of hypertransaminasaemia in adults presenting with newlydiagnosed coeliac disease.

METHODS

Search Strategy and Study Selection

A search of the medical literature was conducted using MEDLINE (1950 to 1 st August 2010) and EMBASE (1980 to 1 st August 2010) to identify case series and casecontrol studies that recruited unselected adults (over 90% of participants aged over 16 years) with either cryptogenic hypertransaminasaemia that applied serological tests for coeliac disease and/or distal duodenal biopsy to participants, or with newly-diagnosed biopsy-proven coeliac disease that assessed serum transaminases.

Hypertransaminasaemia was defined as any value above the upper limit of normal for the individual investigating laboratory. IgA-class anti-EMAs and tTG were considered valid serological markers of coeliac disease. Studies were only eligible for inclusion if they contained 90 or more individuals, because of a priori concerns about statistical handling of rare events. First or senior authors of studies were contacted to provide additional information on studies where required.

Potentially eligible studies were identified using the following medical subject headings and free text terms: coeliac, celiac, sprue, gluten sensitive enteropathy, villous atrophy, antigliadin, endomyseal, tissue transglutaminase, or duodenal biopsy. These were combined using the set operator 'AND' with studies identified with the following terms: transaminase, transaminitis, hypertransaminasemia, hypertransaminasaemia, alanine aminotransferase, or aspartate aminotransferase.

There were no language restrictions. All abstracts of the identified papers were evaluated, and potentially relevant papers were obtained and evaluated independently by two investigators (AS and ACF) according to the prospectively defined eligibility criteria, using pre-designed eligibility forms. Any disagreement between investigators

Data Extraction

Data were extracted independently by two reviewers (AS and ACF) onto a Microsoft Excel spreadsheet (XP Professional Edition; Microsoft Corp, Redmond, WA, USA), and discrepancies were resolved by consensus. The following data were collected for each study: type of study, year(s) conducted, country, setting (primary, secondary, or tertiary care), number of centres, total number of subjects recruited, mean age of subjects, and proportion of male subjects. In studies investigating subjects with cryptogenic hypertransaminasaemia, the number of individuals testing positive using either serum anti-EMA or tTG, or having biopsy-proven coeliac disease, were expressed as a proportion of the total number of subjects with cryptogenic hypertransaminasaemia. In studies reporting serum transaminase levels in subjects with newly-diagnosed biopsy-proven coeliac disease, the number of individuals with abnormal serum transaminases was expressed as a proportion of the total number of subjects with newly-diagnosed coeliac disease. For the latter group of studies we also extracted the proportion of individuals whose serum transaminases normalised following the institution of a gluten-free diet, where individual studies reported these data.

Data synthesis and statistical analysis

In studies investigating subjects with cryptogenic hypertransaminasaemia, the proportion of individuals testing positive using serum anti-EMA or tTG, or having biopsy-proven coeliac disease, were combined for all studies to give a pooled prevalence of positive coeliac serology, or biopsy-proven coeliac disease, in cryptogenic hypertransaminasaemia, with 95% confidence intervals (CI). In studies reporting serum transaminase levels in subjects with newly-diagnosed biopsy-proven coeliac disease, the proportion of individuals with abnormal serum transaminases were combined for all studies to give a pooled prevalence of hypertransaminasaemia in newly-diagnosed coeliac disease with 95% CIs. Data were pooled using a random effects model, to give a more conservative estimate of the prevalence of positive coeliac serology or biopsy-proven coeliac disease in cryptogenic hypertransaminasaemia, and the prevalence of hypertransaminasaemia in newly-diagnosed coeliac disease. [START_REF] Dersimonian | Meta-analysis in clinical trials[END_REF] Heterogeneity between studies was assessed using the I 2 statistic with a cut off of 50%, [START_REF] Higgins | Measuring inconsistency in meta-analyses[END_REF] and the χ 2 test with a P value < 0.10, used to define a statistically significant degree of heterogeneity. StatsDirect version 2.7.2 (StatsDirect Ltd, Sale, Cheshire, England) was used to generate Forest plots of pooled prevalences with 95% CIs.

RESULTS

The search identified 2705 citations. Of these, 30 studies appeared relevant and were retrieved for evaluation (Figure 1). Twelve studies were deemed eligible for inclusion initially, with excellent agreement between investigators (kappa = 0.86). [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Carroccio | Screening for celiac disease in patients with chronic liver disease[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF][START_REF] Bardella | Prevalence of hypertransaminasemia in adult celiac patients and effect of gluten-free diet[END_REF][START_REF] Dickey | Liver abnormalities associated with celiac sprue: How common are they, what is their significance, and what do we do about them?[END_REF][START_REF] Novacek | Prevalence and clinical importance of hypertransaminasaemia in coeliac disease[END_REF][START_REF] Foley | Frequency of elevated ALT in untreated coeliac disease and the impact of compliance with gluten free diet on ALT normalisation[END_REF][START_REF] Lewis | Prevalence and consequence of hypertransaminasaemia in incident coeliac disease: How common is it and does it matter?[END_REF][START_REF] Bardella | Gluten intolerance: Gender-and agerelated differences in symptoms[END_REF] After contacting one author for clarification of study methodology, 1 of the 12 studies was subsequently excluded from the meta-analysis. [START_REF] Bardella | Gluten intolerance: Gender-and agerelated differences in symptoms[END_REF] This study only reported the proportion of subjects in whom hypertransaminasaemia was the principal presenting feature of coeliac disease; the overall prevalence of hypertransaminasaemia in the study cohort was not reported. This left 11 studies eligible for inclusion. [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Carroccio | Screening for celiac disease in patients with chronic liver disease[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF][START_REF] Bardella | Prevalence of hypertransaminasemia in adult celiac patients and effect of gluten-free diet[END_REF][START_REF] Dickey | Liver abnormalities associated with celiac sprue: How common are they, what is their significance, and what do we do about them?[END_REF][START_REF] Novacek | Prevalence and clinical importance of hypertransaminasaemia in coeliac disease[END_REF][START_REF] Foley | Frequency of elevated ALT in untreated coeliac disease and the impact of compliance with gluten free diet on ALT normalisation[END_REF][START_REF] Lewis | Prevalence and consequence of hypertransaminasaemia in incident coeliac disease: How common is it and does it matter?[END_REF]

Yield of Serologic Testing for Coeliac Disease in Subjects with Cryptogenic

Hypertransaminasaemia

Six studies, involving a total of 919 patients, reported the yield of testing for coeliac disease, using either serum anti-EMA or tTG, in patients with cryptogenic hypertransaminasaemia. [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Carroccio | Screening for celiac disease in patients with chronic liver disease[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF] Detailed characteristics of individual studies are provided in Table 1. Two of the studies utilised a case-control design, employing blood donors, [START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF] and patients with chronic liver disease, [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF] as control participants. As these control subjects were not felt to be representative of the general population we did not extract their data for comparison. The remaining four studies were case series.

The proportion of subjects with cryptogenic hypertransaminasaemia with positive coeliac serology ranged from 1.6% to 11.9% in individual studies. The pooled prevalence of positive coeliac serology in patients presenting with cryptogenic hypertransaminasaemia was 5.9% (95% CI 2.7% to 10.3%), with statistically significant heterogeneity between studies (I 2 = 83.3%, P < 0.001) (Figure 2). Five of the six studies excluded other causes of abnormal serum transaminases prior to screening for coeliac disease, with a non-invasive liver screen for viral hepatitis, haemochromatosis and auto-immune liver disease. [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF] Four studies also screened for Wilson's disease and alpha-1 anti-trypsin deficiency. [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF] When only the five studies that had performed a non-invasive liver screen were analysed, the pooled prevalence of positive coeliac serology in cryptogenic hypertransaminasaemia increased to 6.4% (95% CI 2.6% to 11.7%), though significant heterogeneity between studies remained (I 2 = 86.3%, P < 0.001).

Yield of Distal Duodenal Biopsy Following Positive Coeliac Serology in Subjects with Cryptogenic Hypertransaminasaemia

In all six studies that applied serological testing for coeliac disease to subjects with cryptogenic hypertransaminasaemia, [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Carroccio | Screening for celiac disease in patients with chronic liver disease[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF] individuals with a positive serological test for coeliac disease were offered distal duodenal biopsy for histological confirmation of coeliac disease. Of the 54 patients with positive coeliac serology, 52 (96.3%) agreed to undergo endoscopy with distal duodenal biopsy. The proportion of subjects with cryptogenic hypertransaminasaemia with biopsy-proven coeliac disease ranged from 0.8% to 9.1% in individual studies. The pooled prevalence of biopsyproven coeliac disease in cryptogenic hypertransaminasaemia in these six studies was 3.6% (95% CI 1.4% to 7.0%), with statistically significant heterogeneity between studies (I 2 = 80.9%, P < 0.001) (Figure 3). When only the five studies that excluded other causes of abnormal serum transaminases prior to testing for coeliac disease were included in the analysis, [START_REF] Bardella | Chronic unexplained hypertransaminasemia may be caused by occult celiac disease[END_REF][START_REF] Mugica | Prevalence of coeliac disease in unexplained chronic hypertransaminasemia[END_REF][START_REF] Soresi | Screening for autoantibodies to tissue transglutaminase reveals a low prevalence of celiac disease in blood donors with cryptogenic hypertransaminasemia[END_REF][START_REF] Volta | Coeliac disease in patients with hypertransaminasaemia of unknown origin[END_REF][START_REF] Lo Iacono | Anti-tissue transglutaminase antibodies in patients with abnormal liver tests: is it always coeliac disease?[END_REF] the pooled prevalence of biopsy-proven coeliac disease in cryptogenic hypertransaminasaemia increased to 4.1% (95% CI 1.4% to

Prevalence of Abnormal Serum Transaminases in Patients with Newly-

Diagnosed Coeliac Disease

Five studies, involving a total of 1136 patients, reported the prevalence of abnormal serum transaminases in newly-diagnosed coeliac disease. [START_REF] Bardella | Prevalence of hypertransaminasemia in adult celiac patients and effect of gluten-free diet[END_REF][START_REF] Dickey | Liver abnormalities associated with celiac sprue: How common are they, what is their significance, and what do we do about them?[END_REF][START_REF] Novacek | Prevalence and clinical importance of hypertransaminasaemia in coeliac disease[END_REF][START_REF] Foley | Frequency of elevated ALT in untreated coeliac disease and the impact of compliance with gluten free diet on ALT normalisation[END_REF][START_REF] Lewis | Prevalence and consequence of hypertransaminasaemia in incident coeliac disease: How common is it and does it matter?[END_REF] All studies were of case series type. Detailed characteristics of individual studies are provided in Table 2. The prevalence of abnormal serum transaminases in newly-diagnosed coeliac disease ranged from 10.5% to 42.4%. The pooled prevalence of abnormal serum transaminases in newly-diagnosed coeliac disease was 27.0% (95% CI 13.0% to 44.0%), with statistically significant heterogeneity between studies (I 2 = 96.9%, P < 0.001) (Figure 4).

All five studies reported the degree of improvement in abnormal serum transaminases with institution of a gluten-free diet within 1 year. In four of the studies, exclusion of gluten led to normalisation of serum transaminase levels in between 63% and 90% of participants. [START_REF] Bardella | Prevalence of hypertransaminasemia in adult celiac patients and effect of gluten-free diet[END_REF][START_REF] Dickey | Liver abnormalities associated with celiac sprue: How common are they, what is their significance, and what do we do about them?[END_REF][START_REF] Novacek | Prevalence and clinical importance of hypertransaminasaemia in coeliac disease[END_REF][START_REF] Foley | Frequency of elevated ALT in untreated coeliac disease and the impact of compliance with gluten free diet on ALT normalisation[END_REF] The fifth study reported statistically significant reductions in mean transaminases levels with gluten withdrawal.

DISCUSSION

The current meta-analysis has demonstrated a pooled prevalence of biopsyproven coeliac disease of between 3.6% and 4.1% in patients found to have cryptogenic hypertransaminasaemia. This equates to an approximately four-fold risk of coeliac disease, compared to the background population risk of around 1%. [START_REF] Dubé | The prevalence of celiac disease in average-risk and at-risk Western European populations: a systematic review[END_REF] It is conceivable that growing numbers of patients with coeliac disease will be identified in this way, given that the classical presentation of malabsorption and failure to thrive in childhood is increasingly being replaced by more subtle gastrointestinal symptoms, or incidental abnormalities found on routine blood testing. [START_REF] Lo | Changing presentation of adult celiac disease[END_REF] Individuals identified with coeliac disease during the investigation of hypertransaminasaemia are presumably detected at an early stage in the course of the condition. [START_REF] Volta | Coeliac disease hidden by cryptogenic hypertransaminasaemia[END_REF] It has been suggested that timely diagnosis may be advantageous, in that it may halt or even reverse progression to chronic liver disease. [START_REF] Hagander | Hepatic injury in adult coeliac disease[END_REF] The complications of coeliac disease may also be reduced, including osteoporosis [START_REF] Mcfarlane | Osteoporosis in treated adult coeliac disease[END_REF] and infertility. [START_REF] Collin | Infertility in coeliac disease[END_REF] These data also demonstrate that over 20% of individuals with newlydiagnosed coeliac disease may have elevated serum transaminases at presentation. All of the included studies reported either improvement in, or normalisation of, serum transaminase levels in the majority of participants following dietary exclusion of gluten. Increased awareness of this phenomenon will enable clinicians to reserve invasive investigations for individuals in whom deranged biochemistry persist despite adherence to a strict gluten-free diet.

The strengths of our study include a rigorous search strategy and extensive literature search, the judging of study eligibility and extraction of data in duplicate, and pooling of data to allow synthesis of all the available published evidence. Limitations of the current study relate to those of the available studies; 8 out of 12 studies were based in tertiary care, which may reduce the applicability of the results to the general population. [START_REF] Layde | Generalizability of clinical studies conducted at tertiary care medical centers: a population-based analysis[END_REF] The Italian population was disproportionately represented, with 7 out of the 12 studies conducted in Italy, compared to only 2 in the UK. The prevalence of coeliac disease in Italy may be somewhat lower than in the UK. [START_REF] Catassi | High prevalence of undiagnosed coeliac disease in 5280 Italian students screened by antigliadin antibodies[END_REF] Moreover, robust studies comparing the sensitivity and specificity of coeliac serological tests in different populations are lacking. There was heterogeneity between study results in all of our analyses, which we were unable to uncover reasons for due to the relatively small number of studies available. Finally, in the six studies investigating cryptogenic hypertransaminasaemia, body mass index (BMI) was not included in the panel of liver screening tests. As obesity is a common cause for deranged serum transaminases in the western world, our meta-analysis may have underestimated the true prevalence of coeliac disease in true 'cryptogenic' hypertransaminasaemia.

Consideration should be given to coeliac serology testing in the investigation of deranged liver biochemistry, and the term 'cryptogenic hypertransaminasaemia' should be reserved for those in whom a comprehensive non-invasive liver screen has included testing for coeliac disease. However, clinicians should also be aware of the possibility of false positive serological tests for coeliac disease in patients with chronic liver disease, [START_REF] Carroccio | Screening for celiac disease in patients with chronic liver disease[END_REF] underlining the importance of confirmatory distal duodenal biopsy in these patients. A non-invasive liver screen for abnormal serum transaminases frequently includes tests such as serum caeruloplasmin, although the diagnostic yield is low and Wilson's disease is a rare condition.

Should we as clinicians also consider including coeliac serology as part of the initial non-invasive liver screen? Our data suggests that this may be worthwhile, but the cost-effectiveness of this approach has not been studied to date. Until this issue is examined, these data suggest that routine testing for coeliac disease in patients with cryptogenic hypertransaminasaemia may be a worthwhile strategy. * The other two patients testing positive for tTG in this study had co-existent chronic hepatitis C infection, and duodenal biopsy was normal histologically. † 121 patients were ultimately diagnosed with non-alcoholic fatty liver disease, and four of these had biopsy-proven coeliac disease. 13

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Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

21,22,23

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency.

11

DISCUSSION

Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

12

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Limitations 25 Discuss limitations at study and outcomelevel (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

13 Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research.

12,13 FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.

14

  ), though significant heterogeneity between studies remained (I 2 = 83.8%, P < 0.001).

31

 31

FIGURES Figure 1. Flow Diagram of Assessment of Studies Identified in the Systematic Review and Meta-analysis.

	Studies identified in literature
	search (n = 2705)
		Excluded (title and abstract revealed
	o r F o r F o r F o r F o r F o r F o r F o r		not appropriate) (n = 2675)
	P P P P P P P P
	e e r e e r e e r e e r e e r e e r e e r Studies retrieved for evaluation e (n = 30) e r		Excluded (n = 19) because:
	R e v i e R e v i e R e v i e R e v i e R e v i e R e v i e R e v i e R e v i e	• • • • •	Less than 90 subjects = 9 Subjects did not have cryptogenic hypertransaminasaemia = 4 Did not report serum transaminase levels = 3 Serum transaminase data not extractable = 2 Review article = 1
	w w w w w w w w
	Studies eligible for inclusion
	(n = 11):
	Reported prevalence of positive
	coeliac serology or biopsy-
	proven coeliac disease in
	cryptogenic
	hypertransaminasaemia (n = 5)
	Reported prevalence of
	abnormal serum transaminases
	in newly-diagnosed coeliac
	disease (n = 6)

Figure 2. Forest Plot of the Pooled Prevalence of Positive Coeliac Serology in Cryptogenic Hypertransaminasaemia.

			Proportion meta-analysis plot [random effects]
	Bardella 1999			0.093 (0.050, 0.154)
	Mugica 2001			0.027 (0.007, 0.068)
	Soresi 2001			0.016 (0.004, 0.039)
	Carrocio 2003 Volta 2001	F o r	0.031 (0.006, 0.089) 0.091 (0.044, 0.161)
	Lo Iacono 2005		P	0.119 (0.074, 0.178)
	combined		e	0.059 (0.027, 0.103)
	0.00	0.05 proportion (95% confidence interval) 0.10 0.15 e r	0.20
			R
			e
			v i e
			w

Figure 3. Forest Plot of the Pooled Prevalence of Biospy-proven Coeliac Disease in Cryptogenic Hypertransaminasaemia.

			Proportion meta-analysis plot [random effects]
	Bardella 1999			0.0857 (0.0451, 0.1449)
	Mugica 2001			0.0136 (0.0017, 0.0483)
	Soresi 2001			0.0078 (0.0009, 0.0277)
	Carrocio 2003 Volta 2001	F o r	0.0104 (0.0003, 0.0567) 0.0909 (0.0445, 0.1608)
	Lo Iacono 2005		P	0.0357 (0.0132, 0.0761)
	combined		e	0.0364 (0.0136, 0.0696)
	0.00	0.05 proportion (95% confidence interval) 0.10 0.15 e r	0.20
			R
			e
			v i e
			w

Figure 4. Forest Plot of the Pooled Prevalence of Abnormal Serum Transaminases in Newly-Diagnosed Coeliac Disease.

			Proportion meta-analysis plot [random effects]
	combined Lewis 2009 Foley 2009 Novacek 1999 Dickey 1995 Bardella 1995	0.0	0.2 proportion (95% confidence interval) 0.4 F o r P e e r R e v i e w 0.6 0.27 (0.13, 0.44) 0.10 (0.08, 0.13) 0.34 (0.25, 0.45) 0.40 (0.33, 0.48) 0.15 (0.09, 0.22) 0.42 (0.35, 0.51)

Table 1 . Studies Reporting the Prevalence of Positive Coeliac Serology or Biopsy-proven Coeliac Disease in Patients with Cryptogenic Hypertransaminasaemia.

 1

	Study	Country, setting,	Other causes of abnormal	Tests for	Number of	Number with	Number with
		and number of	serum transaminases	coeliac	participants (%	positive coeliac	biopsy-proven
		centres	excluded prior to coeliac	disease	male)	serology (%)	coeliac disease
			testing?	applied			(%)
	Bardella	Italy, tertiary	Yes	EMA,	140 (66.4)	13 (9.3)	12 (8.6)
	1999 21	care, 3 sites		duodenal
				biopsy
	Mugica	Spain, secondary	Yes	EMA,	147 (57.8)	4 (2.7)	2 (1.4)
	2001 22	and tertiary care,		duodenal
		4 sites		biopsy
	Soresi	Italy, primary	Yes	tTG and EMA,	258 (63.6)	4 (1.6)	2 (0.8)
	2001 23	care, 1 site		duodenal
				biopsy

Table 2 . Studies Reporting the Prevalence of Abnormal Serum Transaminases in Newly-diagnosed Coeliac Disease.

 2 Sainsbury et al.28 of 28 † The study reported statistically significant reductions in mean transaminases levels with gluten withdrawal, but not absolute number of patients whose transaminases improved.Risk of bias in individual studies12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.9,20Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. 20 Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12).

	Study	Country, setting,	Number of	Number with abnormal serum	Number improving with
		and number of	participants (% male)	transaminases (%)	institution of gluten-free diet (%)
		centres
	Bardella	Italy, tertiary care, number of	158 (19.6)	67 (42.4)	60 (89.6)
	1995 27	sites not reported
	Dickey	Northern Ireland, secondary	129 (32.0)	19 (14.7)	15 (78.9)
	1999 28	and tertiary care, 4 sites
	Novacek	Austria, tertiary care, 1 site	178 (27.0)	72 (40.4)	64 (88.9)
	1999 29
	Foley	Australia, tertiary care, 1 site	99 (24.0)	34 (34.3)	12/19* (63.2)
	2009 30
	Lewis	United Kingdom, tertiary	572 (not reported)	60 (10.5)	Not reported †
	2009 31	care, 2 sites
	* Only the initial 53 study participants were followed up regularly for 12 months after gluten withdrawal, of whom 19 had abnormal liver
	biochemistry.

  9,10,11,21,22,23 Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 13 Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

ACKNOWLEDGEMENTS

We are grateful to Dr MT Bardella for providing us with supplementary information regarding her study.

STATEMENTS OF INTEREST

Authors' Declaration of Personal Interests:

Anita Sainsbury: none declared. David S Sanders: none declared. Alexander C. Ford: none declared.

Declaration of Funding Interests:

None.

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