Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time. Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions, interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which term corresponds to which region of which image in the group. In this paper, we address this problem using a new machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant performance improvement over state-of-the-art approaches. E MBRYONIC development is orchestrated by the spatiotemporal expression of a multitude of genes over time. Understanding of this expression dynamics paves the way toward unraveling the patterns and processes governing development. The fruit fly Drosophila melanogaster is a wellstudied model organism for this purpose. To accelerate genome-wide studies of Drosophila embryogenesis, the Berkeley Drosophila Genome Project (BDGP) [41] , [42] has produced comprehensive atlas of gene expression patterns during Drosophila embryonic development in the form of two-dimensional (2D) digital images by high-throughput RNA in situ hybridization to whole-mount embryos. Each in situ image records the spatial distribution of gene expression within an embryo at a particular Drosophila time (developmental stage range). These spatio-temporal images are stored in a database and organized in groups based on genes and developmental stages. Text-based anatomical and developmental ontology terms using a controlled vocabulary (CV) [42] are assigned to each image group, as illustrated in Fig. 1 . These annotations indicate the gene expression and can be used for identifying genes with similar patterns and for connecting embryonic gene expression with their adult counterparts [17] , [21] , [27] . The annotation task is generally carried out manually by human curators. With the rapid accumulation of the in situ images, manual annotation becomes more and more intractable. Therefore, it is highly desired to develop computational methods to automate the annotation task [23] , [52] .. The task of automating expression annotation poses several significant challenges. First of all, in the BDGP database, the annotation terms are assigned collectively to groups of images, and some terms do not apply to all the images in the group. As illustrated in Fig. 1 , each image group is assigned with multiple CV terms, but this does not imply that each image in the group is associated with all the terms. Moreover, the terms are generally relevant to regions rather than whole images, while in the BDGP database the correspondence between the regions and the terms is unknown. The problem is even more complicated by considering that even for the same term, the corresponding regions in different images may have significant variations in visual appearances. In addition, the variability and complexity in the morphology of embryo anatomical structures and the effects of overlapping structure in a 3D embryo on signal detection also cause difficulties for the annotation problem.. Several prior attempts for the automated annotation of fruit fly embryo in situ images have been reported. Zhou and Peng [52] conducted their study based on a simplified assumption that each image in the data set is annotated with CV terms. They represented each image by wavelet embryo features and designed a two-tier automatic annotation system. The setting in their study, however, does not directly apply to the BDGP data due to their simplified assumption. Ji et al. [23] used pyramid match kernels [15] , [28] to calculate the similarity between sets of images, and proposed a multiple-kernel-learning formulation based on a hypergraph to construct a predictive model. To achieve better annotation performance, a bag-of-words scheme was employed later [22] to represent the expression patterns of image groups, and a linear classification model based on shared-subspace learning framework was constructed. Recently, they built a local regularization based classification model with image groups represented by an improved bag-of-words scheme to perform computational annotation [24] , and achieved the best annotation performance on the BDGP database to date.. In this paper, we first show that the learning task underlying the Drosophila gene expression pattern annotation problem matches well with a new machine learning framework, Multi-Instance Multi-Label (MIML) learning [56] , [57] . Then, we propose two support vector machine algorithms under the MIML framework to tackle the annotation task. Experiments show that the exploitation of MIML framework can lead to performance superior to existing Drosophila gene expression pattern annotation approaches.. The rest of this paper is organized as follows: In Section 2, we focus on the formalization of the annotation problem and briefly introduce the MIML learning framework. In Section 3, we present the MIMLSVM þ method and extend it further by incorporating a strategy to take into account the correlations between annotations. Experimental results are reported in Section 4, followed by discussions in Section 5. Finally, Section 6 concludes the paper. In this paper, we address the problem of automated annotation of Drosophila gene expression pattern images, which extends our preliminary research [30] . We first show that the underlying nature of the annotation task matches well with a new machine learning framework, MIML learning [56] , [57] . Then, we propose two MIML learning algorithms, MIMLSVM þ and E-MIMLSVM þ , to deal with the annotation problem. Experiments show that our MIML solutions to the annotation problem can lead to performance superior to state-of-the-art Drosophila gene expression pattern annotation method, which demonstrates the effectiveness of the MIML learning framework for annotating bioimages.. The maturing of technologies for high-throughput data production and curation makes genome-wide studies and the integration of genomic data with high volume of various information feasible. Although, large amounts of image data have been produced, automatically interpreting these data is still in its infancy. Effective and efficient approaches are demanded. The work described in this paper provides a new solution to automated understanding of bioimages. Although our work is based on the Drosophila embryo image data, it can also be applied to image data of other species or other bioimage-related problems. This makes our methods valuable for the general areas of bioimage informatics [35] .. The proposed MIML learning methods achieve promising prediction results, however, there is still much room to improve. This can be achieved either by designing more descriptive features for representing expression patterns or by enhancing the models to utilize training information more effectively, for example, identifying high-order correlations among annotation terms and trying to make use of them. Currently, our work focuses more on the problem of data representation and model construction, and less on the image normalization problem. In the future, we plan to work out computational methods that can implement fully automated analysis of the original expression images. Sudhir Kumar received the bachelor's degree in electrical and electronics engineering and the masters' degree in biological sciences from the Birla Institute of Technology and Science (BITS) at Pilani in India. He is a professor of life sciences and director of the Center for Evolutionary Medicine and Informatics, Biodesign Institute, at Arizona State University. His doctoral work in genetics and postdoctoral research in molecular evolution was conducted at Pennsylvania State University. He is an interdisciplinary scientist bringing the problemsolving skills from his engineering background and his knowledge of evolutionary genetics to tackle long-standing problems in functional genomics, medicine, and evolutionary biology. Zhi-Hua Zhou (S'00-M'01-SM'06) received the BSc, MSc, and PhD degrees in computer science from Nanjing University, China, in 1996, 1998, and 2000, respectively, all with the highest honors. He joined the Department of Computer Science and Technology at Nanjing University as an assistant professor in 2001, and is currently professor and director of the LAMDA group. His research interests are in artificial intelligence, machine learning, data mining, pattern recognition and image retrieval. In these areas he has published more than 80 papers in leading international journals or conference proceedings, and holds 11 patents. He has won various awards/honors including the National Science & Technology Award for Young Scholars of China, the Fok Ying Tung Young Professorship 1st-Grade Award, the Microsoft Young Professorship Award, PAKDD2006 Data Mining Competition Grand Champion, and a number of international journals/ conferences paper awards. He is an associate editor-in-chief of the Chinese Science Bulletin, associate editor of the IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Intelligent Systems and Technology, and on the editorial boards of various other journals. He is the founding steering committee cochair of ACML, a steering committee member of PAKDD and PRICAI, program committee chair/cochair of PAKDD07, PRICAI08 and ACML09, vice chair, or area chair or senior PC of conferences including IEEE ICDM06, IEEE ICDM08, SIAM DM09, ACM CIKM09, ACM SIGKDD10, ECML PKDD10, ICPR10, AAAI'11, IJCAI'11, ACM SIGKDD'11, etc., and general chair/cochair or program committee chair/cochair of various native conferences in China. He is the chair of the Machine Learning Technical Committee of the Chinese Association of Artificial Intelligence, vice chair of the Artificial Intelligence & Pattern Recognition Technical Committee of the China Computer Federation, and the chair of the IEEE Computer Society Nanjing Chapter. He is a senior member of the ACM and the IEEE.. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib.

B B I I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I I O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O B I O B I I I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A nonadaptive NC checker for permutation group intersection In this paper we design a nonadaptive NC Motivated by the problem of program correctness and reliability, Blum and Kannan introduced in [3] the concept of program checking. Rather than trying to prove a given program correct for all inputs (which is the approach taken in the area of Program Verification), the approach of program checking is to test and certify a given program for a given input instance. More precisely, a program checker for a given program P is another program that for any instance 3: of P decides whether the output of P on x is correct or whether P has errors (the formal definition is given later in Section 2). In the course of checking P on z, the program checker might also query the program P on instances different from Blum and Kannan in [3] and subsequent researchers, e.g. [4] have shown that this is a fundamental concept. In [3, 41 and several other papers, efficient (in an appropriate sense depending on the problem) checkers have been

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Listing all potential maximal cliques of a graph A potential maximal clique of a graph is a vertex set that induces a maximal clique in some minimal triangulation of that graph. It is known that if these objects can be listed in polynomial time for a class of graphs, the treewidth and the minimum ÿll-in are polynomially tractable for these graphs. We show here that the potential maximal cliques of a graph can be generated in polynomial time in the number of minimal separators of the graph. Thus, the treewidth and the minimum ÿll-in are polynomially tractable for all classes of graphs with a polynomial number of minimal separators. The notion of treewidth was introduced at the beginning of the 1980s by Robertson and Seymour [28, 29] in the framework of their graph minor theory. A graph H is a minor of a graph G if we can obtain H from G by repeatedly using the following operations: vertex deletion, edge deletion and edge contraction. Among the deep results obtained by Robertson and Seymour, we can cite the fact that every class of graphs closed under taking minors which does not contain all the planar graphs has bounded treewidth.. A graph is chordal or triangulated if every cycle of length greater or equal to four has a chord, i.e. an edge between two non-consecutive vertices of the cycle. A triangulation of a graph is a chordal embedding, that is a supergraph, on the same vertex set, which is triangulated. The treewidth problem consists in ÿnding a triangulation such that the maximum size of a clique is as small as possible. Another close problem is the minimum ÿll-in problem. Here we have to ÿnd a triangulation of the graph such that the number of added edges is minimum. In both cases, we can restrict to minimal triangulations, i.e. triangulations with a set of edges minimal by inclusion.. The treewidth and the minimum ÿll-in play an important role in various areas of computer science. Among the real-life applications we can cite sparse matrix factorization [30] , Cholesky factorization [26] , perfect phylogeny problem [18] and register allocation [32, 10] . General techniques exist for solving classical algorithmic graph theory problems for graphs of bounded treewidth [3, 15, 2, 8] . For an extensive survey of these applications see also [5, 7] .. For computing the treewidth we usually look for a tree decomposition, that is a tree such that each node of the tree is labeled by a vertex set of the graph. The labels of the nodes must respect some constraints: every vertex of the graph must appear in some label, the endpoints of an edge must appear in a same label, if a same vertex is in two di erent labels it must be in all the labels on the unique path of the tree connecting the two occurrences of the vertex. The width of the tree decomposition is then the size of the largest label minus one, and the treewidth is the smallest width over all the tree decompositions of the graph. In some sense, the smaller the treewidth of a graph, the more it has tree-like structure. Many graph problems that model real-life problems are intractable in the sense that they are NP-hard. If we deal with a class of graphs with bounded treewidth most of these problems become polynomial and even linear e.g. maximum independent set, hamiltonian circuit or Steiner tree. There are two ways to solve problems when the treewidth is bounded, the ÿrst uses dynamic programming [5, 17] and the second is based upon reduction techniques [2, 8] .. Unfortunately the computation of the treewidth and of the minimum ÿll-in of a graph are NP-hard [1, 34] even for co-bipartite graphs. However, a polynomial time approximation algorithm with O(log n) performance ratio is described in [9] . The problem of the existence of a polynomial algorithm to approximate the treewidth of a graph within a multiplicative constant remains still open. For any ÿxed constant k, there exist polynomial algorithms ÿnding a tree decomposition of width at most k if such a decomposition exists. Arnborg et al. [1] gave the ÿrst algorithm that solves this problem in O(n k+2 ) time. Since numerous improvements have been done on the domain until the linear time algorithm of Bodlaender [6] . Notice that the constant hidden by the O notation is doubly exponential in k 2 . Several results for treewidth appeared in the literature in connection with logic. The works by Arnborg et al. [2] , Courcelle [14] , Courcelle and Mosbah [15] led to the conclusion that all the problems which are expressible in extended monadic second order logic can be solved in linear time for graphs of bounded treewidth.. There exist several classes of graphs with unbounded treewidth for which we can solve polynomially the problem of the treewidth and the minimum ÿll-in. Among them there are the chordal bipartite graphs [21, 13] , circle and circular-arc graphs [31, 25] , AT-free graphs with polynomial number of minimal separators [24] . Most of these algorithms use the fact that these classes of graphs have a polynomial number of minimal separators, i.e. for each of these classes, there is a polynomial P(n) such that every graph G in the class of n vertices has at most P(n) minimal separators. It was conjectured in [19, 20] that the treewidth and the minimum ÿll-in should be tractable in polynomial time for every graph class having a polynomial number of minimal separators. We solve here this ESA'93 conjecture.. The crucial interplay between the minimal separators of a graph and the minimal triangulations was pointed out by Kloks et al. in [23] , these results were concluded in Parra and Sche er [27] . Two minimal separators S and T cross if T intersects two connected components of G\S, otherwise they are parallel. The result of [27] states that a minimal triangulation is obtained by considering a maximal set of pairwise parallel separators and by completing them i.e. by adding all the missing edges inside each separator. However this characterization gives no algorithmic information about how we should construct a minimal triangulation in order to minimize the cliquesize or the ÿll-in.. Trying to solve this later conjecture, we studied in [11, 12] the notion of a potential maximal clique. A vertex set K is a potential maximal clique if it appears as a maximal clique in some minimal triangulation. In [11] , we characterized a potential maximal clique in terms of the maximal sets of neighbor separators, which are the minimal separators contained in it. We designed an algorithm which takes as input the graph and the maximal sets of neighbor separators and which computes the treewidth in polynomial time in the size of the input. For all the classes mentioned above we can list the maximal sets of neighbor separators in polynomial time, so we uniÿed all the previous algorithms. Actually, the previous algorithms compute the maximal sets of neighbor separators in an implicit manner. In [12] , we gave a new characterization of the potential maximal cliques avoiding the minimal separators. This allowed us to design a new algorithm that, given a graph and its potential maximal cliques, computes the treewidth and the minimum ÿll-in in polynomial time. Moreover this approach permitted us to solve the two problems for a new class of graphs, namely the weakly triangulated graphs. It was probably the last natural class of graphs with polynomial number of minimal separators for which the two problems remained open.. This paper is devoted to solve the ESA'93 conjecture, that is the treewidth and the minimum ÿll-in are polynomially tractable for every graph class having a polynomial number of minimal separators. Recall that if we are able to generate all the potential maximal cliques of any graph in polynomial time in the number of its minimal separators, then the treewidth and the minimum ÿll-in are also computable in polynomial time in the number of minimal separators. We deÿne the notion of an active separator for a potential maximal clique which leads to two results. First, the number of potential maximal cliques is polynomially bounded by the number of minimal separators. Secondly, we are able to enumerate the potential maximal cliques in polynomial time in their number. These results reinforce our conviction that the potential maximal cliques are the pertinent objects to study when dealing with treewidth and minimum ÿll-in. The notion of a potential maximal clique seems to be very useful for the study of the treewidth and the minimum ÿll-in problems. We proved in [12] that the potential maximal cliques are su cient for computing the treewidth and the minimum ÿll-in of a graph. In this paper, we enumerate the potential maximal cliques in polynomial time in the number of minimum separators of the input graph. In particular, this gives a polynomial algorithm computing the treewidth and the minimum ÿll-in for all the classes of graphs with polynomial number of minimal separators.. Our generic algorithm is, of course, less e cient than algorithms working on particular graph classes. While enumerating the potential maximal cliques of the input graph, we also generate sets of vertices that do not form potential maximal cliques, so we loose time. There might also be a faster algorithm to recognize whether a given a set of vertices K of a graph G is a potential maximal clique of G than the one of Corollary 12. We feel that improving these two points might lead to a much better time complexity for the treewidth and minimum ÿll-in algorithm.. A class of graphs may have an exponential number of minimal separators and consequently an exponential number of potential maximal cliques. Notice that there is no such class of graphs for which the treewidth problem has been solved in polynomial time, except the graphs of bounded treewidth. For example, the problem is still open for planar graphs. We think that a polynomial number of well-chosen potential maximal cliques could permit to compute or at least approximate the treewidth for classes of graphs with "many" minimal separators.

O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O O O O O O O O O O O O O O B I I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I I O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I O O O O O O O B I I O O O O O O O O O O O O O B I I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B I I O O O O O O B O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O B I I O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B O O O O O O B I


Semiautomated Segmentation of Myocardial Contours for Fast Strain Analysis in Cine Displacement-Encoded MRI Abstract-The purposes of this study were to develop a semiautomated cardiac contour segmentation method for use with cine displacement-encoded MRI and evaluate its accuracy against manual segmentation. This segmentation model was designed with two distinct phases: preparation and evolution. During the model preparation phase, after manual image cropping and then image intensity standardization, the myocardium is separated from the background based on the difference in their intensity distributions, and the endo-and epi-cardial contours are initialized automatically as zeros of an underlying level set function. During the model evolution phase, the model deformation is driven by the minimization of an energy function consisting of five terms: model intensity, edge attraction, shape prior, contours interaction, and contour smoothness. The energy function is minimized iteratively by adaptively weighting the five terms in the energy function using an annealing algorithm. The validation experiments were performed on a pool of cine data sets of five volunteers. The difference between the semiautomated segmentation and manual segmentation was sufficiently small as to be considered clinically irrelevant. This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications.

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A review on the cognitive function of information structure during language comprehension Abstract During communication, information structure can be used to highlight the most relevant piece of information, so that sufficient amount of attention can be allocated to the most important information. This paper aims to review the cognitive function of information structure during language comprehension from a neurocognitve perspective. First, we gave a brief introduction to the concept of information structure that has been studied mostly in linguistic field. Then we introduced recent studies on information structure using electrophysiological and neuroimaging techniques. After that, we discussed the relationship between attention and language processing more generally. Finally, we discussed potential directions for future studies. Picture yourself in a party while a stranger talks to you about how to build a computer in which you have no interest at all. Your mind might drift away thinking about your hungry dog at home. In this case, you will not be able to understand him because you simply do not pay enough attention to his words. However, a question such as ''what do you think?'' or a word with suddenly raised pitch in his sentence might grab your attention and then you start to try to understand what he is talking about. In this scenario, attention plays an important role in how well you are engaged in the conversation.. Specifically, there are two sets of processes that control the flow of information. The top-down control refers to the ability to selectively attend to the relevant piece of information based on prior knowledge or current goals, whereas the bottom-up selection refers to the capacity of quickly allocating processing resources to novel or salient stimuli irrespective of prior goal or task. These two processes have been termed as two aspects of attention, and have been evidenced in numerous studies of visual perception (for a review see Corbetta et al. 2002) . The relationship between attention and language processing has been discussed previously. For instance, the visual world paradigm assumes that language-mediated eye movement reflects overt attention guided by language input. Since the language users' eye movements are systematically related to their linguistic processing, the visual world paradigm has been used to study the way listeners understand and speakers produce utterances (for a review see Huettig et al. 2011) . Meanwhile, it has been shown that the executive control ability (including attentional control) of bilinguals is more advanced than that of monolinguals (Bialystok 1999; Bialystok and Viswanathan 2009) , suggesting the influence of language experience on cognitive development. However, the above-mentioned studies have not addressed how language processing itself is modulated by different attentional settings. In particular, little is known how linguistic device is used to modulate attention, which in turn affects language processing. Therefore, this paper aims to provide a comprehensive insight into the interface between attention and language comprehension by reviewing recent studies on how linguistic marking-information structure (IS)-modulates language processing from a neurocognitive perspective.. In this paper, we first introduced some concepts regarding IS, then we presented empirical studies on the IS influence of language processing. Among these studies, the role of IS in modulating depth of processing was highlighted. After that, the relationship between attention and language processing was discussed in general. Finally, future directions on the interaction between language and attention were pointed out.

O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O B O O O


Automatic Evaluation of Trainee Nurses' Patient Transfer Skills Using Multiple Kinect Sensors To help student nurses learn to transfer patients from a bed to a wheelchair, this paper proposes a system for automatic skill evaluation in nurses' training for this task. Multiple Kinect sensors were employed, in conjunction with colored markers attached to the trainee's and patient's clothing and to the wheelchair, in order to measure both participants' postures as they interacted closely during the transfer and to assess the correctness of the trainee's movements and use of equipment. The measurement method involved identifying body joints, and features of the wheelchair, via the colors of the attached markers and calculating their 3D positions by combining color and depth data from two sensors. We first developed an automatic segmentation method to convert a continuous recording of the patient transfer process into discrete steps, by extracting from the raw sensor data the defining features of the movements of both participants during each stage of the transfer. Next, a checklist of 20 evaluation items was defined in order to evaluate the trainee nurses' skills in performing the patient transfer. The items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on whether the participants' relevant body parts were positioned in a predefined spatial range that was considered 'correct' in terms of safety and efficacy (e.g., feet placed appropriately for balance). The second method was based on quantitative indexes and thresholds for parameters describing the participants' postures and movements, as determined by a Bayesian minimum-error method. A prototype system was constructed and experiments were performed to assess the proposed approach. The evaluation of nurses' patient transfer skills was performed successfully and automatically. The automatic evaluation results were compared with evaluation by human teachers and achieved an accuracy exceeding 80%. Automatic skill evaluation in the context of manual tasks can be achieved by utilizing sensors to track a trainee's motion, which can then be compared with known spatial criteria for correct execution of the task (e.g., correct posture, safe positioning of equipment). An automatic system can then rate the trainee's skill level or judge whether the perforManuscript received June 11, 2013 . Manuscript revised August 17, 2013 . † The authors are with Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, 277-8568 Japan.. † † The authors are with the Faculty of Nursing, Tokyo Ariake University of Medical and Health Sciences, Tokyo, 135-0063 Japan.. † † † The author is with the Department of Advanced FibroScience, Kyoto Institute of Technology, Kyoto-shi, 606-8585 Japan.. a) E-mail: zhifeng@race.u-tokyo.ac.jp DOI: 10.1587/transinf.E97.D.107 mance of the task was correct or incorrect. Compared with traditional skill evaluation, which is carried out by trainers or teachers, automatic skill evaluation has several advantages: saving manpower and time, providing quantitative and objective evaluation results, and enabling the trainees to train by themselves. For these reasons, recent research has focused increasingly on the development of automatic skill evaluation in a variety of fields.. Nursing involves complex and heavy physical work. To conserve energy, avoid injuries, and ensure the patient's comfort and safety [1] , [2] , nurses require many skills in their daily work, including the use of proper body mechanics [3] . However, with the shortage of nursing teachers and with limited class time, students are often unable to receive sufficient training with skill evaluation before starting their hospital careers. As a result, nurses may perform inadequately, leading to fatigue, injuries, and even medical accidents. A large percentage of nurses suffer from work-related musculoskeletal disorders [4] , particularly lower back pain [5] - [7] . In view of this, developing technologies that could relieve teachers of the tasks of monitoring performance and providing objective and quantitative evaluation results would be very helpful for students developing their nursing skills.. Patient transfer is one of the heaviest and most difficult nursing activities, and it is performed frequently by nurses in hospitals and nursing homes [8] . The skills required for patient transfer include using proper body mechanics with appropriate timing of the transfer, placing the wheelchair properly before starting to transfer the patient. Moreover, according to the patient's conditions (e.g., disorder and disease) and the relations of body features between a patient and a nurse, there are varieties of adjustments and modifications of the skills. As the first step, we focused on the basic patient transfer skill training for the beginner. We targeted the trainees who have learned the knowledge of patient transfer from textbooks and demonstrate videos. According to nursing teachers' suggestion, the patients were assumed to be weak persons who cannot stand up by themselves. Also, the influence of the body size between patient and nurses to the skills was not taken into account in this step.. To realize a skill evaluation system for patient transfer training, the following two problems should be addressed. be segmented into discrete steps.. • The postures of the patient and nurse during their close interaction need to be measured. In addition, the system should not interfere with the nurse's actions.. In many previous studies, researchers have utilized image-processing technologies for skill evaluation in various fields. In previous work [9] , based on a depth camera, a real-time posture analysis system was developed to classify a single worker's posture as ergonomic or not. Motion recognition techniques were applied for the construction of a system for single-person aerobic training [10] . In former study [11] , based on motion capture technology, a virtual-reality dance training system was developed. In previous work [12] , virtual-reality techniques and motion capture technology were applied for Tai Chi training. Using a red-green-blue (RGB) camera and a dummy skin in which force sensors were embedded, a system was established for surgical skill evaluation [13] . In previous research [14] , using multiple RGB cameras, the postures of both the patient and nurse during patient transfer training were measured. In addition, other researchers dealted with the skill evaluation in medical teamwork training by utilizing a single RGB camera to tracked the trajectory of each trainee's head [15] . Other work has involved attaching sensors to human bodies for skill evaluation or for loading analysis. Inertia measurement unit (IMU) sensors were used to evaluate skills in golf training [16] . Also, other researches used IMU sensors to attached to the trainee's upper limbs, for evaluating skills in laparoscopic surgery [17] . In previous work [18] , a triaxial electrogoniometer was used to measure the 3D position, velocity, and acceleration of the participants' trunks, to assess their spinal loading during patient transfer. However, these earlier studies were limited because the methods only measured a single person's posture [9] - [13] , or only detected the trajectory of the head [15] , or might have interfered with the trainee's actions [16] - [18] . In other words, they could not measure the postures of two people interacting closely. Furthermore, none of these studies addressed the problem of the segmentation of the motion sequence.. The aim of the research described in this paper is to solve the problems of motion sequence segmentation and the measurement of the posture of two people interacting closely, for the evaluation of patient transfer skills. The challenging aspect of sequence segmentation is that an unskilled trainee might perform unpredictable actions during the patient transfer, such as walking back and forth. This will make the segmentation difficult. The challenging aspect of posture measurement is the body overlap caused by the close interaction between trainee and patient.. First, to realize the motion sequence segmentation during patient transfer, we used features of the 3D trajectory of the patient's head and the fixed order of the steps required for patient transfer.. Next, to realize the measurement of the postures of two people in close interaction, two Kinect sensors (Microsoft Co., Ltd) [19] was employed. The existing softwares, including Microsoft official Kinect SDK [19] and the middleware libraries of OpenNI [20] , are capable of capturing up to 2 persons simultaneously. However, the precondition of these software to work is that there must be not anybody overlap between the persons. Therefore, the existing softwares are unsuitable for posture measurement of patient transfer, since the body overlap is serious. To solve our problem, markers of different colors were employed. The body joints were recognized via the colors of the attached markers, and their 3D positions were calculated by combining the depth and color images from the sensors. This measurement method has two advantages. First, it does not interfere with the motion of the trainee, in contrast to methods involving sensors attached to the participants [16] - [18] . Second, compared with systems using multiple RGB cameras [11] , [12] , [14] , the present system is simpler and requires fewer calculations.. This paper extends the studies of [21] - [23] by (a) including the evaluation of motions segmentation's accuracy for a more detailed assessment of the evaluation accuracy and (b) increasing the number of experimental subjects (10) to examine the evaluation accuracy of all evaluation items (20) of patient transfer.. The remainder of the paper is structured as follows. Section 2 describes the skills required for patient transfer and presents the problem statement. Section 3 details the proposed approach, including the sensor system's composition, the motion sequence segmentation, posture measurement, and methods for classifying trainee performance for the evaluated items as correct or incorrect. Section 4 presents the results of experiments examining the performance of the proposed approach. Section 5 discusses the results and concludes the paper. This paper addressed the task of automatically evaluating trainee nurses' skills in performing patient transfer, using multiple Kinect sensors and colored markers on both participants' clothing and on a wheelchair.. The paper proposed a method that utilizes the features of the trajectory of the patient's head, under the assumption that all steps are performed in a fixed order, to segment the motion sequence during patient transfer into discrete steps. The postures of the trainee and patient, and the wheelchair's placement, orientation, and state of preparation, were measured by combining color and depth information. The evaluation items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on detecting whether target body joints were inside a defined region. The second method was based on quantitative indexes and thresholds determined by using a Bayesian minimum-error method. Experimental results revealed that the skill evaluation was performed automatically and successfully. The accuracy of the system's evaluation was examined in comparison with nursing teachers' assessments, and the system's accuracy for all items exceeded 80%. This result confirms that our proposed approach is able to achieve good precision rates for skill evaluation in patient transfer training. The results show that the prototype system was able to measure automatically the postures of the trainee and the patient throughout their close interaction. The whole process of skill evaluation was completed automatically within 2 minutes, which satisfied the nursing teachers' recommendations for the timeliness of the evaluation. The recognition rate for the patient-transfer steps demonstrated that the proposed segmentation method was able to recognize the defined patient transfer steps effectively and precisely. In addition, the segmentation method was not affected by interruptions when the trainees performed unpredictable actions. For the region-based items, all results demonstrated more than 80% accuracy, with the average evaluation accuracy being 95.1%. This result proved that, for the region-based items, the per- formance of the trainee could be effectively evaluated by detecting whether target body joints were inside the defined regions. Furthermore, the average evaluation accuracy for the value-based items exceeded our target of 80%. These results demonstrated that the defined evaluation indexes were effective in discriminating between correct and incorrect performance. In addition, the result also revealed the effectiveness of applying a Bayesian minimum-error method to determine the thresholds. In short, the proposed approach was able to evaluate a trainee's performance for each item with almost the same precision as a teacher.. The results of the evaluation accuracy also revealed that the proposed measurement methods described in Sect. 3.4 are appropriate. For the region-based items, the evaluation accuracy proved that the proposed method which was applying color markers to recognize body joints' was effective. For the value-based items, as Fig. 14 shown, the measurement precision in each index was sufficient to identify trainee's performance as correct or incorrect.. In this paper, we applied the domain knowledge to develop the automatic skill evaluation approach for patient transfer training. Several recognition techniques were proposed. We chose these techniques according to different evaluation items rather than use the unified methods to evaluate all of the items. The benefit of the application of domain knowledge is that the detections of the checklist could make the trainee easy to understand their errors and the correct method. For example, in Item 13, we measured the height of the trainee's waist and compared it with the threshold to identify whether trainees lowered their waist enough. This technique could inform the trainees whether their performances are correct. In addition, it enables to develop the much detailed feedback methods, for instance, to instruct the trainees how much they need to reduce the height of their waist if they perform incorrectly. Also, the recognition techniques match the nursing teacher's evaluation experiences. The evaluation indexes were the same as those the teachers used. This factor facilitates the application of teacher's experience to develop the system. This is very useful when other problems of patient transfer training, for instance, the influence of patients' disease and disorder, were taken into account in future work.. However, there were still a few erroneous judgments in the system's evaluation results. One reason was the misrecognition of patient transfer steps when a trainee forgot to perform one or more of the steps. This misrecognition would lead to the false-negative situation. It caused the detection of target color markers or the measurement of indexes was performed in the wrong image sequence. This misrecognition was outside the scope of the problem addressed in this paper, as a precondition of our proposed approach was that the target trainees have been learned the knowledge of patient transfer from textbooks and demonstrate videos. The trainees would perform all of the patient transfer steps in the correct order. The other reason for errors in the evaluation results was the existence of critical states. For region-based items, a critical state occurs when the evaluation target is located on the boundary of the search region. For value-based items, a critical state occurs when the value of the index is close to the threshold. In such situations, even the teachers found it difficult to judge trainee performance, resulting in inconsistencies in the evaluation results.. In future work, these remaining problems will be solved to reduce the number of erroneous judgments. A method for judging whether the trainee has performed each step will be considered, with the aim of decreasing the error rate in the segmentation of motion sequences. Other factors influencing the determination of the evaluation thresholds will be taken into account, such as the body size of the trainee. In addition, we will extend the application of the proposed approach to other nurse training tasks related to lifting or moving a patient's body. For each sample, the system automatically performed the motion sequence segmentation and the posture measurements. It then output the evaluation results successfully without interruption. The average calculation time was 115 seconds, with a standard deviation of 13 seconds. Figure 11 shows the recognition rate for each patienttransfer step. The average was 96.4%. For the steps denoted as 'standing assistance', 'pivoting assistance', and 'sitting assistance', the system achieved a recognition rate of 100%.. The system's evaluation results were compared with the teachers' evaluation results. The coincidence rate was defined as the system's evaluation accuracy. For the regionbased items, the evaluation accuracy for each item is shown in Fig. 12 . The average was 95.1%, and the system achieved better than the target accuracy of 80% for all items.. For most of the value-based items, the results via the LOOCV method are shown in Fig. 13 . The average evaluation accuracy was 86.8%, and the system achieved better than 80% accuracy for all items. Note that, because the threshold for Item 6 was available via a textbook, its evaluation accuracy was examined by direct comparison with the

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Synthesis of robust tunable oscillators using mitogen activated protein kinase cascades Abstract Mitogen activated protein kinase (MAPK) cascade is evolutionally preserved in all eukaryotic cells, and regulates various cellular activities such as gene expression, mitosis, differentiation, and apoptosis. Recently, Bashor et al. have shown that Ste5 scaffold protein can be used to reshape the MAPK cascade through engineered feedback loops, and have used heuristic tuning mechanisms to synthesize the feedback. A problem of interest is to determine whether information regarding the underlying biochemical reactions can be used to synthesize robust feedback that will ensure that the resultant circuit has the desired properties. In this paper, we consider the problem of engineering feedback in MAPK cascade to synthesize an oscillator of the desired frequency. Our approach builds on the MAPK cascade model derived by Chikarmane et al. who have exploited the existence of a Hopf bifurcation point in the Markevich model of the MAPK cascade to synthesize the exciting kinase as a function of the doubly phosphorylated protein. We show how the L 1 -control theory can be used for a robust synthesis of the oscillator and present the simulation results. Mitogen activated protein kinases (MAPK) are serine/ threonine-specific protein kinases that respond to extracellular stimuli, viz. mitogens, and regulate various cellular activities such as gene expression, mitosis, differentiation, and apoptosis. Extracellular stimuli lead to activation of a MAPK via a signaling cascade, known as the MAPK cascade, comprising MAP kinase, MAP kinase kinase (MAP2K), and MAP kinase kinase kinase (MAP3K). An activated MAP3K phosphorylates a MAP2K on its serine and threonine residues. An activated MAP2K, in turn, phosphorylates a MAPK on its serine and threonine residues. The MAPK cascade is evolutionally preserved in all eukaryotic cells, ranging from yeasts to mammals (see Qiao et al. 2007 ). The MAPK cascade has been implicated in malignant transformations, and in the regulation of cellular growth and proliferation of several tumor types. For example, the active phosphorylated form of MAPK/ ERK is aberrantly expressed in the cultured and primary Hodgkin disease cells (see Zheng et al. 2003) . Therefore, mechanisms that can reshape the MAPK pathways signaling dynamics are of interest.. Recently, a platform based on the Ste5 scaffold protein has been developed to implement feedback in the MAPK cascade in order to alter its response to the mating pheromone stimulation (see Bashor et al. 2008) . Several mathematical models, such as the Huang-Ferrell model (1996) and the Markevich model (2004) , are known to characterize the biochemical reactions in the MAPK cascade reasonably well. Therefore, it should be possible to use well-known system theoretic techniques to synthesize the required feedback controller, if it exists. For example, Chikarmane et al. have engineered a relay relaxation oscillator using the MAPK cascade by exploiting the fact that the Markevich model of the MAPK cascade exhibits a Hopf bifurcation point if a first order low pass filter is used as the feedback controller to synthesize the exciting kinase as a function of the doubly phosphorylated protein (see Chikarmane et al. 2007) . In this paper, we use the wellknown L 1 -control theory to characterize a class of feedback controllers to synthesize the exciting kinase as a function of the doubly phosphorylated protein so that the cascade now exhibits robust tunable oscillations in its output, i.e., in the doubly phosphorylated protein.. An advantage of the L 1 -control theory approach is that it enables one to decouple the problem of meeting the performance specifications and the problem of ensuring robustness to parameter variations and disturbances: the performance can be improved systematically by increasing the adaptation gain whereas the robustness is guaranteedso long as it is feasible-by choosing an appropriate filter. Unlike the traditional adaptive control methodologies, the maximum adaptation gain is restricted only by limitations of the components used to implement the L 1 controller. This ability to decouple the two problems is useful since a MAPK cascade is subject to parameter variations and disturbances (see Chikarmane et al. 2007; Qiao et al. 2007; Bashor et al. 2008) .. The paper is organized as follows. In ''Description of the MAPK cascade'', we present the deterministic mathematical model of the MAPK cascade derived by Chikarmane et al. in 2007 and state the problem of interest. Since hysteresis has been shown to be a key property of the MAPK cascade, we present in ''Background results'' some background results on how oscillations can be induced in a hysteretic system by using an integrator or a low-pass-filter as the negative feedback. In Background on L 1 adaptive controllers, we present an abridged introduction to L 1 adaptive control. In ''Synthesis of the L 1 adaptive controller for the MAPK cascade'', we synthesize an L 1 adaptive controller that induces oscillations of the desired frequency in the MAPK cascade described in ''Description of the MAPK cascade''. In ''Simulation results'', we present the simulation results. In ''Discussion'', we briefly discuss how the control of MAPK cascade oscillations is important in cellular response and in the formation of hippocampal neuron memory. We conclude the paper in ''Conclusion''. MAPK cascade is evolutionally preserved in all eukaryotic cells, and regulates various cellular activities such as gene expression, mitosis, differentiation, and apoptosis. Recently, in Bashor et al. (2008) , have shown that Ste5 scaffold protein can be used to reshape the MAPK cascade through engineered feedback loops, and have used heuristic tuning mechanisms to synthesize the feedback . A problem of interest is to determine whether information regarding the underlying biochemical reactions can be used to synthesize robust feedback that will ensure that the resultant circuit has the desired properties. In this paper, we have examined the problem of engineering feedback in MAPK cascade to synthesize an oscillator of the desired frequency. Our approach builds on the MAPK cascade model derived by Chikarmane et al. in 2007 . In Chikarmane et al. (2007 , the existence of a Hopf bifurcation point in the Markevich model of the MAPK cascade is used to synthesize the exciting kinase as a function of the doubly phosphorylated protein. We show how the L 1 -control theory can be used for a robust synthesis of the desired oscillator. Our L 1 controller is robust to changes in the kinetic parameters of the MAPK cascade. The resultant oscillations are provably stable. Furthermore, it can be shown that the error in the desired states and the actual states asymptotically approaches zero and is upper bounded during the transient response phase. MAPK Fus3 is a key regulator of the yeast mating pheromone response and is known to undergo sustained oscillations in its phosphorylation/activation state during continuous pheromone exposure (see Hilioti et al. 2008) . The MAPK activity oscillations lead to corresponding oscillations in the mating gene expression. These oscillations in MAPK activity and gene expression have been empirically observed to need the negative regulator of G-protein signaling Sst2, and partially need the MAPK phosphatase Msg5. The peaks in Fus3 activation are correlated with periodic rounds of cell morphogenesis, with each peak preceding the formation of an additional mating projection (see Hilioti et al. 2008 ). Fus3-GFP and Fus1-GFP protein levels display strong oscillatory dynamics with frequencies similar to the oscillations of Fus3 activation. Furthermore, increasing the pheromone dose has been observed to result in a progressive phase shift of Fus3-GFP expression peaks towards earlier time points; this trend is also observed for phospho-MAPK levels so that it is now established that the expression levels of MAPK target genes oscillate in approximate synchrony with MAPK activation levels (see Hilioti et al. 2008) . Likewise, the effect of MAPK cascade oscillations on the hippocampal neuron memory has recently been characterized (see Gerstner et al. 2009 ). So, an elaborate control over some cellular functions may be exercised by controlling oscillations in the MAPK cascade. We have used the bifurcation analysis to determine the parameter values required to obtain the desired oscillations. In addition, we have used the bifurcation analysis to determine the sensitivity of the amplitude of oscillations to the parameters. Large amplitude oscillations are obtained only in the close vicinity of the Hopf bifurcation. Indeed, several systems found in the human body, such as the oculomotors and hair cells in the cochlea, operate very close to Hopf bifurcations (Moreau and Sontag 2003) . Operating close to the bifurcation point ensures that large amplitude oscillations encounter minimal damping and are hence sustained.. Our L 1 adaptive controller synthesis approach can be used to synthesize feedback that induces tunable robust oscillations of desired frequency in a network of MAPK cascades as well. Our approach quantifies the transient and steady-state performance bounds of the L 1 adaptive backstepping controller in the presence of fast adaptation. These bounds can be systematically increased by increasing the adaptation rate. L 1 adaptive control architecture guarantees robustness in the presence of fast adaptation. The robustness to parameter variations and disturbances is ensured by suitably modifying the control objective with the understanding that the uncertainties can be adequately compensated for only if the controller bandwidth is high enough-the constraints imposed by robustness requirements on the limits of achievable performance in closed-loop systems are well illustrated in Chaps. 3 and 6 (Skogestad and Postlethwaite 2005) . With the L 1 adaptive control architecture, it appears that a fast rate of adaptation helps meet the performance specifications and also ensures the required robustness margins-the trade-off between these two requirements is to be resolved using the filter (see Li et al. 2008 ).. An advantage of our L 1 -control theory approach is that it enables us to decouple the problem of meeting the performance specifications and the problem of ensuring robustness to parameter variations and disturbances: the performance can be improved systematically by increasing the adaptation gain whereas the robustness is guaranteedso long as it is feasible-by choosing an appropriate filter.. Better performance than the one reported in this paper may be obtained by using a much larger adaptation gain. Unlike the traditional adaptive control methodologies, the maximum adaptation gain is restricted only by limitations of the components used to implement the L 1 controller. In traditional control theoretic applications (e.g., aircraft altitude control and automotive engine speed control), these limitations refer to the hardware limitations whereas in a systems and synthetic biology setting, these refer to the quality of the scaffold and the speed of the chemical reactionsthese implementation aspects are well discussed in Kharam et al. (2011) and Jiang et al. (2010) .

O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Determining the performance of website-based relationship marketing Company websites are an important instrument for relationship marketing activities. We present a methodological framework that aligns website performance assessment and marketing intelligence for evaluating the performance of relationship marketing activities. In this context, we develop an extended web mining approach that integrates managerial perspectives in the analyst's investigation of the customer-website interaction based on historical clickstream data.This approach enables quantification of the moderating effect of a website's structure and content regarding website-based relationship marketing. The applicability of our approach is demonstrated by clickstream data of 477,471 visitor sessions on a software developer's website. The results provide detailed insights into the usage behavior on a website and the mechanisms to enhance e-commerce efficiency via website optimization. Today's companies use their websites as windows through which much of the world will see it (Winter, Saunders, & Hart, 2003) . In contrast to traditional e-commerce sites or portals, modern corporate websites provide a broad range of information about themselves, their products and related services to the public (Stuart & Jones, 2004) . As a result, the online presence becomes the most important communication channel a company possesses; determining how effective it supports this communication function is crucial in the overall webpage evaluation (Weinberg, Parise, & Guinan, 2007) .. Several approaches have been introduced to improve web performance covering models based on web mining and click stream analysis (Cho, Kim, & Kim, 2002; Chou, Li, Chen, & Wua, 2010; Kalczynski, Senecal, & Nantel, 2006; Kim & Yum, 2011) , web-based marketing (Tsai, Chou, & Leu, 2011; Wang, Wang, & Farn, 2009) as well as online effectiveness and website evaluation (Chou & Cheng, 2012; Kim, Kwon, & Chang, 2011) . Due to its practical importance, this issue is for example also addressed by popular evaluation instruments such as the Web Effectiveness Index, which helps investigating how a corporate website reacts on global communication trends and how it performs against its peers by quantifying the serving potential for different public target groups (BowenCraggs & Co, 2011) . Although such performance indicators are beneficial for companies, they often exclude direct business related measurements, because they cannot quantify website performance in their serving character for the customer value (Financial Times/Bowen Craggs & Co, 2011) . But this is of utmost importance, as besides benchmarking among peers, companies need to assess customer value first for achieving a competitive advantage (Woodruff, 1997) .. Business relevant measures can be included by referring to the company's applied relationship marketing strategies (RMS), which aim at establishing, developing, and maintaining successful relational exchanges with its customers (Morgan & Hunt, 1994) . These strategies are directly reflected in online performance, as the Internet also became the most important communication channel for a company's RM effort (Srinivasan & Moorman, 2005) . The evaluation of such RM effectiveness is based on customer behavior and allows increasing a company's return of RM investment by offering demand-actuated strategies and services (Reinartz & Kumar, 2003) . In this line of arguing, customer value can be determined in two steps: firstly, by measuring how well the corporate website moderates the company RMS, which is expressed in how well it serves its customers online; and secondly, by inferring involved relationship economics (Palmatier, Dant, Grwal, & Evans, 2006) . As a result, the evaluation of corporate online performance can be integrated with internal business measures and is substantial for marketing decisions (Weischedel, Matear, & Deans, 2005) .. Similar to the spread of relationship marketing evaluation that mainly uses customer data, website performance assessment increasingly takes place on the customer/visitor-level, empirically using clickstream data (Ayanso & Yoogalingam, 2009; Hahn & Kauffman, 2004; Kalczynski, et al., 2006; Olbrich & Holsing, 2011/12) . From the viewpoint of business practice, particularly the "easy-to-use" web analytic approaches that use internal web page-tagging, have been widely employed as they provide a fast method for surveying visitor behavior (Kohavi, Rothleder, & Simoudis, 2002) . However, their scope of analysis is usually limited to a structural, aggregated level of internal page frequentation and related web metrics such as stickiness or prominent exit pages (Booth & Jansen, 2009; Olbrich & Holsing, Winter 2011-12) , which are difficult to relate to customer value. Investigating whole visitor paths using clickstream data can set a much broader scope; further, this allows to infer business relevant website performance measures from visitor usage behavior, as specified in various web mining approaches (Lee, Podlaseck, Schonberg, & Hoch, 2001) . Nevertheless, such elaborate approaches are highly underutilized in business practice due to their increased complexity in data storage, data preparation and analysis in comparison to typical web metric-based methodologies (Sen, Dacin, & Pattichis, 2006) . However, only an investigation on website performance based on the individual visitor session level enables a valid demand-actuated evaluation of customer/visitor value by quantifying the effectiveness of a company's RM effort in their website usage. Though, suitable methodological approaches that are able to enrich existing company data by providing internal measure of website performance in the context of relational marketing analysis are rare (Hahn & Kauffman, 2004; Hochsztain, Millán, Pardo, Pena, & Menasalvas, 2003) .. Furthermore, as far as we know, approaches that directly link the evaluation of a company's RM effectiveness to website performance, empirically based on customer website usage, do not exist.. In this study we attempt to overcome this shortage by presenting a methodological framework that aligns marketing analysis with web mining approaches on the theoretical level. Therein, we develop a methodological approach that integrates managerial perspectives into the modeling and analyzing of a company's RM effectiveness in website performance based on customer website usage data. The knowledge gained supports customer value determination by quantifying RM effective website usage behavior and also enables a demandactuated website optimization and customization for different relational marketing strategies. Furthermore, we extend existing web mining approaches by integrating managerial perspectives on website usage. Addressing. Internet based marketing communication environments finally enriches traditional marketing analysis approaches.

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