Disease-biased and shared characteristics of the immunoglobulin gene repertoires in marginal zone B cell lymphoproliferations.

The B cell receptor immunoglobulin (BcR IG) gene repertoires of marginal zone (MZ) lymphoproliferations were analyzed in order to obtain insight into their ontogenetic relationships. Our cohort included cases with MZ lymphomas (n=488) i.e. splenic (SMZL), nodal (NMZL) and extranodal (ENMZL) as well as provisional entities (n=76) according to the World Health Organization classification. The most striking IG gene repertoire skewing was observed in SMZL. However, restrictions were also identified in all other MZ lymphomas studied, particularly ENMZL, with significantly different IG gene distributions depending on the primary site of involvement. Cross-entity comparisons of the MZ IG sequence dataset with a large dataset of IG sequences (MZ-related or not; n=65,837) revealed four major clusters of cases sharing homologous ('public') heavy variable complementarity-determining region 3. These clusters included rearrangements from SMZL, ENMZL (gastric, salivary gland, ocular adnexa), chronic lymphocytic leukemia but also rheumatoid factors and non-malignant spleen MZ cells. In conclusion, different MZ lymphomas display biased immunogenetic signatures indicating distinct antigen exposure histories. The existence of rare public stereotypes raises the intriguing possibility that common, pathogen-triggered, immune-mediated mechanisms, may result in diverse B lymphoproliferations due to targeting versatile progenitor B cells and/or operating in particular microenvironments.This work was supported in part by H2020 “AEGLE, An analytics framework for integrated and personalized healthcare services in Europe”, by the European Union (EU); H2020 No. 692298 project “MEDGENET, Medical Genomics and Epigenomics Network” by the EU; grant AZV 15-30015A from the Ministry of Health of the Czech Republic, and the project CEITEC2020 LQ1601 from the Ministry of Education, Youth, and Sports of the Czech Republic; Bloodwise Research Grant (15019); the Swedish Cancer Society, the Swedish Research Council, the Knut and Alice Wallenberg Foundation, Karolinska Institutet, Stockholm, the Lion’s Cancer Research Foundation, Uppsala, the Marcus Borgström Foundation and Selander’s Foundation, Uppsala

Simulation and Visual Analysis of Neuromusculoskeletal Models and Data

Part 7: New Methods and Tools for Big DataInternational audienceThis work presents a novel medical decision support system for diseases related to the upper body neuromusculature. The backbone of the system is a simulation engine able to perform both forward and inverse simulation of upper limb motions. In forward mode neural signals are fed to the muscles that perform the corresponding motion. In the inverse mode, a specified motion trajectory is used as input and the neural signals that are the root cause of this particular motion are estimated and investigated. Due to the vast amount of information that results from even simple simulations, the results are presented to the expert using visual analytics metaphors and in particular both embodied and symbolic visualizations. Several use cases are presented so as to demonstrate the analytics potential of the proposed system

Discovering the Discriminating Power in Patient Test Features Using Visual Analytics: A Case Study in Parkinson’s Disease

Part 11: New Methods and Tools for Big Data Wokshop (MT4BD)International audienceThis paper presents a novel methodology for selecting the most representative features for identifying the presence of the Parkinson’s Disease (PD). The proposed methodology is based on interactive visual analytic based on multi-objective optimisation. The implemented tool processes and visualises the information extracted via performing a typical line-tracking test using a tablet device. Such output information includes several modalities, such as position, velocity, dynamics, etc. Preliminary results depict that the implemented visual analytics technique has a very high potential in discriminating the PD patients from healthy individuals and thus, it can be used for the identification of the best feature type which is representative of the disease presence