The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
Pradip Sinha - One of the best experts on this subject based on the ideXlab platform.
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Selector genes display tumor cooperation and inhibition in Drosophila epithelium in a developmental context-dependent manner
The Company of Biologists, 2017Co-Authors: Ram Prakash Gupta, Anjali Bajpai, Pradip SinhaAbstract:During animal development, Selector genes determine identities of body Segments and those of individual organs. Selector genes are also misexpressed in cancers, although their contributions to tumor progression per se remain poorly understood. Using a model of cooperative tumorigenesis, we show that gain of Selector genes results in tumor cooperation, but in only select developmental domains of the wing, haltere and eye-antennal imaginal discs of Drosophila larva. Thus, the field Selector, Eyeless (Ey), and the Segment Selector, Ultrabithorax (Ubx), readily cooperate to bring about neoplastic transformation of cells displaying somatic loss of the tumor suppressor, Lgl, but in only those developmental domains that express the homeo-box protein, Homothorax (Hth), and/or the Zinc-finger protein, Teashirt (Tsh). In non-Hth/Tsh-expressing domains of these imaginal discs, however, gain of Ey in lgl− somatic clones induces neoplastic transformation in the distal wing disc and haltere, but not in the eye imaginal disc. Likewise, gain of Ubx in lgl− somatic clones induces transformation in the eye imaginal disc but not in its endogenous domain, namely, the haltere imaginal disc. Our results reveal that Selector genes could behave as tumor drivers or inhibitors depending on the tissue contexts of their gains
Ram Prakash Gupta - One of the best experts on this subject based on the ideXlab platform.
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Selector genes display tumor cooperation and inhibition in Drosophila epithelium in a developmental context-dependent manner
The Company of Biologists, 2017Co-Authors: Ram Prakash Gupta, Anjali Bajpai, Pradip SinhaAbstract:During animal development, Selector genes determine identities of body Segments and those of individual organs. Selector genes are also misexpressed in cancers, although their contributions to tumor progression per se remain poorly understood. Using a model of cooperative tumorigenesis, we show that gain of Selector genes results in tumor cooperation, but in only select developmental domains of the wing, haltere and eye-antennal imaginal discs of Drosophila larva. Thus, the field Selector, Eyeless (Ey), and the Segment Selector, Ultrabithorax (Ubx), readily cooperate to bring about neoplastic transformation of cells displaying somatic loss of the tumor suppressor, Lgl, but in only those developmental domains that express the homeo-box protein, Homothorax (Hth), and/or the Zinc-finger protein, Teashirt (Tsh). In non-Hth/Tsh-expressing domains of these imaginal discs, however, gain of Ey in lgl− somatic clones induces neoplastic transformation in the distal wing disc and haltere, but not in the eye imaginal disc. Likewise, gain of Ubx in lgl− somatic clones induces transformation in the eye imaginal disc but not in its endogenous domain, namely, the haltere imaginal disc. Our results reveal that Selector genes could behave as tumor drivers or inhibitors depending on the tissue contexts of their gains
Anjali Bajpai - One of the best experts on this subject based on the ideXlab platform.
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Selector genes display tumor cooperation and inhibition in Drosophila epithelium in a developmental context-dependent manner
The Company of Biologists, 2017Co-Authors: Ram Prakash Gupta, Anjali Bajpai, Pradip SinhaAbstract:During animal development, Selector genes determine identities of body Segments and those of individual organs. Selector genes are also misexpressed in cancers, although their contributions to tumor progression per se remain poorly understood. Using a model of cooperative tumorigenesis, we show that gain of Selector genes results in tumor cooperation, but in only select developmental domains of the wing, haltere and eye-antennal imaginal discs of Drosophila larva. Thus, the field Selector, Eyeless (Ey), and the Segment Selector, Ultrabithorax (Ubx), readily cooperate to bring about neoplastic transformation of cells displaying somatic loss of the tumor suppressor, Lgl, but in only those developmental domains that express the homeo-box protein, Homothorax (Hth), and/or the Zinc-finger protein, Teashirt (Tsh). In non-Hth/Tsh-expressing domains of these imaginal discs, however, gain of Ey in lgl− somatic clones induces neoplastic transformation in the distal wing disc and haltere, but not in the eye imaginal disc. Likewise, gain of Ubx in lgl− somatic clones induces transformation in the eye imaginal disc but not in its endogenous domain, namely, the haltere imaginal disc. Our results reveal that Selector genes could behave as tumor drivers or inhibitors depending on the tissue contexts of their gains
Philip H. S. Torr - One of the best experts on this subject based on the ideXlab platform.
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Associative Hierarchical Random Fields
IEEE transactions on pattern analysis and machine intelligence, 2014Co-Authors: Lubor Ladicky, Chris Russell, Pushmeet Kohli, Philip H. S. TorrAbstract:This paper makes two contributions: the first is the proposal of a new model—The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic Segmentation. Most methods for semantic Segmentation are formulated as a labeling problem for variables that might correspond to either pixels or Segments such as super-pixels. It is well known that the generation of super pixel Segmentations is not unique. This has motivated many researchers to use multiple super pixel Segmentations for problems such as semantic Segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the Segmentations form a Segmentation tree. Our new hierarchical random field model allows information from all of the multiple Segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or Segments, and the resulting labelings can be viewed both as a detailed Segmentation at the pixel level, or at the other extreme, as a Segment Selector that pieces together a solution like a jigsaw, selecting the best Segments from different Segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class Segmentation, and show that this ability to perform inference using multiple overlapping Segmentations leads to state-of-the-art results.
Lubor Ladicky - One of the best experts on this subject based on the ideXlab platform.
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Associative Hierarchical Random Fields
IEEE transactions on pattern analysis and machine intelligence, 2014Co-Authors: Lubor Ladicky, Chris Russell, Pushmeet Kohli, Philip H. S. TorrAbstract:This paper makes two contributions: the first is the proposal of a new model—The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic Segmentation. Most methods for semantic Segmentation are formulated as a labeling problem for variables that might correspond to either pixels or Segments such as super-pixels. It is well known that the generation of super pixel Segmentations is not unique. This has motivated many researchers to use multiple super pixel Segmentations for problems such as semantic Segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the Segmentations form a Segmentation tree. Our new hierarchical random field model allows information from all of the multiple Segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or Segments, and the resulting labelings can be viewed both as a detailed Segmentation at the pixel level, or at the other extreme, as a Segment Selector that pieces together a solution like a jigsaw, selecting the best Segments from different Segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class Segmentation, and show that this ability to perform inference using multiple overlapping Segmentations leads to state-of-the-art results.