PanopticNDT: Efficient and Robust Panoptic Mapping

As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent is, and how they can be reached; i.e., information about free space is also crucial. Panoptic mapping is a powerful instrument providing such information. However, building 3D panoptic maps with high spatial resolution is challenging on mobile robots, given their limited computing capabilities. In this paper, we propose PanopticNDT - an efficient and robust panoptic mapping approach based on occupancy normal distribution transform (NDT) mapping. We evaluate our approach on the publicly available datasets Hypersim and ScanNetV2. The results reveal that our approach can represent panoptic information at a higher level of detail than other state-of-the-art approaches while enabling real-time panoptic mapping on mobile robots. Finally, we prove the real-world applicability of PanopticNDT with qualitative results in a domestic application.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 202

Recherche efficace de motifs fréquents dans des grilles

National audienceGeneral-purpose exhaustive graph mining algorithms are seldom used in real life contexts due to the high complexity of the process mostly based on costly isomorphism tests and countless expansion possibilities. In this paper, we show how to exploit grid-based representations to efficiently extract frequent grid subgraphs, and we introduce an efficient grid mining algorithm called GRIMA designed to scale to large amount of data. We apply our algorithm on image classification problems. Experiments show that our algorithm is efficient and that adding the structure may help the image classification process.La complexité des algorithmes de fouille de graphes généraux est telle qu'ils sont peu utilisés en pratique. Cette complexité est due à la fois aux tests d'isomor-phisme et au grand nombre de combinaisons permettant d'étendre un graphe durant le processus de fouille. Dans cet article, nous proposons d'exploiter des représenta-tions géométriques régulières (des grilles) pour recher-cher efficacement des motifs fréquents dans un ensemble de grilles. Nous présentons un algorithme appelé GRIMA qui, contrairement aux algorithmes généraux, peut passer l'échelle. Nous appliquons cet algorithme à un problème de classification d'images, pour lesquelles nous proposons une représentation par Sac de grilles. Les expérimenta-tions montrent l'efficacité de notre algorithme et l'intérêt d'utiliser une représentation structurée pour représenter les images

A Cross-Species Analysis of a Mouse Model of Breast Cancer-Specific Osteolysis and Human Bone Metastases Using Gene Expression Profiling

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the second leading cause of cancer-related death in women in the United States. During the advanced stages of disease, many breast cancer patients suffer from bone metastasis. These metastases are predominantly osteolytic and develop when tumor cells interact with bone. <it>In vivo </it>models that mimic the breast cancer-specific osteolytic bone microenvironment are limited. Previously, we developed a mouse model of tumor-bone interaction in which three mouse breast cancer cell lines were implanted onto the calvaria. Analysis of tumors from this model revealed that they exhibited strong bone resorption, induction of osteoclasts and intracranial penetration at the tumor bone (TB)-interface.</p> <p>Methods</p> <p>In this study, we identified and used a TB microenvironment-specific gene expression signature from this model to extend our understanding of the metastatic bone microenvironment in human disease and to predict potential therapeutic targets.</p> <p>Results</p> <p>We identified a TB signature consisting of 934 genes that were commonly (among our 3 cell lines) and specifically (as compared to tumor-alone area within the bone microenvironment) up- and down-regulated >2-fold at the TB interface in our mouse osteolytic model. By comparing the TB signature with gene expression profiles from human breast metastases and an <it>in vitro </it>osteoclast model, we demonstrate that our model mimics both the human breast cancer bone microenvironment and osteoclastogenesis. Furthermore, we observed enrichment in various signaling pathways specific to the TB interface; that is, TGF-β and myeloid self-renewal pathways were activated and the Wnt pathway was inactivated. Lastly, we used the TB-signature to predict cyclopenthiazide as a potential inhibitor of the TB interface.</p> <p>Conclusion</p> <p>Our mouse breast cancer model morphologically and genetically resembles the osteoclastic bone microenvironment observed in human disease. Characterization of the gene expression signature specific to the TB interface in our model revealed signaling mechanisms operative in human breast cancer metastases and predicted a therapeutic inhibitor of cancer-mediated osteolysis.</p

Molecular characterization of hepatocellular carcinoma in patients with nonalcoholic steatohepatitis

Background and aims: Non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (HCC) is increasing globally, but its molecular features are not well defined. We aimed to identify unique molecular traits characterising NASH-HCC compared to other HCC aetiologies. Methods: We collected 80 NASH-HCC and 125 NASH samples from 5 institutions. Expression array (n = 53 NASH-HCC; n = 74 NASH) and whole exome sequencing (n = 52 NASH-HCC) data were compared to HCCs of other aetiologies (n = 184). Three NASH-HCC mouse models were analysed by RNA-seq/expression-array (n = 20). Activin A receptor type 2A (ACVR2A) was silenced in HCC cells and proliferation assessed by colorimetric and colony formation assays. Results: Mutational profiling of NASH-HCC tumours revealed TERT promoter (56%), CTNNB1 (28%), TP53 (18%) and ACVR2A (10%) as the most frequently mutated genes. ACVR2A mutation rates were higher in NASH-HCC than in other HCC aetiologies (10% vs. 3%, p <0.05). In vitro, ACVR2A silencing prompted a significant increase in cell proliferation in HCC cells. We identified a novel mutational signature (MutSig-NASH-HCC) significantly associated with NASH-HCC (16% vs. 2% in viral/alcohol-HCC, p = 0.03). Tumour mutational burden was higher in non-cirrhotic than in cirrhotic NASH-HCCs (1.45 vs. 0.94 mutations/megabase; p <0.0017). Compared to other aetiologies of HCC, NASH-HCCs were enriched in bile and fatty acid signalling, oxidative stress and inflammation, and presented a higher fraction of Wnt/TGF-β proliferation subclass tumours (42% vs. 26%, p = 0.01) and a lower prevalence of the CTNNB1 subclass. Compared to other aetiologies, NASH-HCC showed a significantly higher prevalence of an immunosuppressive cancer field. In 3 murine models of NASH-HCC, key features of human NASH-HCC were preserved. Conclusions: NASH-HCCs display unique molecular features including higher rates of ACVR2A mutations and the presence of a newly identified mutational signature. Lay summary: The prevalence of hepatocellular carcinoma (HCC) associated with non-alcoholic steatohepatitis (NASH) is increasing globally, but its molecular traits are not well characterised. In this study, we uncovered higher rates of ACVR2A mutations (10%) - a potential tumour suppressor - and the presence of a novel mutational signature that characterises NASH-related HCC

Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma

Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients

CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

IntroductionMounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).MethodsA total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.ResultsAccording to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.ConclusionsOur study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management

Ranked centroid projection: A data visualization approach based on self-organizing maps

The Self-Organizing Map (SOM) is an unsupervised neural network model that provides topology-preserving mapping from high-dimensional input spaces onto a commonly two-dimensional output space. In this study, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e. document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text data mining. The proposed approach first transforms the document space into a multi-dimensional vector space by means of document encoding. Then a growing hierarchical SOM (GHSOM) is trained and used as a baseline framework, which automatically produces maps with various levels of details. Following the training of the GHSOM, a novel projection method, namely the Ranked Centroid Projection (RCP), is applied to project the input vectors onto a hierarchy of two-dimensional output maps. The projection of the input vectors is treated as a vector interpolation into a two-dimensional regular map grid. A ranking scheme is introduced to select the nearest R units around the input vector in the original data space, the positions of which will be taken into account in computing the projection coordinates.The proposed approach can be used both as a data analysis tool and as a direct interface to the data. Its applicability has been demonstrated in this study using an illustrative data set and two real-world document clustering tasks, i.e. the SOM paper collection and the Anthrax paper collection. Based on the proposed approach, a software toolbox is designed for analyzing and visualizing document collections, which provides a user-friendly interface and several exploration and analysis functions.The presented SOM-based approach incorporates several unique features, such as the adaptive structure, the hierarchical training, the automatic parameter adjustment and the incremental clustering. Its advantages include the ability to convey a large amount of information in a limited space with comparatively low computation load, the potential to reveal conceptual relationships among documents, and the facilitation of perceptual inferences on both inter-cluster and within-cluster relationships