[{"doi":"10.1017/S0016672307009081","publist_id":"2777","publication_status":"published","publisher":"Cambridge University Press","extern":1,"type":"journal_article","status":"public","date_updated":"2021-01-12T07:44:37Z","intvolume":"        90","issue":"1","publication":"Genetical Research","day":"01","abstract":[{"lang":"eng","text":"Explicit formulae are given for the effects of a barrier to gene flow on random fluctuations in allele frequency; these formulae can also be seen as generating functions for the distribution of coalescence times. The formulae are derived using a continuous diffusion approximation, which is accurate over all but very small spatial scales. The continuous approximation is confirmed by comparison with the exact solution to the stepping stone model. In both one and two spatial dimensions, the variance of fluctuations in allele frequencies increases near the barrier; when the barrier is very strong, the variance doubles. However, the effect on fluctuations close to the barrier is much greater when the population is spread over two spatial dimensions than when it occupies a linear, one-dimensional habitat: barriers of strength comparable with the dispersal range (B≈σ) can have an appreciable effect in two dimensions, whereas only barriers with strength comparable with the characteristic scale (B\\! \\approx\\! L \\equals \\sigma \\sol \\sqrt {2 \\mu}\\hskip2) are significant in one dimension (μ is the rate of mutation or long-range dispersal). Thus, in a two-dimensional population, barriers to gene flow can be detected through their effect on the spatial pattern of genetic marker alleles."}],"page":"139 - 149","_id":"3606","year":"2008","author":[{"orcid":"0000-0002-8548-5240","full_name":"Nicholas Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton"}],"quality_controlled":0,"date_created":"2018-12-11T12:04:12Z","month":"02","volume":90,"citation":{"short":"N.H. Barton, Genetical Research 90 (2008) 139–149.","ista":"Barton NH. 2008. The effect of a barrier to gene flow on patterns of geographic variation. Genetical Research. 90(1), 139–149.","mla":"Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of Geographic Variation.” <i>Genetical Research</i>, vol. 90, no. 1, Cambridge University Press, 2008, pp. 139–49, doi:<a href=\"https://doi.org/10.1017/S0016672307009081\">10.1017/S0016672307009081</a>.","ieee":"N. H. Barton, “The effect of a barrier to gene flow on patterns of geographic variation,” <i>Genetical Research</i>, vol. 90, no. 1. Cambridge University Press, pp. 139–149, 2008.","apa":"Barton, N. H. (2008). The effect of a barrier to gene flow on patterns of geographic variation. <i>Genetical Research</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/S0016672307009081\">https://doi.org/10.1017/S0016672307009081</a>","ama":"Barton NH. The effect of a barrier to gene flow on patterns of geographic variation. <i>Genetical Research</i>. 2008;90(1):139-149. doi:<a href=\"https://doi.org/10.1017/S0016672307009081\">10.1017/S0016672307009081</a>","chicago":"Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of Geographic Variation.” <i>Genetical Research</i>. Cambridge University Press, 2008. <a href=\"https://doi.org/10.1017/S0016672307009081\">https://doi.org/10.1017/S0016672307009081</a>."},"date_published":"2008-02-01T00:00:00Z","title":"The effect of a barrier to gene flow on patterns of geographic variation"},{"day":"13","abstract":[{"text":"Distributed Denial of Service (DDoS) attacks are today the most destabilizing factor in the global internet and there is a strong need for sophisticated solutions. We introduce a formal statistical framework and derive a Bayes optimal packet classifier from it. Our proposed practical algorithm &quot;Adaptive History-Based IP Filtering&quot; (AHIF) mitigates DDoS attacks near the victim and outperforms existing methods by at least 32% in terms of collateral damage. Furthermore, it adjusts to the strength of an ongoing attack and ensures availability of the attacked server. In contrast to other adaptive solutions, firewall rulesets used to resist an attack can be precalculated before an attack takes place. This ensures an immediate response in a DDoS emergency. For evaluation, simulated DDoS attacks and two real-world user traffic datasets are used.","lang":"eng"}],"page":"174 - 179","_id":"3694","year":"2008","doi":"10.1109/ICN.2008.64","publist_id":"2671","publisher":"IEEE","status":"public","extern":1,"publication_status":"published","type":"conference","date_updated":"2021-01-12T07:49:01Z","main_file_link":[{"open_access":"0","url":"http://pub.ist.ac.at/~chl/papers/goldstein-icn2008.pdf"}],"month":"04","citation":{"ista":"Goldstein M, Lampert C, Reif M, Stahl A, Breuel T. 2008. Bayes optimal DDoS mitigation by adaptive history-based IP filtering. ICN: International Conference on Networking, 174–179.","short":"M. Goldstein, C. Lampert, M. Reif, A. Stahl, T. Breuel, in:, IEEE, 2008, pp. 174–179.","chicago":"Goldstein, Markus, Christoph Lampert, Matthias Reif, Armin Stahl, and Thomas Breuel. “Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering,” 174–79. IEEE, 2008. <a href=\"https://doi.org/10.1109/ICN.2008.64\">https://doi.org/10.1109/ICN.2008.64</a>.","ama":"Goldstein M, Lampert C, Reif M, Stahl A, Breuel T. Bayes optimal DDoS mitigation by adaptive history-based IP filtering. In: IEEE; 2008:174-179. doi:<a href=\"https://doi.org/10.1109/ICN.2008.64\">10.1109/ICN.2008.64</a>","apa":"Goldstein, M., Lampert, C., Reif, M., Stahl, A., &#38; Breuel, T. (2008). Bayes optimal DDoS mitigation by adaptive history-based IP filtering (pp. 174–179). Presented at the ICN: International Conference on Networking, IEEE. <a href=\"https://doi.org/10.1109/ICN.2008.64\">https://doi.org/10.1109/ICN.2008.64</a>","mla":"Goldstein, Markus, et al. <i>Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering</i>. IEEE, 2008, pp. 174–79, doi:<a href=\"https://doi.org/10.1109/ICN.2008.64\">10.1109/ICN.2008.64</a>.","ieee":"M. Goldstein, C. Lampert, M. Reif, A. Stahl, and T. Breuel, “Bayes optimal DDoS mitigation by adaptive history-based IP filtering,” presented at the ICN: International Conference on Networking, 2008, pp. 174–179."},"date_published":"2008-04-13T00:00:00Z","title":"Bayes optimal DDoS mitigation by adaptive history-based IP filtering","author":[{"last_name":"Goldstein","full_name":"Goldstein,Markus","first_name":"Markus"},{"first_name":"Christoph","full_name":"Christoph Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","orcid":"0000-0001-8622-7887"},{"last_name":"Reif","full_name":"Reif,Matthias","first_name":"Matthias"},{"last_name":"Stahl","first_name":"Armin","full_name":"Stahl,Armin"},{"last_name":"Breuel","first_name":"Thomas","full_name":"Breuel,Thomas M"}],"quality_controlled":0,"date_created":"2018-12-11T12:04:39Z","conference":{"name":"ICN: International Conference on Networking"}},{"publist_id":"2662","doi":"10.1007/978-3-540-87479-9_27","alternative_title":["LNCS"],"intvolume":"      5211","publisher":"Springer","publication_status":"published","extern":1,"type":"conference","status":"public","date_updated":"2021-01-12T07:49:02Z","abstract":[{"lang":"eng","text":"Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying semantics of the data rather than noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the manifold structure of the data. We propose a method that is simultaneously able to find highly correlated directions that are also located on high variance directions along the data manifold. This is achieved by the use of semi-supervised Laplacian regularization of KCCA. We show experimentally that Laplacian regularized training improves class separation over KCCA with only Tikhonov regularization, while causing no degradation in the correlation between modalities. We propose a model selection criterion based on the Hilbert-Schmidt norm of the semi-supervised Laplacian regularized cross-covariance operator, which we compute in closed form."}],"day":"21","issue":"Part 1","year":"2008","page":"133 - 145","_id":"3698","quality_controlled":0,"author":[{"first_name":"Matthew","last_name":"Blaschko","full_name":"Blaschko,Matthew B"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert","last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887"},{"last_name":"Gretton","first_name":"Arthur","full_name":"Gretton,Arthur"}],"conference":{"name":"ECML: European Conference on Machine Learning"},"date_created":"2018-12-11T12:04:41Z","citation":{"ista":"Blaschko M, Lampert C, Gretton A. 2008. Semi-supervised Laplacian regularization of kernel canonical correlation analysis. ECML: European Conference on Machine Learning, LNCS, vol. 5211, 133–145.","short":"M. Blaschko, C. Lampert, A. Gretton, in:, Springer, 2008, pp. 133–145.","apa":"Blaschko, M., Lampert, C., &#38; Gretton, A. (2008). Semi-supervised Laplacian regularization of kernel canonical correlation analysis (Vol. 5211, pp. 133–145). Presented at the ECML: European Conference on Machine Learning, Springer. <a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">https://doi.org/10.1007/978-3-540-87479-9_27</a>","ieee":"M. Blaschko, C. Lampert, and A. Gretton, “Semi-supervised Laplacian regularization of kernel canonical correlation analysis,” presented at the ECML: European Conference on Machine Learning, 2008, vol. 5211, no. Part 1, pp. 133–145.","mla":"Blaschko, Matthew, et al. <i>Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis</i>. Vol. 5211, no. Part 1, Springer, 2008, pp. 133–45, doi:<a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">10.1007/978-3-540-87479-9_27</a>.","ama":"Blaschko M, Lampert C, Gretton A. Semi-supervised Laplacian regularization of kernel canonical correlation analysis. In: Vol 5211. Springer; 2008:133-145. doi:<a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">10.1007/978-3-540-87479-9_27</a>","chicago":"Blaschko, Matthew, Christoph Lampert, and Arthur Gretton. “Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis,” 5211:133–45. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">https://doi.org/10.1007/978-3-540-87479-9_27</a>."},"month":"10","volume":5211,"date_published":"2008-10-21T00:00:00Z","title":"Semi-supervised Laplacian regularization of kernel canonical correlation analysis"},{"status":"public","extern":1,"publication_status":"published","type":"conference","publisher":"IEEE","date_updated":"2021-01-12T07:51:35Z","publist_id":"2657","acknowledgement":"This work was funded in part by the EC project CLASS, IST 027978.","doi":"10.1109/CVPR.2008.4587448","year":"2008","page":"1 - 8","_id":"3700","abstract":[{"text":"We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP&amp;amp;amp;amp;amp;amp;amp;amp;amp;lsquo;s separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail.","lang":"eng"}],"day":"18","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"date_created":"2018-12-11T12:04:41Z","quality_controlled":0,"author":[{"last_name":"Lampert","full_name":"Christoph Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"date_published":"2008-09-18T00:00:00Z","title":"Partitioning of image datasets using discriminative context information","citation":{"ista":"Lampert C. 2008. Partitioning of image datasets using discriminative context information. CVPR: Computer Vision and Pattern Recognition, 1–8.","short":"C. Lampert, in:, IEEE, 2008, pp. 1–8.","apa":"Lampert, C. (2008). Partitioning of image datasets using discriminative context information (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>","mla":"Lampert, Christoph. <i>Partitioning of Image Datasets Using Discriminative Context Information</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>.","ieee":"C. Lampert, “Partitioning of image datasets using discriminative context information,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","chicago":"Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context Information,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>.","ama":"Lampert C. Partitioning of image datasets using discriminative context information. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>"},"main_file_link":[{"url":"http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf","open_access":"0"}],"month":"09"},{"conference":{"name":"ECCV: European Conference on Computer Vision"},"date_created":"2018-12-11T12:04:43Z","quality_controlled":0,"author":[{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"full_name":"Christoph Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887"}],"date_published":"2008-11-17T00:00:00Z","title":"Learning to localize objects with structured output regression","citation":{"ama":"Blaschko M, Lampert C. Learning to localize objects with structured output regression. In: Vol 5302. Springer; 2008:2-15. doi:<a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">10.1007/978-3-540-88682-2_2</a>","chicago":"Blaschko, Matthew, and Christoph Lampert. “Learning to Localize Objects with Structured Output Regression,” 5302:2–15. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">https://doi.org/10.1007/978-3-540-88682-2_2</a>.","apa":"Blaschko, M., &#38; Lampert, C. (2008). Learning to localize objects with structured output regression (Vol. 5302, pp. 2–15). Presented at the ECCV: European Conference on Computer Vision, Springer. <a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">https://doi.org/10.1007/978-3-540-88682-2_2</a>","mla":"Blaschko, Matthew, and Christoph Lampert. <i>Learning to Localize Objects with Structured Output Regression</i>. Vol. 5302, Springer, 2008, pp. 2–15, doi:<a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">10.1007/978-3-540-88682-2_2</a>.","ieee":"M. Blaschko and C. Lampert, “Learning to localize objects with structured output regression,” presented at the ECCV: European Conference on Computer Vision, 2008, vol. 5302, pp. 2–15.","ista":"Blaschko M, Lampert C. 2008. Learning to localize objects with structured output regression. ECCV: European Conference on Computer Vision, LNCS, vol. 5302, 2–15.","short":"M. Blaschko, C. Lampert, in:, Springer, 2008, pp. 2–15."},"month":"11","volume":5302,"main_file_link":[{"open_access":"0","url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/ECCV2008-Blaschko_5247%5b0%5d.pdf"}],"intvolume":"      5302","status":"public","type":"conference","publication_status":"published","extern":1,"publisher":"Springer","date_updated":"2021-01-12T07:51:37Z","publist_id":"2653","doi":"10.1007/978-3-540-88682-2_2","alternative_title":["LNCS"],"year":"2008","page":"2 - 15","_id":"3705","day":"17","abstract":[{"text":"Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe rformance over binary training as well as the best previously published scores.","lang":"eng"}]},{"conference":{"name":"NIPS SISO: NIPS Workshop on \"Structured Input - Structured Output\""},"date_created":"2018-12-11T12:04:43Z","quality_controlled":0,"author":[{"first_name":"Christoph","full_name":"Christoph Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","orcid":"0000-0001-8622-7887"},{"full_name":"Blaschko,Matthew B","last_name":"Blaschko","first_name":"Matthew"}],"date_published":"2008-12-12T00:00:00Z","title":"Joint kernel support estimation for structured prediction","citation":{"short":"C. Lampert, M. Blaschko, in:, Curran Associates, Inc., 2008, pp. 1–4.","ista":"Lampert C, Blaschko M. 2008. Joint kernel support estimation for structured prediction. NIPS SISO: NIPS Workshop on ‘Structured Input - Structured Output’, 1–4.","chicago":"Lampert, Christoph, and Matthew Blaschko. “Joint Kernel Support Estimation for Structured Prediction,” 1–4. Curran Associates, Inc., 2008.","ama":"Lampert C, Blaschko M. Joint kernel support estimation for structured prediction. In: Curran Associates, Inc.; 2008:1-4.","ieee":"C. Lampert and M. Blaschko, “Joint kernel support estimation for structured prediction,” presented at the NIPS SISO: NIPS Workshop on “Structured Input - Structured Output,” 2008, pp. 1–4.","mla":"Lampert, Christoph, and Matthew Blaschko. <i>Joint Kernel Support Estimation for Structured Prediction</i>. Curran Associates, Inc., 2008, pp. 1–4.","apa":"Lampert, C., &#38; Blaschko, M. (2008). Joint kernel support estimation for structured prediction (pp. 1–4). Presented at the NIPS SISO: NIPS Workshop on “Structured Input - Structured Output,” Curran Associates, Inc."},"month":"12","main_file_link":[{"url":"http://agbs.kyb.tuebingen.mpg.de/wikis/bg/siso2008/Blaschkoetal.pdf","open_access":"0"}],"status":"public","type":"conference","extern":1,"publisher":"Curran Associates, Inc.","publication_status":"published","date_updated":"2021-01-12T07:51:37Z","publist_id":"2650","year":"2008","page":"1 - 4","_id":"3706","day":"12","abstract":[{"text":"We present a new technique for structured prediction that works in a hybrid generative/discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random fields or structured output SVMs?, the proposed method has the advantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them.","lang":"eng"}]},{"citation":{"mla":"Blaschko, Matthew, and Christoph Lampert. <i>Correlational Spectral Clustering</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>.","ieee":"M. Blaschko and C. Lampert, “Correlational spectral clustering,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","apa":"Blaschko, M., &#38; Lampert, C. (2008). Correlational spectral clustering (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>","ama":"Blaschko M, Lampert C. Correlational spectral clustering. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>","chicago":"Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>.","short":"M. Blaschko, C. Lampert, in:, IEEE, 2008, pp. 1–8.","ista":"Blaschko M, Lampert C. 2008. Correlational spectral clustering. CVPR: Computer Vision and Pattern Recognition, 1–8."},"month":"09","date_published":"2008-09-18T00:00:00Z","title":"Correlational spectral clustering","quality_controlled":0,"author":[{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"full_name":"Christoph Lampert","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"date_created":"2018-12-11T12:04:45Z","abstract":[{"text":"We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity measure, or by co-occurrence matrices that are then used in biclustering. In contrast, the proposed method uses separate similarity measures for each data representation, and allows for projection of previously unseen data that are only observed in one representation (e.g. images but not text). We show that this algorithm generalizes traditional spectral clustering algorithms and show consistent empirical improvement over spectral clustering on a variety of datasets of images with associated text.","lang":"eng"}],"day":"18","year":"2008","page":"1 - 8","_id":"3712","publist_id":"2646","doi":"10.1109/CVPR.2008.4587353","status":"public","publication_status":"published","type":"conference","publisher":"IEEE","extern":1,"date_updated":"2021-01-12T07:51:40Z"},{"date_updated":"2021-01-12T07:51:40Z","publisher":"IEEE","publication_status":"published","extern":1,"type":"conference","status":"public","doi":"10.1109/CVPR.2008.4587586","publist_id":"2644","_id":"3714","page":"1 - 8","year":"2008","day":"18","abstract":[{"text":"Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branchand- bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition.","lang":"eng"}],"date_created":"2018-12-11T12:04:46Z","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"author":[{"full_name":"Christoph Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","orcid":"0000-0001-8622-7887"},{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"full_name":"Hofmann,Thomas","first_name":"Thomas","last_name":"Hofmann"}],"quality_controlled":0,"title":"Beyond sliding windows: Object localization by efficient subwindow search","date_published":"2008-09-18T00:00:00Z","main_file_link":[{"url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf","open_access":"0"}],"month":"09","citation":{"short":"C. Lampert, M. Blaschko, T. Hofmann, in:, IEEE, 2008, pp. 1–8.","ista":"Lampert C, Blaschko M, Hofmann T. 2008. Beyond sliding windows: Object localization by efficient subwindow search. CVPR: Computer Vision and Pattern Recognition, 1–8.","ama":"Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization by efficient subwindow search. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>","chicago":"Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding Windows: Object Localization by Efficient Subwindow Search,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>.","ieee":"C. Lampert, M. Blaschko, and T. Hofmann, “Beyond sliding windows: Object localization by efficient subwindow search,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","mla":"Lampert, Christoph, et al. <i>Beyond Sliding Windows: Object Localization by Efficient Subwindow Search</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>.","apa":"Lampert, C., Blaschko, M., &#38; Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>"}},{"year":"2008","_id":"3716","page":"31 - 40","abstract":[{"lang":"eng","text":"Most current methods for multi-class object classification and localization work as independent 1-vs-rest classifiers. They decide whether and where an object is visible in an image purely on a per-class basis. Joint learning of more than one object class would generally be preferable, since this would allow the use of contextual information such as co-occurrence between classes. However, this approach is usually not employed because of its computational cost.\n\nIn this paper we propose a method to combine the efficiency of single class localization with a subsequent decision process that works jointly for all given object classes. By following a multiple kernel learning (MKL) approach, we automatically obtain a sparse dependency graph of relevant object classes on which to base the decision. Experiments on the PASCAL VOC 2006 and 2007 datasets show that the subsequent joint decision step clearly improves the accuracy compared to single class detection.\n"}],"day":"07","intvolume":"      5096","date_updated":"2021-01-12T07:51:41Z","publisher":"Springer","publication_status":"published","extern":1,"status":"public","type":"conference","publist_id":"2641","alternative_title":["LNCS"],"doi":"10.1007/978-3-540-69321-5_4","title":"A multiple kernel learning approach to joint multi-class object detection","date_published":"2008-07-07T00:00:00Z","citation":{"ista":"Lampert C, Blaschko M. 2008. A multiple kernel learning approach to joint multi-class object detection. DAGM: German Association For Pattern Recognition, LNCS, vol. 5096, 31–40.","short":"C. Lampert, M. Blaschko, in:, Springer, 2008, pp. 31–40.","apa":"Lampert, C., &#38; Blaschko, M. (2008). A multiple kernel learning approach to joint multi-class object detection (Vol. 5096, pp. 31–40). Presented at the DAGM: German Association For Pattern Recognition, Springer. <a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">https://doi.org/10.1007/978-3-540-69321-5_4</a>","mla":"Lampert, Christoph, and Matthew Blaschko. <i>A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection</i>. Vol. 5096, Springer, 2008, pp. 31–40, doi:<a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">10.1007/978-3-540-69321-5_4</a>.","ieee":"C. Lampert and M. Blaschko, “A multiple kernel learning approach to joint multi-class object detection,” presented at the DAGM: German Association For Pattern Recognition, 2008, vol. 5096, pp. 31–40.","ama":"Lampert C, Blaschko M. A multiple kernel learning approach to joint multi-class object detection. In: Vol 5096. Springer; 2008:31-40. doi:<a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">10.1007/978-3-540-69321-5_4</a>","chicago":"Lampert, Christoph, and Matthew Blaschko. “A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection,” 5096:31–40. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">https://doi.org/10.1007/978-3-540-69321-5_4</a>."},"main_file_link":[{"url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/DAGM2008-Lampert-Blaschko_5072%5b0%5d.pdf","open_access":"0"}],"volume":5096,"month":"07","conference":{"name":"DAGM: German Association For Pattern Recognition"},"date_created":"2018-12-11T12:04:46Z","quality_controlled":0,"author":[{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert","first_name":"Christoph","last_name":"Lampert"},{"first_name":"Matthew","full_name":"Blaschko,Matthew B","last_name":"Blaschko"}]},{"intvolume":"        12","publisher":"Springer","type":"book_chapter","status":"public","publication_status":"published","extern":1,"date_updated":"2021-01-12T07:51:45Z","publist_id":"2503","doi":"10.1007/978-3-540-73924-1_11","year":"2008","page":"279 - 311","_id":"3726","abstract":[{"lang":"eng","text":"Single-molecule atomic force microscopy (AFM) provides novel ways to characterize the structure-function relationship of native membrane proteins. High-resolution AFM topographs allow observing the structure of single proteins at sub-nanometer resolution as well as their conformational changes, oligomeric state, molecular dynamics and assembly. We will review these feasibilities illustrating examples of membrane proteins in native and reconstituted membranes. Classification of individual topographs of single proteins allows understanding the principles of motions of their extrinsic domains, to learn about their local structural flexibilities and to find the entropy minima of certain conformations. Combined with the visualization of functionally related conformational changes these insights allow understanding why certain flexibilities are required for the protein to function and how structurally flexible regions allow certain conformational changes. Complementary to AFM imaging, single-molecule force spectroscopy (SMFS) experiments detect molecular interactions established within and between membrane proteins. The sensitivity of this method makes it possible to measure interactions that stabilize secondary structures such as transmembrane α-helices, polypeptide loops and segments within. Changes in temperature or protein-protein assembly do not change the locations of stable structural segments, but influence their stability established by collective molecular interactions. Such changes alter the probability of proteins to choose a certain unfolding pathway. Recent examples have elucidated unfolding and refolding pathways of membrane proteins as well as their energy landscapes."}],"day":"08","publication":"Single Molecules and Nanotechnology","date_created":"2018-12-11T12:04:50Z","quality_controlled":0,"author":[{"last_name":"Engel","full_name":"Engel, Andreas","first_name":"Andreas"},{"orcid":"0000-0002-8023-9315","full_name":"Harald Janovjak","last_name":"Janovjak","id":"33BA6C30-F248-11E8-B48F-1D18A9856A87","first_name":"Harald L"},{"full_name":"Fotiadis, Dimtrios","first_name":"Dimtrios","last_name":"Fotiadis"},{"last_name":"Kedrov","first_name":"Alexej","full_name":"Kedrov, Alexej"},{"last_name":"Cisneros","first_name":"David","full_name":"Cisneros, David"},{"last_name":"Mueller","full_name":"Mueller, Daniel J","first_name":"Daniel"}],"date_published":"2008-01-08T00:00:00Z","title":"Single-molecule microscopy and force spectroscopy of membrane proteins","citation":{"ista":"Engel A, Janovjak HL, Fotiadis D, Kedrov A, Cisneros D, Mueller D. 2008.Single-molecule microscopy and force spectroscopy of membrane proteins. In: Single Molecules and Nanotechnology. vol. 12, 279–311.","short":"A. Engel, H.L. Janovjak, D. Fotiadis, A. Kedrov, D. Cisneros, D. Mueller, in:, Single Molecules and Nanotechnology, Springer, 2008, pp. 279–311.","apa":"Engel, A., Janovjak, H. L., Fotiadis, D., Kedrov, A., Cisneros, D., &#38; Mueller, D. (2008). Single-molecule microscopy and force spectroscopy of membrane proteins. In <i>Single Molecules and Nanotechnology</i> (Vol. 12, pp. 279–311). Springer. <a href=\"https://doi.org/10.1007/978-3-540-73924-1_11\">https://doi.org/10.1007/978-3-540-73924-1_11</a>","mla":"Engel, Andreas, et al. “Single-Molecule Microscopy and Force Spectroscopy of Membrane Proteins.” <i>Single Molecules and Nanotechnology</i>, vol. 12, Springer, 2008, pp. 279–311, doi:<a href=\"https://doi.org/10.1007/978-3-540-73924-1_11\">10.1007/978-3-540-73924-1_11</a>.","ieee":"A. Engel, H. L. Janovjak, D. Fotiadis, A. Kedrov, D. Cisneros, and D. Mueller, “Single-molecule microscopy and force spectroscopy of membrane proteins,” in <i>Single Molecules and Nanotechnology</i>, vol. 12, Springer, 2008, pp. 279–311.","chicago":"Engel, Andreas, Harald L Janovjak, Dimtrios Fotiadis, Alexej Kedrov, David Cisneros, and Daniel Mueller. “Single-Molecule Microscopy and Force Spectroscopy of Membrane Proteins.” In <i>Single Molecules and Nanotechnology</i>, 12:279–311. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-73924-1_11\">https://doi.org/10.1007/978-3-540-73924-1_11</a>.","ama":"Engel A, Janovjak HL, Fotiadis D, Kedrov A, Cisneros D, Mueller D. Single-molecule microscopy and force spectroscopy of membrane proteins. In: <i>Single Molecules and Nanotechnology</i>. Vol 12. Springer; 2008:279-311. doi:<a href=\"https://doi.org/10.1007/978-3-540-73924-1_11\">10.1007/978-3-540-73924-1_11</a>"},"month":"01","volume":12},{"publist_id":"2498","doi":"10.1371/journal.pone.0002774","acknowledgement":"NSF Grant PHY-0650617; NIH grants P50 GM071508, R01 GM077599; Burroughs Wellcome Program in Biological Dynamics","intvolume":"         3","type":"journal_article","extern":1,"publisher":"Public Library of Science","status":"public","publication_status":"published","date_updated":"2021-01-12T07:51:49Z","day":"23","abstract":[{"lang":"eng","text":"Gene expression levels fluctuate even under constant external conditions. Much emphasis has usually been placed on the components of this noise that are due to randomness in transcription and translation. Here we focus on the role of noise associated with the inputs to transcriptional regulation; in particular, we analyze the effects of random arrival times and binding of transcription factors to their target sites along the genome. This contribution to the total noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, universal models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally."}],"issue":"7","publication":"PLoS One","year":"2008","_id":"3734","quality_controlled":0,"author":[{"orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Gasper Tkacik"},{"full_name":"Gregor, Thomas","first_name":"Thomas","last_name":"Gregor"},{"full_name":"Bialek, William S","first_name":"William","last_name":"Bialek"}],"date_created":"2018-12-11T12:04:52Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"citation":{"ama":"Tkačik G, Gregor T, Bialek W. The role of input noise in transcriptional regulation. <i>PLoS One</i>. 2008;3(7). doi:<a href=\"https://doi.org/10.1371/journal.pone.0002774\">10.1371/journal.pone.0002774</a>","chicago":"Tkačik, Gašper, Thomas Gregor, and William Bialek. “The Role of Input Noise in Transcriptional Regulation.” <i>PLoS One</i>. Public Library of Science, 2008. <a href=\"https://doi.org/10.1371/journal.pone.0002774\">https://doi.org/10.1371/journal.pone.0002774</a>.","ieee":"G. Tkačik, T. Gregor, and W. Bialek, “The role of input noise in transcriptional regulation,” <i>PLoS One</i>, vol. 3, no. 7. Public Library of Science, 2008.","mla":"Tkačik, Gašper, et al. “The Role of Input Noise in Transcriptional Regulation.” <i>PLoS One</i>, vol. 3, no. 7, Public Library of Science, 2008, doi:<a href=\"https://doi.org/10.1371/journal.pone.0002774\">10.1371/journal.pone.0002774</a>.","apa":"Tkačik, G., Gregor, T., &#38; Bialek, W. (2008). The role of input noise in transcriptional regulation. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0002774\">https://doi.org/10.1371/journal.pone.0002774</a>","short":"G. Tkačik, T. Gregor, W. Bialek, PLoS One 3 (2008).","ista":"Tkačik G, Gregor T, Bialek W. 2008. The role of input noise in transcriptional regulation. PLoS One. 3(7)."},"volume":3,"month":"07","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2475664","open_access":"1"}],"oa":1,"date_published":"2008-07-23T00:00:00Z","title":"The role of input noise in transcriptional regulation"},{"publist_id":"2488","doi":"10.1103/PhysRevE.78.011910","intvolume":"        78","date_updated":"2021-01-12T07:51:51Z","publisher":"American Institute of Physics","type":"journal_article","status":"public","extern":1,"publication_status":"published","day":"21","abstract":[{"lang":"eng","text":"Changes in a cell's external or internal conditions are usually reflected in the concentrations of the relevant transcription factors. These proteins in turn modulate the expression levels of the genes under their control and sometimes need to perform nontrivial computations that integrate several inputs and affect multiple genes. At the same time, the activities of the regulated genes would fluctuate even if the inputs were held fixed, as a consequence of the intrinsic noise in the system, and such noise must fundamentally limit the reliability of any genetic computation. Here we use information theory to formalize the notion of information transmission in simple genetic regulatory elements in the presence of physically realistic noise sources. The dependence of this &quot;channel capacity&quot; on noise parameters, cooperativity and cost of making signaling molecules is explored systematically. We find that, in the range of parameters probed by recent in vivo measurements, capacities higher than one bit should be achievable. It is of course generally accepted that gene regulatory elements must, in order to function properly, have a capacity of at least one bit. The central point of our analysis is the demonstration that simple physical models of noisy gene transcription, with realistic parameters, can indeed achieve this capacity: it was not self-evident that this should be so. We also demonstrate that capacities significantly greater than one bit are possible, so that transcriptional regulation need not be limited to simple &quot;on-off&quot; components. The question whether real systems actually exploit this richer possibility is beyond the scope of this investigation."}],"issue":"1","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","year":"2008","_id":"3739","quality_controlled":0,"author":[{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Gasper Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"first_name":"Curtis","last_name":"Callan","full_name":"Callan,Curtis G"},{"first_name":"William","full_name":"Bialek, William S","last_name":"Bialek"}],"date_created":"2018-12-11T12:04:54Z","citation":{"short":"G. Tkačik, C. Callan, W. Bialek, Physical Review E Statistical Nonlinear and Soft Matter Physics 78 (2008).","ista":"Tkačik G, Callan C, Bialek W. 2008. Information capacity of genetic regulatory elements. Physical Review E Statistical Nonlinear and Soft Matter Physics. 78(1).","ama":"Tkačik G, Callan C, Bialek W. Information capacity of genetic regulatory elements. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2008;78(1). doi:<a href=\"https://doi.org/10.1103/PhysRevE.78.011910\">10.1103/PhysRevE.78.011910</a>","chicago":"Tkačik, Gašper, Curtis Callan, and William Bialek. “Information Capacity of Genetic Regulatory Elements.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2008. <a href=\"https://doi.org/10.1103/PhysRevE.78.011910\">https://doi.org/10.1103/PhysRevE.78.011910</a>.","ieee":"G. Tkačik, C. Callan, and W. Bialek, “Information capacity of genetic regulatory elements,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 78, no. 1. American Institute of Physics, 2008.","mla":"Tkačik, Gašper, et al. “Information Capacity of Genetic Regulatory Elements.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 78, no. 1, American Institute of Physics, 2008, doi:<a href=\"https://doi.org/10.1103/PhysRevE.78.011910\">10.1103/PhysRevE.78.011910</a>.","apa":"Tkačik, G., Callan, C., &#38; Bialek, W. (2008). Information capacity of genetic regulatory elements. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.78.011910\">https://doi.org/10.1103/PhysRevE.78.011910</a>"},"volume":78,"month":"07","title":"Information capacity of genetic regulatory elements","date_published":"2008-07-21T00:00:00Z"},{"year":"2008","page":"12265 - 12270","_id":"3740","day":"01","abstract":[{"lang":"eng","text":"In the simplest view of transcriptional regulation, the expression of a gene is turned on or off by changes in the concentration of a transcription factor (TF). We use recent data on noise levels in gene expression to show that it should be possible to transmit much more than just one regulatory bit. Realizing this optimal information capacity would require that the dynamic range of TF concentrations used by the cell, the input/output relation of the regulatory module, and the noise in gene expression satisfy certain matching relations, which we derive. These results provide parameter-free, quantitative predictions connecting independently measurable quantities. Although we have considered only the simplified problem of a single gene responding to a single TF, we find that these predictions are in surprisingly good agreement with recent experiments on the Bicoid/Hunchback system in the early Drosophila embryo and that this system achieves approximately 90% of its theoretical maximum information transmission."}],"issue":"34","publication":"PNAS","intvolume":"       105","publisher":"National Academy of Sciences","publication_status":"published","type":"journal_article","status":"public","extern":1,"date_updated":"2021-01-12T07:51:52Z","publist_id":"2489","doi":"10.1073/pnas.0806077105","acknowledgement":"P50 GM071508/GM/NIGMS NIH HHS/United States; R01 GM077599/GM/NIGMS NIH HHS/United States","date_published":"2008-01-01T00:00:00Z","title":"Information flow and optimization in transcriptional regulation","citation":{"ama":"Tkačik G, Callan C, Bialek W. Information flow and optimization in transcriptional regulation. <i>PNAS</i>. 2008;105(34):12265-12270. doi:<a href=\"https://doi.org/10.1073/pnas.0806077105\">10.1073/pnas.0806077105</a>","chicago":"Tkačik, Gašper, Curtis Callan, and William Bialek. “Information Flow and Optimization in Transcriptional Regulation.” <i>PNAS</i>. National Academy of Sciences, 2008. <a href=\"https://doi.org/10.1073/pnas.0806077105\">https://doi.org/10.1073/pnas.0806077105</a>.","ieee":"G. Tkačik, C. Callan, and W. Bialek, “Information flow and optimization in transcriptional regulation,” <i>PNAS</i>, vol. 105, no. 34. National Academy of Sciences, pp. 12265–12270, 2008.","mla":"Tkačik, Gašper, et al. “Information Flow and Optimization in Transcriptional Regulation.” <i>PNAS</i>, vol. 105, no. 34, National Academy of Sciences, 2008, pp. 12265–70, doi:<a href=\"https://doi.org/10.1073/pnas.0806077105\">10.1073/pnas.0806077105</a>.","apa":"Tkačik, G., Callan, C., &#38; Bialek, W. (2008). Information flow and optimization in transcriptional regulation. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.0806077105\">https://doi.org/10.1073/pnas.0806077105</a>","short":"G. Tkačik, C. Callan, W. Bialek, PNAS 105 (2008) 12265–12270.","ista":"Tkačik G, Callan C, Bialek W. 2008. Information flow and optimization in transcriptional regulation. PNAS. 105(34), 12265–12270."},"volume":105,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527900"}],"month":"01","oa":1,"date_created":"2018-12-11T12:04:54Z","quality_controlled":0,"author":[{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Gasper Tkacik","first_name":"Gasper","last_name":"Tkacik"},{"last_name":"Callan","full_name":"Callan,Curtis G","first_name":"Curtis"},{"first_name":"William","full_name":"Bialek, William S","last_name":"Bialek"}]},{"date_created":"2018-12-11T12:04:56Z","author":[{"last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Gasper Tkacik","orcid":"0000-0002-6699-1455"},{"last_name":"Magnasco","full_name":"Magnasco, Marcelo O","first_name":"Marcelo"}],"quality_controlled":0,"date_published":"2008-07-01T00:00:00Z","title":"Decoding spike timing: The differential reverse-correlation method","month":"07","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2792887","open_access":"1"}],"volume":93,"oa":1,"citation":{"ieee":"G. Tkačik and M. Magnasco, “Decoding spike timing: The differential reverse-correlation method,” <i>Biosystems</i>, vol. 93, no. 1–2. Elsevier, pp. 90–100, 2008.","mla":"Tkačik, Gašper, and Marcelo Magnasco. “Decoding Spike Timing: The Differential Reverse-Correlation Method.” <i>Biosystems</i>, vol. 93, no. 1–2, Elsevier, 2008, pp. 90–100, doi:<a href=\"https://doi.org/10.1016/j.biosystems.2008.04.011\">10.1016/j.biosystems.2008.04.011</a>.","apa":"Tkačik, G., &#38; Magnasco, M. (2008). Decoding spike timing: The differential reverse-correlation method. <i>Biosystems</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.biosystems.2008.04.011\">https://doi.org/10.1016/j.biosystems.2008.04.011</a>","ama":"Tkačik G, Magnasco M. Decoding spike timing: The differential reverse-correlation method. <i>Biosystems</i>. 2008;93(1-2):90-100. doi:<a href=\"https://doi.org/10.1016/j.biosystems.2008.04.011\">10.1016/j.biosystems.2008.04.011</a>","chicago":"Tkačik, Gašper, and Marcelo Magnasco. “Decoding Spike Timing: The Differential Reverse-Correlation Method.” <i>Biosystems</i>. Elsevier, 2008. <a href=\"https://doi.org/10.1016/j.biosystems.2008.04.011\">https://doi.org/10.1016/j.biosystems.2008.04.011</a>.","short":"G. Tkačik, M. Magnasco, Biosystems 93 (2008) 90–100.","ista":"Tkačik G, Magnasco M. 2008. Decoding spike timing: The differential reverse-correlation method. Biosystems. 93(1–2), 90–100."},"publisher":"Elsevier","publication_status":"published","status":"public","extern":1,"type":"journal_article","date_updated":"2021-01-12T07:51:53Z","intvolume":"        93","doi":"10.1016/j.biosystems.2008.04.011","publist_id":"2482","page":"90 - 100","_id":"3744","year":"2008","publication":"Biosystems","issue":"1-2","abstract":[{"text":"It is widely acknowledged that detailed timing of action potentials is used to encode information, for example, in auditory pathways; however, the computational tools required to analyze encoding through timing are still in their infancy. We present a simple example of encoding, based on a recent model of time-frequency analysis, in which units fire action potentials when a certain condition is met, but the timing of the action potential depends also on other features of the stimulus. We show that, as a result, spike-triggered averages are smoothed so much that they do not represent the true features of the encoding. Inspired by this example, we present a simple method, differential reverse correlations, that can separate an analysis of what causes a neuron to spike, and what controls its timing. We analyze with this method the leaky integrate-and-fire neuron and show the method accurately reconstructs the model's kernel.","lang":"eng"}],"day":"01"},{"month":"04","volume":3,"oa":1,"citation":{"ista":"Kinkhabwala A, Guet CC. 2008. Uncovering cis regulatory codes using synthetic promoter shuffling. PLoS One. 3(4), e2030.","short":"A. Kinkhabwala, C.C. Guet, PLoS One 3 (2008).","chicago":"Kinkhabwala, Ali, and Calin C Guet. “Uncovering Cis Regulatory Codes Using Synthetic Promoter Shuffling.” <i>PLoS One</i>. Public Library of Science, 2008. <a href=\"https://doi.org/10.1371/journal.pone.0002030\">https://doi.org/10.1371/journal.pone.0002030</a>.","ama":"Kinkhabwala A, Guet CC. Uncovering cis regulatory codes using synthetic promoter shuffling. <i>PLoS One</i>. 2008;3(4). doi:<a href=\"https://doi.org/10.1371/journal.pone.0002030\">10.1371/journal.pone.0002030</a>","apa":"Kinkhabwala, A., &#38; Guet, C. C. (2008). Uncovering cis regulatory codes using synthetic promoter shuffling. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0002030\">https://doi.org/10.1371/journal.pone.0002030</a>","mla":"Kinkhabwala, Ali, and Calin C. Guet. “Uncovering Cis Regulatory Codes Using Synthetic Promoter Shuffling.” <i>PLoS One</i>, vol. 3, no. 4, e2030, Public Library of Science, 2008, doi:<a href=\"https://doi.org/10.1371/journal.pone.0002030\">10.1371/journal.pone.0002030</a>.","ieee":"A. Kinkhabwala and C. C. Guet, “Uncovering cis regulatory codes using synthetic promoter shuffling,” <i>PLoS One</i>, vol. 3, no. 4. Public Library of Science, 2008."},"file_date_updated":"2020-07-14T12:46:15Z","date_published":"2008-04-30T00:00:00Z","language":[{"iso":"eng"}],"quality_controlled":"1","date_created":"2018-12-11T12:04:58Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"3751","pmid":1,"year":"2008","doi":"10.1371/journal.pone.0002030","publist_id":"2477","publisher":"Public Library of Science","status":"public","type":"journal_article","date_updated":"2021-01-12T07:51:56Z","intvolume":"         3","file":[{"file_name":"2008_PLOS1_Kinkhabwala.PDF","creator":"dernst","checksum":"42c26f8337298a9ecadbe34a16139466","file_id":"6400","date_created":"2019-05-10T11:00:36Z","date_updated":"2020-07-14T12:46:15Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_size":679786}],"tmp":{"image":"/images/cc_0.png","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"title":"Uncovering cis regulatory codes using synthetic promoter shuffling","author":[{"first_name":"Ali","last_name":"Kinkhabwala","full_name":"Kinkhabwala, Ali"},{"orcid":"0000-0001-6220-2052","first_name":"Calin C","last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"}],"license":"https://creativecommons.org/publicdomain/zero/1.0/","external_id":{"pmid":["18446205"]},"issue":"4","publication":"PLoS One","day":"30","ddc":["570"],"abstract":[{"lang":"eng","text":"Revealing the spectrum of combinatorial regulation of transcription at individual promoters is essential for understanding the complex structure of biological networks. However, the computations represented by the integration of various molecular signals at complex promoters are difficult to decipher in the absence of simple cis regulatory codes. Here we synthetically shuffle the regulatory architecture-operator sequences binding activators and repressors-of a canonical bacterial promoter. The resulting library of complex promoters allows for rapid exploration of promoter encoded logic regulation. Among all possible logic functions, NOR and ANDN promoter encoded logics predominate. A simple transcriptional cis regulatory code determines both logics, establishing a straightforward map between promoter structure and logic phenotype. The regulatory code is determined solely by the type of transcriptional regulation combinations: two repressors generate a NOR: NOT (a OR b) whereas a repressor and an activator generate an ANDN: a AND NOT b. Three-input versions of both logics, having an additional repressor as an input, are also present in the library. The resulting complex promoters cover a wide dynamic range of transcriptional strengths. Synthetic promoter shuffling represents a fast and efficient method for exploring the spectrum of complex regulatory functions that can be encoded by complex promoters. From an engineering point of view, synthetic promoter shuffling enables the experimental testing of the functional properties of complex promoters that cannot necessarily be inferred ab initio from the known properties of the individual genetic components. Synthetic promoter shuffling may provide a useful experimental tool for studying naturally occurring promoter shuffling."}],"oa_version":"Published Version","article_number":"e2030","has_accepted_license":"1","publication_status":"published","extern":"1"},{"publist_id":"2474","doi":"10.1093/nar/gkn329","acknowledgement":"PMCID: PMC2475643 ","intvolume":"        36","extern":1,"publisher":"Oxford University Press","status":"public","type":"journal_article","publication_status":"published","date_updated":"2021-01-12T07:51:57Z","abstract":[{"text":"Fluorescence correlation spectroscopy (FCS) has permitted the characterization of high concentrations of noncoding RNAs in a single living bacterium. Here, we extend the use of FCS to low concentrations of coding RNAs in single living cells. We genetically fuse a red fluorescent protein (RFP) gene and two binding sites for an RNA-binding protein, whose translated product is the RFP protein alone. Using this construct, we determine in single cells both the absolute [mRNA] concentration and the associated [RFP] expressed from an inducible plasmid. We find that the FCS method allows us to reliably monitor in real-time [mRNA] down to similar to 40 nM (i.e. approximately two transcripts per volume of detection). To validate these measurements, we show that [mRNA] is proportional to the associated expression of the RFP protein. This FCS-based technique establishes a framework for minimally invasive measurements of mRNA concentration in individual living bacteria.","lang":"eng"}],"day":"01","issue":"12","publication":"Nucleic Acids Research","year":"2008","_id":"3754","quality_controlled":0,"author":[{"orcid":"0000-0001-6220-2052","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","full_name":"Calin Guet","last_name":"Guet"},{"full_name":"Bruneaux,Luke","first_name":"Luke","last_name":"Bruneaux"},{"full_name":"Min,Taejin L","first_name":"Taejin","last_name":"Min"},{"first_name":"Dan","full_name":"Siegal-Gaskins,Dan","last_name":"Siegal Gaskins"},{"first_name":"Israel","last_name":"Figueroa","full_name":"Figueroa,Israel"},{"full_name":"Emonet,Thierry","last_name":"Emonet","first_name":"Thierry"},{"full_name":"Cluzel,Philippe","last_name":"Cluzel","first_name":"Philippe"}],"license":"https://creativecommons.org/licenses/by-nc/4.0/","date_created":"2018-12-11T12:04:59Z","tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)"},"citation":{"ieee":"C. C. Guet <i>et al.</i>, “Minimally invasive determination of mRNA concentration in single living bacteria,” <i>Nucleic Acids Research</i>, vol. 36, no. 12. Oxford University Press, 2008.","mla":"Guet, Calin C., et al. “Minimally Invasive Determination of MRNA Concentration in Single Living Bacteria.” <i>Nucleic Acids Research</i>, vol. 36, no. 12, Oxford University Press, 2008, doi:<a href=\"https://doi.org/10.1093/nar/gkn329\">10.1093/nar/gkn329</a>.","apa":"Guet, C. C., Bruneaux, L., Min, T., Siegal Gaskins, D., Figueroa, I., Emonet, T., &#38; Cluzel, P. (2008). Minimally invasive determination of mRNA concentration in single living bacteria. <i>Nucleic Acids Research</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/nar/gkn329\">https://doi.org/10.1093/nar/gkn329</a>","chicago":"Guet, Calin C, Luke Bruneaux, Taejin Min, Dan Siegal Gaskins, Israel Figueroa, Thierry Emonet, and Philippe Cluzel. “Minimally Invasive Determination of MRNA Concentration in Single Living Bacteria.” <i>Nucleic Acids Research</i>. Oxford University Press, 2008. <a href=\"https://doi.org/10.1093/nar/gkn329\">https://doi.org/10.1093/nar/gkn329</a>.","ama":"Guet CC, Bruneaux L, Min T, et al. Minimally invasive determination of mRNA concentration in single living bacteria. <i>Nucleic Acids Research</i>. 2008;36(12). doi:<a href=\"https://doi.org/10.1093/nar/gkn329\">10.1093/nar/gkn329</a>","short":"C.C. Guet, L. Bruneaux, T. Min, D. Siegal Gaskins, I. Figueroa, T. Emonet, P. Cluzel, Nucleic Acids Research 36 (2008).","ista":"Guet CC, Bruneaux L, Min T, Siegal Gaskins D, Figueroa I, Emonet T, Cluzel P. 2008. Minimally invasive determination of mRNA concentration in single living bacteria. Nucleic Acids Research. 36(12)."},"month":"01","volume":36,"date_published":"2008-01-01T00:00:00Z","title":"Minimally invasive determination of mRNA concentration in single living bacteria"},{"date_published":"2008-08-01T00:00:00Z","title":"Fast viscoelastic behavior with thin features","main_file_link":[{"url":"http://www.cc.gatech.edu/~turk/my_papers/fast_goop_2008.pdf"}],"month":"08","volume":27,"article_processing_charge":"No","citation":{"ama":"Wojtan C, Turk G. Fast viscoelastic behavior with thin features. <i>ACM Transactions on Graphics</i>. 2008;27(3). doi:<a href=\"https://doi.org/10.1145/1360612.1360646\">10.1145/1360612.1360646</a>","chicago":"Wojtan, Chris, and Greg Turk. “Fast Viscoelastic Behavior with Thin Features.” <i>ACM Transactions on Graphics</i>. ACM, 2008. <a href=\"https://doi.org/10.1145/1360612.1360646\">https://doi.org/10.1145/1360612.1360646</a>.","apa":"Wojtan, C., &#38; Turk, G. (2008). Fast viscoelastic behavior with thin features. <i>ACM Transactions on Graphics</i>. ACM. <a href=\"https://doi.org/10.1145/1360612.1360646\">https://doi.org/10.1145/1360612.1360646</a>","ieee":"C. Wojtan and G. Turk, “Fast viscoelastic behavior with thin features,” <i>ACM Transactions on Graphics</i>, vol. 27, no. 3. ACM, 2008.","mla":"Wojtan, Chris, and Greg Turk. “Fast Viscoelastic Behavior with Thin Features.” <i>ACM Transactions on Graphics</i>, vol. 27, no. 3, ACM, 2008, doi:<a href=\"https://doi.org/10.1145/1360612.1360646\">10.1145/1360612.1360646</a>.","ista":"Wojtan C, Turk G. 2008. Fast viscoelastic behavior with thin features. ACM Transactions on Graphics. 27(3).","short":"C. Wojtan, G. Turk, ACM Transactions on Graphics 27 (2008)."},"date_created":"2018-12-11T12:05:01Z","author":[{"id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87","full_name":"Wojtan, Christopher J","last_name":"Wojtan","first_name":"Christopher J","orcid":"0000-0001-6646-5546"},{"full_name":"Turk, Greg","last_name":"Turk","first_name":"Greg"}],"language":[{"iso":"eng"}],"_id":"3760","year":"2008","oa_version":"None","issue":"3","publication":"ACM Transactions on Graphics","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"We introduce a method for efficiently animating a wide range of deformable materials. We combine a high resolution surface mesh with a tetrahedral finite element simulator that makes use of frequent re-meshing. This combination allows for fast and detailed simulations of complex elastic and plastic behavior. We significantly expand the range of physical parameters that can be simulated with a single technique, and the results are free from common artifacts such as volume-loss, smoothing, popping, and the absence of thin features like strands and sheets. Our decision to couple a high resolution surface with low-resolution physics leads to efficient simulation and detailed surface features, and our approach to creating the tetrahedral mesh leads to an order-of-magnitude speedup over previous techniques in the time spent re-meshing. We compute masses, collisions, and surface tension forces on the scale of the fine mesh, which helps avoid visual artifacts due to the differing mesh resolutions. The result is a method that can simulate a large array of different material behaviors with high resolution features in a short amount of time.","lang":"eng"}],"day":"01","status":"public","publication_status":"published","extern":"1","publisher":"ACM","type":"journal_article","date_updated":"2023-02-23T11:41:29Z","intvolume":"        27","doi":"10.1145/1360612.1360646","publist_id":"2467"},{"date_updated":"2021-01-12T07:52:04Z","type":"journal_article","publisher":"Mary Ann Liebert","extern":1,"publication_status":"published","status":"public","intvolume":"        15","acknowledgement":"10.1089/cmb.2008.0068","doi":"4200","publist_id":"2458","_id":"3769","page":"577 - 591","year":"2008","issue":"6","publication":"Journal of Computational Biology","abstract":[{"lang":"eng","text":"The geometrical representation of the space of phylogenetic trees implies a metric on the space of weighted trees. This metric, the geodesic distance, is the length of the shortest path through that space. We present an exact algorithm to compute this metric. For biologically reasonable trees, the implementation allows fast computations of the geodesic distance, although the running time of the algorithm is worst-case exponential. The algorithm was applied to pairs of 118 gene trees of the metazoa. The results show that a special path in tree space, the cone path, which can be computed in linear time, is a good approximation of the geodesic distance. The program GeoMeTree is a python implementation of the geodesic distance, and it is approximations and is available from www.cibiv.at/software/geometree."}],"day":"01","date_created":"2018-12-11T12:05:04Z","author":[{"id":"2BB22BC2-F248-11E8-B48F-1D18A9856A87","last_name":"Kupczok","first_name":"Anne","full_name":"Anne Kupczok"},{"full_name":"von Haeseler,Arndt","last_name":"Von Haeseler","first_name":"Arndt"},{"first_name":"Steffen","full_name":"Klaere,Steffen","last_name":"Klaere"}],"quality_controlled":0,"title":"An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees.","date_published":"2008-01-01T00:00:00Z","volume":15,"month":"01","citation":{"short":"A. Kupczok, A. Von Haeseler, S. Klaere, Journal of Computational Biology 15 (2008) 577–591.","ista":"Kupczok A, Von Haeseler A, Klaere S. 2008. An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees. Journal of Computational Biology. 15(6), 577–591.","chicago":"Kupczok, Anne, Arndt Von Haeseler, and Steffen Klaere. “An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees.” <i>Journal of Computational Biology</i>. Mary Ann Liebert, 2008. <a href=\"https://doi.org/4200\">https://doi.org/4200</a>.","ama":"Kupczok A, Von Haeseler A, Klaere S. An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees. <i>Journal of Computational Biology</i>. 2008;15(6):577-591. doi:<a href=\"https://doi.org/4200\">4200</a>","ieee":"A. Kupczok, A. Von Haeseler, and S. Klaere, “An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees.,” <i>Journal of Computational Biology</i>, vol. 15, no. 6. Mary Ann Liebert, pp. 577–591, 2008.","mla":"Kupczok, Anne, et al. “An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees.” <i>Journal of Computational Biology</i>, vol. 15, no. 6, Mary Ann Liebert, 2008, pp. 577–91, doi:<a href=\"https://doi.org/4200\">4200</a>.","apa":"Kupczok, A., Von Haeseler, A., &#38; Klaere, S. (2008). An Exact Algorithm for the Geodesic Distance between Phylogenetic Trees. <i>Journal of Computational Biology</i>. Mary Ann Liebert. <a href=\"https://doi.org/4200\">https://doi.org/4200</a>"}},{"title":"Action potential initiation and propagation in hippocampal mossy fibre axons","date_published":"2008-01-01T00:00:00Z","oa":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375716/","open_access":"1"}],"volume":586,"month":"01","citation":{"ama":"Schmidt Hieber C, Jonas PM, Bischofberger J. Action potential initiation and propagation in hippocampal mossy fibre axons. <i>Journal of Physiology</i>. 2008;586(7):1849-1857. doi:<a href=\"https://doi.org/10.1113/jphysiol.2007.150151 \">10.1113/jphysiol.2007.150151 </a>","chicago":"Schmidt Hieber, Christoph, Peter M Jonas, and Josef Bischofberger. “Action Potential Initiation and Propagation in Hippocampal Mossy Fibre Axons.” <i>Journal of Physiology</i>. Wiley-Blackwell, 2008. <a href=\"https://doi.org/10.1113/jphysiol.2007.150151 \">https://doi.org/10.1113/jphysiol.2007.150151 </a>.","mla":"Schmidt Hieber, Christoph, et al. “Action Potential Initiation and Propagation in Hippocampal Mossy Fibre Axons.” <i>Journal of Physiology</i>, vol. 586, no. 7, Wiley-Blackwell, 2008, pp. 1849–57, doi:<a href=\"https://doi.org/10.1113/jphysiol.2007.150151 \">10.1113/jphysiol.2007.150151 </a>.","ieee":"C. Schmidt Hieber, P. M. Jonas, and J. Bischofberger, “Action potential initiation and propagation in hippocampal mossy fibre axons,” <i>Journal of Physiology</i>, vol. 586, no. 7. Wiley-Blackwell, pp. 1849–57, 2008.","apa":"Schmidt Hieber, C., Jonas, P. M., &#38; Bischofberger, J. (2008). Action potential initiation and propagation in hippocampal mossy fibre axons. <i>Journal of Physiology</i>. Wiley-Blackwell. <a href=\"https://doi.org/10.1113/jphysiol.2007.150151 \">https://doi.org/10.1113/jphysiol.2007.150151 </a>","short":"C. Schmidt Hieber, P.M. Jonas, J. Bischofberger, Journal of Physiology 586 (2008) 1849–57.","ista":"Schmidt Hieber C, Jonas PM, Bischofberger J. 2008. Action potential initiation and propagation in hippocampal mossy fibre axons. Journal of Physiology. 586(7), 1849–57."},"date_created":"2018-12-11T12:05:21Z","author":[{"full_name":"Schmidt-Hieber, Christoph","first_name":"Christoph","last_name":"Schmidt Hieber"},{"first_name":"Peter M","last_name":"Jonas","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","full_name":"Peter Jonas","orcid":"0000-0001-5001-4804"},{"last_name":"Bischofberger","full_name":"Bischofberger, Josef","first_name":"Josef"}],"quality_controlled":0,"_id":"3822","page":"1849 - 57","year":"2008","publication":"Journal of Physiology","issue":"7","abstract":[{"text":"Dentate gyrus granule cells transmit action potentials (APs) along their unmyelinated mossy fibre axons to the CA3 region. Although the initiation and propagation of APs are fundamental steps during neural computation, little is known about the site of AP initiation and the speed of propagation in mossy fibre axons. To address these questions, we performed simultaneous somatic and axonal whole-cell recordings from granule cells in acute hippocampal slices of adult mice at approximately 23 degrees C. Injection of short current pulses or synaptic stimulation evoked axonal and somatic APs with similar amplitudes. By contrast, the time course was significantly different, as axonal APs had a higher maximal rate of rise (464 +/- 30 V s(-1) in the axon versus 297 +/- 12 V s(-1) in the soma, mean +/- s.e.m.). Furthermore, analysis of latencies between the axonal and somatic signals showed that APs were initiated in the proximal axon at approximately 20-30 mum distance from the soma, and propagated orthodromically with a velocity of 0.24 m s(-1). Qualitatively similar results were obtained at a recording temperature of approximately 34 degrees C. Modelling of AP propagation in detailed cable models of granule cells suggested that a approximately 4 times higher Na(+) channel density ( approximately 1000 pS mum(-2)) in the axon might account for both the higher rate of rise of axonal APs and the robust AP initiation in the proximal mossy fibre axon. This may be of critical importance to separate dendritic integration of thousands of synaptic inputs from the generation and transmission of a common AP output.","lang":"eng"}],"day":"01","date_updated":"2021-01-12T07:52:27Z","extern":1,"publisher":"Wiley-Blackwell","type":"journal_article","publication_status":"published","status":"public","intvolume":"       586","doi":"10.1113/jphysiol.2007.150151 ","publist_id":"2387"},{"title":"The two sides of hippocampal mossy fiber plasticity (Review)","date_published":"2008-01-10T00:00:00Z","month":"01","volume":57,"citation":{"chicago":"Kerr, Angharad, and Peter M Jonas. “The Two Sides of Hippocampal Mossy Fiber Plasticity (Review).” <i>Neuron</i>. Elsevier, 2008. <a href=\"https://doi.org/10.1016/j.neuron.2007.12.015\">https://doi.org/10.1016/j.neuron.2007.12.015</a>.","ama":"Kerr A, Jonas PM. The two sides of hippocampal mossy fiber plasticity (Review). <i>Neuron</i>. 2008;57(1):5-7. doi:<a href=\"https://doi.org/10.1016/j.neuron.2007.12.015\">10.1016/j.neuron.2007.12.015</a>","mla":"Kerr, Angharad, and Peter M. Jonas. “The Two Sides of Hippocampal Mossy Fiber Plasticity (Review).” <i>Neuron</i>, vol. 57, no. 1, Elsevier, 2008, pp. 5–7, doi:<a href=\"https://doi.org/10.1016/j.neuron.2007.12.015\">10.1016/j.neuron.2007.12.015</a>.","ieee":"A. Kerr and P. M. Jonas, “The two sides of hippocampal mossy fiber plasticity (Review),” <i>Neuron</i>, vol. 57, no. 1. Elsevier, pp. 5–7, 2008.","apa":"Kerr, A., &#38; Jonas, P. M. (2008). The two sides of hippocampal mossy fiber plasticity (Review). <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2007.12.015\">https://doi.org/10.1016/j.neuron.2007.12.015</a>","short":"A. Kerr, P.M. Jonas, Neuron 57 (2008) 5–7.","ista":"Kerr A, Jonas PM. 2008. The two sides of hippocampal mossy fiber plasticity (Review). Neuron. 57(1), 5–7."},"date_created":"2018-12-11T12:05:22Z","author":[{"full_name":"Kerr, Angharad M","first_name":"Angharad","last_name":"Kerr"},{"last_name":"Jonas","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","first_name":"Peter M","full_name":"Peter Jonas","orcid":"0000-0001-5001-4804"}],"quality_controlled":0,"_id":"3823","page":"5 - 7","year":"2008","issue":"1","publication":"Neuron","day":"10","abstract":[{"lang":"eng","text":"Two studies in this issue of Neuron (Kwon and Castillo and Rebola et al.) show that the mossy fiber-CA3 pyramidal neuron synapse, a hippocampal synapse well known for its presynaptic plasticity, exhibits a novel form of long-term potentiation of NMDAR-mediated currents, which is induced and expressed postsynaptically."}],"date_updated":"2021-01-12T07:52:27Z","publisher":"Elsevier","extern":1,"publication_status":"published","status":"public","type":"journal_article","intvolume":"        57","doi":"10.1016/j.neuron.2007.12.015","publist_id":"2388"}]