Future Challenges and Unsolved Problems in Multi-field Visualization

Evaluation, solved and unsolved problems, and future directions are popular themes pervading the visualization community over the last decade. The top unsolved problem in both scientific and information visualization was the subject of an IEEE Visualization Conference panel in 2004. The future of graphics hardware was another important topic of discussion the same year. The subject of how to evaluate visualization returned a few years later. Chris Johnson published a list of 10 top problems in scientific visualization research. This was followed up by report of both past achievements and future challenges in visualization research as well as financial support recommendations to the National Science Foundation (NSF) and National Institute of Health (NIH). Chen recently published the first list of top unsolved information visualization problems. Future research directions of topology-based visualization was also a major theme of a workshop on topology-based methods. Laramee and Kosara published a list of top future challenges in human-centered visualization

VEGFA Upregulates FLJ10540 and Modulates Migration and Invasion of Lung Cancer via PI3K/AKT Pathway

BACKGROUND: Lung adenocarcinoma is the leading cause of cancer-related deaths among both men and women in the world. Despite recent advances in diagnosis and treatment, the mortality rates with an overall 5-year survival of only 15%. This high mortality is probably attributable to early metastasis. Although several well-known markers correlated with poor/metastasis prognosis in lung adenocarcinoma patients by immunohistochemistry was reported, the molecular mechanisms of lung adenocarcinoma development are still not clear. To explore novel molecular markers and their signaling pathways will be crucial for aiding in treatment of lung adenocarcinoma patients. METHODOLOGY/PRINCIPAL FINDINGS: To identify novel lung adenocarcinoma-associated /metastasis genes and to clarify the underlying molecular mechanisms of these targets in lung cancer progression, we created a bioinformatics scheme consisting of integrating three gene expression profile datasets, including pairwise lung adenocarcinoma, secondary metastatic tumors vs. benign tumors, and a series of invasive cell lines. Among the novel targets identified, FLJ10540 was overexpressed in lung cancer tissues and is associated with cell migration and invasion. Furthermore, we employed two co-expression strategies to identify in which pathway FLJ10540 was involved. Lung adenocarcinoma array profiles and tissue microarray IHC staining data showed that FLJ10540 and VEGF-A, as well as FLJ10540 and phospho-AKT exhibit positive correlations, respectively. Stimulation of lung cancer cells with VEGF-A results in an increase in FLJ10540 protein expression and enhances complex formation with PI3K. Treatment with VEGFR2 and PI3K inhibitors affects cell migration and invasion by activating the PI3K/AKT pathway. Moreover, knockdown of FLJ10540 destabilizes formation of the P110-alpha/P85-alpha-(PI3K) complex, further supporting the participation of FLJ10540 in the VEGF-A/PI3K/AKT pathway. CONCLUSIONS/SIGNIFICANCE: This finding set the stage for further testing of FLJ10540 as a new therapeutic target for treating lung cancer and may contribute to the development of new therapeutic strategies that are able to block the PI3K/AKT pathway in lung cancer cells

Efektivní, jednoduchá aproximace velkých roztroušených 3D vektorových dat pomocí dělení prostoru

Aproximace RBF (Radialní Básové Funkce) je efektivní metoda pro rozptýlená skalární a vektorová pole. Její použití je však v případě velkých rozptýlených dat velmi obtížné. Tato práce prezentuje RBF aproximaci spolu s prostorovou dělící technikou pro velká vektorová pole. Pro velké rozptýlené datové sady je použita technika dělení prostoru s překrývajícími se 3D buňkami. Sloučení překrývajících se 3D buněk se používá k dosažení kontinuity a hladkosti. Navrhovaná metoda je použitelná i pro skalární a vektorové datové sady. Experimenty prokázaly použitelnost tohoto přístupu a jsou prezentovány výsledky s datovým souborem velkého vektorového pole tornáda.The Radial basis function (RBF) approximation is an efficient method for scattered scalar and vector data fields. However its application is very difficult in the case of large scattered data. This paper presents RBF approximation together with space subdivision technique for large vector fields. For large scattered data sets a space subdivision technique with overlapping 3D cells is used. Blending of overlapped 3D cells is used to obtain continuity and smoothness. The proposed method is applicable for scalar and vector data sets as well. Experiments proved applicability of this approach and results with the tornado large vector field data set are presented

RAMPVIS: answering the challenges of building visualisation capabilities for large-scale emergency responses

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses