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1. Zero-shot detection of buildings in mobile LiDAR using Language Vision Model

Author

Goo, June Moh, Zeng, Zichao, and Boehm, Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Recent advances have demonstrated that Language Vision Models (LVMs) surpass the existing State-of-the-Art (SOTA) in two-dimensional (2D) computer vision tasks, motivating attempts to apply LVMs to three-dimensional (3D) data. While LVMs are efficient and effective in addressing various downstream 2D vision tasks without training, they face significant challenges when it comes to point clouds, a representative format for representing 3D data. It is more difficult to extract features from 3D data and there are challenges due to large data sizes and the cost of the collection and labelling, resulting in a notably limited availability of datasets. Moreover, constructing LVMs for point clouds is even more challenging due to the requirements for large amounts of data and training time. To address these issues, our research aims to 1) apply the Grounded SAM through Spherical Projection to transfer 3D to 2D, and 2) experiment with synthetic data to evaluate its effectiveness in bridging the gap between synthetic and real-world data domains. Our approach exhibited high performance with an accuracy of 0.96, an IoU of 0.85, precision of 0.92, recall of 0.91, and an F1 score of 0.92, confirming its potential. However, challenges such as occlusion problems and pixel-level overlaps of multi-label points during spherical image generation remain to be addressed in future studies., Comment: 7 pages, 6 figures, conference

Published
2024

2. Zero-shot Building Age Classification from Facade Image Using GPT-4

Author

Zeng, Zichao, Goo, June Moh, Wang, Xinglei, Chi, Bin, Wang, Meihui, and Boehm, Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

A building's age of construction is crucial for supporting many geospatial applications. Much current research focuses on estimating building age from facade images using deep learning. However, building an accurate deep learning model requires a considerable amount of labelled training data, and the trained models often have geographical constraints. Recently, large pre-trained vision language models (VLMs) such as GPT-4 Vision, which demonstrate significant generalisation capabilities, have emerged as potential training-free tools for dealing with specific vision tasks, but their applicability and reliability for building information remain unexplored. In this study, a zero-shot building age classifier for facade images is developed using prompts that include logical instructions. Taking London as a test case, we introduce a new dataset, FI-London, comprising facade images and building age epochs. Although the training-free classifier achieved a modest accuracy of 39.69%, the mean absolute error of 0.85 decades indicates that the model can predict building age epochs successfully albeit with a small bias. The ensuing discussion reveals that the classifier struggles to predict the age of very old buildings and is challenged by fine-grained predictions within 2 decades. Overall, the classifier utilising GPT-4 Vision is capable of predicting the rough age epoch of a building from a single facade image without any training.

Published
2024

3. Self-supervised Visualisation of Medical Image Datasets

Author

Nwabufo, Ifeoma Veronica, Böhm, Jan Niklas, Berens, Philipp, and Kobak, Dmitry

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervised learning methods based on data augmentations, such as SimCLR, BYOL, or DINO, allow obtaining semantically meaningful representations of image datasets and are widely used prior to supervised fine-tuning. A recent self-supervised learning method, $t$-SimCNE, uses contrastive learning to directly train a 2D representation suitable for visualisation. When applied to natural image datasets, $t$-SimCNE yields 2D visualisations with semantically meaningful clusters. In this work, we used $t$-SimCNE to visualise medical image datasets, including examples from dermatology, histology, and blood microscopy. We found that increasing the set of data augmentations to include arbitrary rotations improved the results in terms of class separability, compared to data augmentations used for natural images. Our 2D representations show medically relevant structures and can be used to aid data exploration and annotation, improving on common approaches for data visualisation.

Published
2024

5. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Author

Chen, Zhishan, Guo, Xingyi, Tao, Ran, Huyghe, Jeroen R., Law, Philip J., Fernandez-Rozadilla, Ceres, Ping, Jie, Jia, Guochong, Long, Jirong, Li, Chao, Shen, Quanhu, Xie, Yuhan, Timofeeva, Maria N., Thomas, Minta, Schmit, Stephanie L., Díez-Obrero, Virginia, Devall, Matthew, Moratalla-Navarro, Ferran, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah E. W., Svinti, Victoria, Donnelly, Kevin, Farrington, Susan M., Blackmur, James, Vaughan-Shaw, Peter G., Shu, Xiao-Ou, Lu, Yingchang, Broderick, Peter, Studd, James, Harrison, Tabitha A., Conti, David V., Schumacher, Fredrick R., Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John L., Jenkins, Mark A., Win, Aung Ko, Pai, Rish K., Figueiredo, Jane C., Haile, Robert W., Gallinger, Steven, Woods, Michael O., Newcomb, Polly A., Duggan, David, Cheadle, Jeremy P., Kaplan, Richard, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Jukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri A., Rissanen, Harri, Pukkala, Eero, Eriksson, Johan G., Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie J., Ruiz-Narvaez, Edward, Palmer, Julie R., Buchanan, Daniel D., Platz, Elizabeth A., Visvanathan, Kala, Ulrich, Cornelia M., Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter T., Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha L., Potter, John D., Tsilidis, Kostas K., Schulze, Matthias B., Gunter, Marc J., Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Bishop, D. Timothy, Giles, Graham G., Southey, Melissa C., Idos, Gregory E., McDonnell, Kevin J., Abu-Ful, Zomoroda, Greenson, Joel K., Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope O., van Guelpen, Bethany, Hudson, Thomas J., Hampel, Heather, Pearlman, Rachel, Berndt, Sonja I., Hayes, Richard B., Martinez, Marie Elena, Thomas, Sushma S., Pharoah, Paul D. P., Larsson, Susanna C., Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly F., Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew T., Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David J., Joshi, Amit D., Schafmayer, Clemens, Scacheri, Peter C., Kundaje, Anshul, Schoen, Robert E., Hampe, Jochen, Stadler, Zsofia K., Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Edlund, Christopher K., Gauderman, W. James, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen J., van Duijnhoven, Franzel, Feskens, Edith J. M., Sakoda, Lori C., Gago-Dominguez, Manuela, Wolk, Alicja, Pardini, Barbara, FitzGerald, Liesel M., Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie A., Kooperberg, Charles, Li, Christopher I., Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Le Marchand, Loic, Wu, Anna H., Qu, Chenxu, McNeil, Caroline E., Coetzee, Gerhard, Hayward, Caroline, Deary, Ian J., Harris, Sarah E., Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Lau, Ken S., Zhao, Hongyu, Hsu, Li, Cai, Qiuyin, Dunlop, Malcolm G., Gruber, Stephen B., Houlston, Richard S., Moreno, Victor, Casey, Graham, Peters, Ulrike, Tomlinson, Ian, and Zheng, Wei

Published
2024
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6. Unsupervised visualization of image datasets using contrastive learning

Author

Böhm, Jan Niklas, Berens, Philipp, and Kobak, Dmitry

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers., Comment: ICLR 2023

Published
2022

7. From $t$-SNE to UMAP with contrastive learning

Author

Damrich, Sebastian, Böhm, Jan Niklas, Hamprecht, Fred A., and Kobak, Dmitry

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

Neighbor embedding methods $t$-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions appear to be unrelated. In practice, they yield strongly differing embeddings and can suggest conflicting interpretations of the same data. The fundamental reasons for this and, more generally, the exact relationship between $t$-SNE and UMAP have remained unclear. In this work, we uncover their conceptual connection via a new insight into contrastive learning methods. Noise-contrastive estimation can be used to optimize $t$-SNE, while UMAP relies on negative sampling, another contrastive method. We find the precise relationship between these two contrastive methods and provide a mathematical characterization of the distortion introduced by negative sampling. Visually, this distortion results in UMAP generating more compact embeddings with tighter clusters compared to $t$-SNE. We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings. Moving along this spectrum of embeddings leads to a trade-off between discrete / local and continuous / global structures, mitigating the risk of over-interpreting ostensible features of any single embedding. We provide a PyTorch implementation., Comment: ICLR 2023. 44 pages, 19 figures. Code at https://github.com/hci-unihd/cl-tsne-umap and https://github.com/berenslab/contrastive-ne

Published
2022

12. Curiosity-driven 3D Object Detection Without Labels

Author

Griffiths, David, Boehm, Jan, and Ritschel, Tobias

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper we set out to solve the task of 6-DOF 3D object detection from 2D images, where the only supervision is a geometric representation of the objects we aim to find. In doing so, we remove the need for 6-DOF labels (i.e., position, orientation etc.), allowing our network to be trained on unlabeled images in a self-supervised manner. We achieve this through a neural network which learns an explicit scene parameterization which is subsequently passed into a differentiable renderer. We analyze why analysis-by-synthesis-like losses for supervision of 3D scene structure using differentiable rendering is not practical, as it almost always gets stuck in local minima of visual ambiguities. This can be overcome by a novel form of training, where an additional network is employed to steer the optimization itself to explore the entire parameter space i.e., to be curious, and hence, to resolve those ambiguities and find workable minima., Comment: 19 pages, 17 figures

Published
2020

14. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Author

Fernandez-Rozadilla, Ceres, Timofeeva, Maria, Chen, Zhishan, Law, Philip, Thomas, Minta, Schmit, Stephanie, Díez-Obrero, Virginia, Hsu, Li, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah, Svinti, Victoria, Donnelly, Kevin, Farrington, Susan, Blackmur, James, Vaughan-Shaw, Peter, Shu, Xiao-ou, Long, Jirong, Cai, Qiuyin, Guo, Xingyi, Lu, Yingchang, Broderick, Peter, Studd, James, Huyghe, Jeroen, Harrison, Tabitha, Conti, David, Dampier, Christopher, Devall, Mathew, Schumacher, Fredrick, Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Moratalla-Navarro, Ferran, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John, Jenkins, Mark, Win, Aung Ko, Pai, Rish, Figueiredo, Jane, Haile, Robert, Gallinger, Steven, Woods, Michael, Newcomb, Polly, Duggan, David, Cheadle, Jeremy, Kaplan, Richard, Maughan, Timothy, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Lukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri, Rissanen, Harri, Pukkala, Eero, Eriksson, Johan, Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Zanke, Brent, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie, Ruiz-Narvaez, Edward, Palmer, Julie, Buchanan, Daniel, Platz, Elizabeth, Visvanathan, Kala, Ulrich, Cornelia, Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter, Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha, Potter, John, Tsilidis, Konstantinos, Schulze, Matthias, Gunter, Marc, Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Stern, Mariana, Pardamean, Bens, Bishop, Timothy, Giles, Graham, Southey, Melissa, Idos, Gregory, McDonnell, Kevin, Abu-Ful, Zomoroda, Greenson, Joel, Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope, van Guelpen, Bethany, Hudson, Thomas, Hampel, Heather, Pearlman, Rachel, Berndt, Sonja, Hayes, Richard, Martinez, Marie Elena, Thomas, Sushma, Corley, Douglas, Pharoah, Paul, Larsson, Susanna, Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly, Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew, Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David, Joshi, Amit, Schafmayer, Clemens, Scacheri, Peter, Kundaje, Anshul, Nickerson, Deborah, Schoen, Robert, Hampe, Jochen, Stadler, Zsofia, Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Papadopoulos, Nickolas, Edlund, Chistopher, Gauderman, William, Thomas, Duncan, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen, van Duijnhoven, Franzel, Feskens, Edith, Sakoda, Lori, Gago-Dominguez, Manuela, Wolk, Alicja, Naccarati, Alessio, Pardini, Barbara, FitzGerald, Liesel, Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie, Kooperberg, Charles, Li, Christopher, Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Tangen, Catherine, Mardis, Elaine, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Haiman, Christopher, Le Marchand, Loic, Wu, Anna, Qu, Chenxu, McNeil, Caroline, Coetzee, Gerhard, Hayward, Caroline, Deary, Ian, Harris, Sarah, Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Moreno, Victor, Casey, Graham, Gruber, Stephen, Tomlinson, Ian, Zheng, Wei, Dunlop, Malcolm, Houlston, Richard, and Peters, Ulrike

Published
2023
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15. Attraction-Repulsion Spectrum in Neighbor Embeddings

Author

Böhm, Jan Niklas, Berens, Philipp, and Kobak, Dmitry

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using $k$NN graphs. To find the low-dimensional embedding, these algorithms combine an attractive force between neighboring pairs of points with a repulsive force between all points. One of the most popular examples of such algorithms is t-SNE. Here we empirically show that changing the balance between the attractive and the repulsive forces in t-SNE using the exaggeration parameter yields a spectrum of embeddings, which is characterized by a simple trade-off: stronger attraction can better represent continuous manifold structures, while stronger repulsion can better represent discrete cluster structures and yields higher $k$NN recall. We find that UMAP embeddings correspond to t-SNE with increased attraction; mathematical analysis shows that this is because the negative sampling optimisation strategy employed by UMAP strongly lowers the effective repulsion. Likewise, ForceAtlas2, commonly used for visualizing developmental single-cell transcriptomic data, yields embeddings corresponding to t-SNE with the attraction increased even more. At the extreme of this spectrum lie Laplacian Eigenmaps. Our results demonstrate that many prominent neighbor embedding algorithms can be placed onto the attraction-repulsion spectrum, and highlight the inherent trade-offs between them.

Published
2020

16. Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

Author

Griffiths, David, Boehm, Jan, and Ritschel, Tobias

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled, but regrettably only in isolation, so without context. 3D scenes can be acquired by range scanners on city-level scale, but much fewer with semantic labels. Addressing this disparity, we introduce a new optimization procedure, which allows training for 3D detection with raw 3D scans while using as little as 5% of the object labels and still achieve comparable performance. Our optimization uses two networks. A scene network maps an entire 3D scene to a set of 3D object centers. As we assume the scene not to be labeled by centers, no classic loss, such as Chamfer can be used to train it. Instead, we use another network to emulate the loss. This loss network is trained on a small labeled subset and maps a non centered 3D object in the presence of distractions to its own center. This function is very similar - and hence can be used instead of - the gradient the supervised loss would provide. Our evaluation documents competitive fidelity at a much lower level of supervision, respectively higher quality at comparable supervision. Supplementary material can be found at: https://dgriffiths3.github.io., Comment: 19 pages, 8 figures, Accepted ECCV 2020

Published
2020

17. SynthCity: A large scale synthetic point cloud

Author

Griffiths, David and Boehm, Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

With deep learning becoming a more prominent approach for automatic classification of three-dimensional point cloud data, a key bottleneck is the amount of high quality training data, especially when compared to that available for two-dimensional images. One potential solution is the use of synthetic data for pre-training networks, however the ability for models to generalise from synthetic data to real world data has been poorly studied for point clouds. Despite this, a huge wealth of 3D virtual environments exist which, if proved effective can be exploited. We therefore argue that research in this domain would be of significant use. In this paper we present SynthCity an open dataset to help aid research. SynthCity is a 367.9M point synthetic full colour Mobile Laser Scanning point cloud. Every point is assigned a label from one of nine categories. We generate our point cloud in a typical Urban/Suburban environment using the Blensor plugin for Blender., Comment: 6 pages, 4 figures, dataset white paper

Published
2019

18. A review on deep learning techniques for 3D sensed data classification

Author

Griffiths, David and Boehm, Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches including; RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable., Comment: 25 pages, 9 figures. Review paper

Published
2019
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19. Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance

Author

Griffiths, David and Boehm, Jan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large class im-balance due to the natural class im-balance observed in nature. For example, a 3D scan of an urban environment will consist mostly of road and facade, whereas other objects such as poles will be under-represented. In this paper we address this issue by employing a weighted augmentation to increase classes that contain fewer points. By mitigating the class im-balance present in the data we demonstrate that a standard PointNet++ deep neural network can achieve higher performance at inference on validation data. This was observed as an increase of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and Semantic3D respectively where no class im-balance pre-processing had been performed. Our networks performed better on both highly-represented and under-represented classes, which indicates that the network is learning more robust and meaningful features when the loss function is not overly exposed to only a few classes., Comment: 7 pages, 6 figures, submitted for ISPRS Geospatial Week conference 2019

Published
2019
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23. Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Author

Fernandez-Rozadilla, Ceres, Timofeeva, Maria, Chen, Zhishan, Law, Philip, Thomas, Minta, Schmit, Stephanie, Díez-Obrero, Virginia, Hsu, Li, Fernandez-Tajes, Juan, Palles, Claire, Sherwood, Kitty, Briggs, Sarah, Svinti, Victoria, Donnelly, Kevin, Farrington, Susan, Blackmur, James, Vaughan-Shaw, Peter, Shu, Xiao-ou, Long, Jirong, Cai, Qiuyin, Guo, Xingyi, Lu, Yingchang, Broderick, Peter, Studd, James, Huyghe, Jeroen, Harrison, Tabitha, Conti, David, Dampier, Christopher, Devall, Mathew, Schumacher, Fredrick, Melas, Marilena, Rennert, Gad, Obón-Santacana, Mireia, Martín-Sánchez, Vicente, Moratalla-Navarro, Ferran, Oh, Jae Hwan, Kim, Jeongseon, Jee, Sun Ha, Jung, Keum Ji, Kweon, Sun-Seog, Shin, Min-Ho, Shin, Aesun, Ahn, Yoon-Ok, Kim, Dong-Hyun, Oze, Isao, Wen, Wanqing, Matsuo, Keitaro, Matsuda, Koichi, Tanikawa, Chizu, Ren, Zefang, Gao, Yu-Tang, Jia, Wei-Hua, Hopper, John, Jenkins, Mark, Win, Aung Ko, Pai, Rish, Figueiredo, Jane, Haile, Robert, Gallinger, Steven, Woods, Michael, Newcomb, Polly, Duggan, David, Cheadle, Jeremy, Kaplan, Richard, Maughan, Timothy, Kerr, Rachel, Kerr, David, Kirac, Iva, Böhm, Jan, Mecklin, Lukka-Pekka, Jousilahti, Pekka, Knekt, Paul, Aaltonen, Lauri, Rissanen, Harri, Pukkala, Eero, Eriksson, Johan, Cajuso, Tatiana, Hänninen, Ulrika, Kondelin, Johanna, Palin, Kimmo, Tanskanen, Tomas, Renkonen-Sinisalo, Laura, Zanke, Brent, Männistö, Satu, Albanes, Demetrius, Weinstein, Stephanie, Ruiz-Narvaez, Edward, Palmer, Julie, Buchanan, Daniel, Platz, Elizabeth, Visvanathan, Kala, Ulrich, Cornelia, Siegel, Erin, Brezina, Stefanie, Gsur, Andrea, Campbell, Peter, Chang-Claude, Jenny, Hoffmeister, Michael, Brenner, Hermann, Slattery, Martha, Potter, John, Tsilidis, Konstantinos, Schulze, Matthias, Gunter, Marc, Murphy, Neil, Castells, Antoni, Castellví-Bel, Sergi, Moreira, Leticia, Arndt, Volker, Shcherbina, Anna, Stern, Mariana, Pardamean, Bens, Bishop, Timothy, Giles, Graham, Southey, Melissa, Idos, Gregory, McDonnell, Kevin, Abu-Ful, Zomoroda, Greenson, Joel, Shulman, Katerina, Lejbkowicz, Flavio, Offit, Kenneth, Su, Yu-Ru, Steinfelder, Robert, Keku, Temitope, van Guelpen, Bethany, Hudson, Thomas, Hampel, Heather, Pearlman, Rachel, Berndt, Sonja, Hayes, Richard, Martinez, Marie Elena, Thomas, Sushma, Corley, Douglas, Pharoah, Paul, Larsson, Susanna, Yen, Yun, Lenz, Heinz-Josef, White, Emily, Li, Li, Doheny, Kimberly, Pugh, Elizabeth, Shelford, Tameka, Chan, Andrew, Cruz-Correa, Marcia, Lindblom, Annika, Hunter, David, Joshi, Amit, Schafmayer, Clemens, Scacheri, Peter, Kundaje, Anshul, Nickerson, Deborah, Schoen, Robert, Hampe, Jochen, Stadler, Zsofia, Vodicka, Pavel, Vodickova, Ludmila, Vymetalkova, Veronika, Papadopoulos, Nickolas, Edlund, Chistopher, Gauderman, William, Thomas, Duncan, Shibata, David, Toland, Amanda, Markowitz, Sanford, Kim, Andre, Chanock, Stephen, van Duijnhoven, Franzel, Feskens, Edith, Sakoda, Lori, Gago-Dominguez, Manuela, Wolk, Alicja, Naccarati, Alessio, Pardini, Barbara, FitzGerald, Liesel, Lee, Soo Chin, Ogino, Shuji, Bien, Stephanie, Kooperberg, Charles, Li, Christopher, Lin, Yi, Prentice, Ross, Qu, Conghui, Bézieau, Stéphane, Tangen, Catherine, Mardis, Elaine, Yamaji, Taiki, Sawada, Norie, Iwasaki, Motoki, Haiman, Christopher, Le Marchand, Loic, Wu, Anna, Qu, Chenxu, McNeil, Caroline, Coetzee, Gerhard, Hayward, Caroline, Deary, Ian, Harris, Sarah, Theodoratou, Evropi, Reid, Stuart, Walker, Marion, Ooi, Li Yin, Moreno, Victor, Casey, Graham, Gruber, Stephen, Tomlinson, Ian, Zheng, Wei, Dunlop, Malcolm, Houlston, Richard, and Peters, Ulrike

Published
2023
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27. High immune cell infiltration predicts improved survival in cholangiocarcinoma.

Author

Wirta, Erkki-Ville, Szeto, Säde, Koppatz, Hanna, Nordin, Arno, Mäkisalo, Heikki, Arola, Johanna, Sirén, Jukka, Ahtiainen, Maarit, Böhm, Jan, Mecklin, Jukka-Pekka, Sallinen, Ville, and Seppälä, Toni T.

Subjects
CHOLANGIOCARCINOMA, PROGRAMMED cell death 1 receptors, PROGRAMMED death-ligand 1, OVERALL survival, PROGNOSIS
Abstract

Background: Antitumoral immune response has a crucial role in constraining cancer. However, previous studies on cholangiocarcinoma (CCA), a rare and aggressive cancer, have reported contradictory findings on the prognostic impact of tumorinfiltrating T-lymphocytes. We aimed to clarify the effect of tumor-infiltrating CD3+ and CD8+ lymphocytes and PD-1/PD-L1 expression on CCA prognosis. Methods: CD3+, CD8+, and PD-1+ lymphocyte densities, as well as PD-L1 expression rate were analyzed from stained tissue microarray samples from the tumor center and invasive margin of 47 cholangiocarcinomas. The association of CD3+ and CD8+ based Immune cell score (ICS) and its components with overall survival was evaluated, adjusting for age, sex, TNM stage, radicality of surgery, tumor location, and PD-L1 expression on immune cells. Results: Low ICS was a strong independent prognostic factor for worse overall survival (Hazard ratio 9.27, 95% confidence interval 2.72-31.64, P<0.001). Among the ICS components, high CD8+ lymphocyte infiltration at the tumor center had the most evident impact on patient outcome. PD-1 and PD-L1 expression on immune cells did not have a significant impact on overall survival alone; however, PD-L1 positivity seemed to impair survival for ICSlow subgroup. Conclusion: Identifying patient subgroups that could benefit from immunotherapy with PD-1/PD-L1 pathway blockade may help improve treatment strategies for this aggressive cancer. Our findings highlight the importance of evaluating the immune contexture in cholangiocarcinoma, as ICS serves as a strong independent prognostic and selective factor for patients who might benefit from immunotherapy. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Toll-like receptors 1–9 in small bowel neuroendocrine tumors–Clinical significance and prognosis.

Author

Hiltunen, Niko, Kemi, Niko, Väyrynen, Juha P., Böhm, Jan, Kauppila, Joonas H., Huhta, Heikki, and Helminen, Olli

Subjects
SMALL intestine, LYMPHATIC metastasis, PATTERN perception receptors, CLINICAL drug trials, SURVIVAL analysis (Biometry), PROGNOSIS, PROGRESSION-free survival
Abstract

Toll-like receptors (TLRs) are pattern recognition receptors of the innate immunity. TLRs are known to mediate both antitumor effects and tumorigenesis. TLRs are abundant in many cancers, but their expression in small bowel neuroendocrine tumors (SB-NETs) is unknown. We aimed to characterize the expression of TLRs 1–9 in SB-NETs and lymph node metastases and evaluate their prognostic relevance. The present study included 125 patients with SB-NETs, of whom 95 had lymph node metastases, from two Finnish hospitals. Tissue samples were stained immunohistochemically for TLR expression, assessed based on cytoplasmic and nucleic staining intensity and percentage of positively stained cells. Statistical methods for survival analysis included Kaplan-Meier method and Cox regression adjusted for confounding factors. Disease-specific survival (DSS) was the primary outcome. TLRs 1–2 and 4–9 were expressed in SB-NETs and lymph node metastases. TLR3 showed no positive staining. In primary SB-NETs, TLRs 1–9 were not associated with survival. For lymph node metastases, high cytoplasmic TLR7 intensity associated with worse DSS compared to low cytoplasmic intensity (26.4% vs. 84.9%, p = 0.028). Adjusted mortality hazard (HR) was 3.90 (95% CI 1.07–14.3). The expression of TLRs 1–6 and 8–9 in lymph node metastases were not associated with survival. SB-NETs and their lymph node metastases express cytoplasmic TLR 1–2 and 4–9 and nucleic TLR5. High TLR7 expression in SB-NET lymph node metastases was associated with worse prognosis. The current research has future perspective, as it can help create base for clinical drug trials to target specific TLRs with agonists or antagonists to treat neuroendocrine tumors. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Multiplexed analysis of macrophage polarisation in pulmonary metastases of microsatellite stable colorectal cancer

Author

Karjula, Topias, primary, Elomaa, Hanna, additional, Väyrynen, Sara A., additional, Kuopio, Teijo, additional, Ahtiainen, Maarit, additional, Mustonen, Olli, additional, Puro, Iiris, additional, Niskakangas, Anne, additional, Mecklin, Jukka-Pekka, additional, Böhm, Jan, additional, Wirta, Erkki-Ville, additional, Seppälä, Toni T., additional, Sihvo, Eero, additional, Yannopoulos, Fredrik, additional, Helminen, Olli, additional, and Väyrynen, Juha, additional

Published
2023
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37. Pro-inflammatory fatty acid profile and colorectal cancer risk: A Mendelian randomisation analysis

Author

May-Wilson, Sebastian, Sud, Amit, Law, Philip J., Palin, Kimmo, Tuupanen, Sari, Gylfe, Alexandra, Hänninen, Ulrika A., Cajuso, Tatiana, Tanskanen, Tomas, Kondelin, Johanna, Kaasinen, Eevi, Sarin, Antti-Pekka, Eriksson, Johan G., Rissanen, Harri, Knekt, Paul, Pukkala, Eero, Jousilahti, Pekka, Salomaa, Veikko, Ripatti, Samuli, Palotie, Aarno, Renkonen-Sinisalo, Laura, Lepistö, Anna, Böhm, Jan, Mecklin, Jukka-Pekka, Al-Tassan, Nada A., Palles, Claire, Farrington, Susan M., Timofeeva, Maria N., Meyer, Brian F., Wakil, Salma M., Campbell, Harry, Smith, Christopher G., Idziaszczyk, Shelley, Maughan, Timothy S., Fisher, David, Kerr, Rachel, Kerr, David, Passarelli, Michael N., Figueiredo, Jane C., Buchanan, Daniel D., Win, Aung K., Hopper, John L., Jenkins, Mark A., Lindor, Noralane M., Newcomb, Polly A., Gallinger, Steven, Conti, David, Schumacher, Fred, Casey, Graham, Aaltonen, Lauri A., Cheadle, Jeremy P., Tomlinson, Ian P., Dunlop, Malcolm G., and Houlston, Richard S.

Published
2017
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40. Immunological and prognostic significance of tumour necrosis in colorectal cancer

Author

Kastinen, Meeri, primary, Sirniö, Päivi, additional, Elomaa, Hanna, additional, Ahtiainen, Maarit, additional, Väyrynen, Sara A., additional, Herzig, Karl-Heinz, additional, Meriläinen, Sanna, additional, Aro, Raila, additional, Häivälä, Reetta, additional, Rautio, Tero, additional, Saarnio, Juha, additional, Wirta, Erkki-Ville, additional, Helminen, Olli, additional, Seppälä, Toni T., additional, Kuopio, Teijo, additional, Böhm, Jan, additional, Tuomisto, Anne, additional, Mecklin, Jukka-Pekka, additional, Mäkinen, Markus J., additional, and Väyrynen, Juha P., additional

Published
2023
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41. Load of the ash dieback pathogen hymenoscyphus fraxineusdiffers in soil

Author

Böhm, Jan Werner, Zübert, Christina, Kahlenberg, Georgia, Jochner-Oette, Susanne, and Kube, Michael

Abstract

The ascomycete Hymenoscyphus fraxineuscauses the devastating ash dieback disease of European ash (Fraxinus excelsiorL.). Spore traps are often used to measure the amount of ascospores in the environment, but the pathogen-load of the soil in ash stands has not been recorded so far. This is of particular interest with regard to the occurrence of ash stem necrosis, a decisive factor for the severe course of the disease. In order to gain a more differentiated insight into the pathogen-load in ash stands, we analysed soil samples from four ash tree sites in southern Germany, covering a clone plantation, two seed orchards and a forest. The pathogen-load was determined using a quantitative TaqMan real-time PCR assay for ten to twenty plots per stand. Results obtained by the species-specific assay highlighted that the pathogen-load is heterogeneously distributed in the ash stands. H. fraxineusDNA targets were detected in 17% of the soil samples. The pathogen-load differed according to soil depth, with the highest pathogen abundance in the top 5 cm, followed by 5–10 cm and finally 10–15 cm. Pathogen-load and thereby infection pressure were found to be highly variable for the individual trees in one stand. Overall, the study discovered detectable levels of H. fraxineusin the soil of all four study sites, which supports the hypothesis that H. fraxineuscan be found in the soil of ash stands. The qPCR approach was found to be an effective method for monitoring the load of H. fraxineusin soil and for demonstrating the successful application of the method on the sample type of custom-made spore traps. Results suggest the implication of site-specific pathogen-load determination in future H. fraxineus-monitoring and selection of less susceptible ash trees for breeding and seed production.

Published
2024
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43. Supplementary Table 1 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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44. Supplementary Table 3 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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45. Supplementary Table 4 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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46. Supplementary Table 2 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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47. Supplementary Table 5 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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48. Supplementary Table 6 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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49. Supplementary Figures 1-19, supplementary methods and extended literature evaluation of the candidate genes from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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50. Supplementary Table 7 from Comprehensive Evaluation of Protein Coding Mononucleotide Microsatellites in Microsatellite-Unstable Colorectal Cancer

Author

Kondelin, Johanna, primary, Gylfe, Alexandra E., primary, Lundgren, Sofie, primary, Tanskanen, Tomas, primary, Hamberg, Jiri, primary, Aavikko, Mervi, primary, Palin, Kimmo, primary, Ristolainen, Heikki, primary, Katainen, Riku, primary, Kaasinen, Eevi, primary, Taipale, Minna, primary, Taipale, Jussi, primary, Renkonen-Sinisalo, Laura, primary, Järvinen, Heikki, primary, Böhm, Jan, primary, Mecklin, Jukka-Pekka, primary, Vahteristo, Pia, primary, Tuupanen, Sari, primary, Aaltonen, Lauri A., primary, and Pitkänen, Esa, primary

Published
2023
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