We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.
Since the early days of the synthetic lethality concept in DNA Damage Response the status of homologous recombination (HR) repair in cancer cells have been the focus of attention of researchers and clinicians. While different approaches exist, such as the RAD51 immunofluorescence (IF) or HRD genomic assays, functional biomarkers that can assess HR proficiency are missing. We report here the development, optimization and validation of two complementary, HR-specific functional assays. The assays, which are based on the STRIDE platform technology, detect double-strand DNA breaks localized in close proximity to RPA or RAD51 proteins. The optimization phase of assay development was performed in U2OS cells. First, repeatability (intra-run variation) and reproducibility (inter-run variation) of the assays were measured in untreated cells. Then, a series of technical negative controls was performed which have shown that the number of false-positive readouts is below 10% of the total number of signals. Finally, treatment of cells with compounds known to induce double-strand DNA breaks (etoposide and cisplatin) resulted in statistically significant increase in the number of detected dSTRIDE-RAD51 and dSTRIDE-RPA foci when compared to untreated controls. The assays were further validated in NCI-H661 (BRCA2 wild-type) and NCI-H169 (BRCA2 KO) cell line pair. The cells were treated with two concentrations of etoposide and the readouts from dSTRIDE, detecting the total pool of DSBs and dSTRIDE-HR assays were compared. In NCI-H661 cells, treatment with etoposide resulted in an increase in the number of double-strand breaks detected by dSTRIDE and as expected, more DSBs were formed after treatment with the higher concentration. dSTRIDE-HR assays confirmed that approximately 15% and 10% of these DSBs contain RPA and RAD51 proteins, respectively. In NCI-H169 cells etoposide produced a stronger reaction with even more DSBs detected by dSTRIDE, but importantly, no increase in the number of dSTRIDE-RAD51 foci was observed. dSTRIDE-RPA foci increased after treatment hinting that this step of HR remains unperturbed. Interestingly, the number of dSTRIDE-RAD51 foci in untreated cells was comparable between the two cell lines. In conclusion, we show here that two newly developed dSTRIDE-HR assays are well validated and can be successfully applied to report on the status of homologous recombination repair in different cell models. Citation Format: Kamil Solarczyk, Agnieszka Waligórska, Karolina Uznańska, Zsombor Prucsi, Olga Wójcikowska, Ewelina Matuszyk, Magdalena Bartyńska, Agata Kitlińska, Aleksandra Bober, Franek Sierpowski, Maja Białecka, Monika Jarosz, Malgorzata Szczygiel, Szymon Koman, Karolina Korpanty, Lukasz Beben, Lukasz Bandzarewicz, Przemyslaw Stachura, Magdalena Kordon-Kiszala. Novel functional dSTRIDE-HR assays to report on the status of homologous recombination repair in cancer cells. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6108.
Designing a 3D game scene is a tedious task that often requires a substantial amount of work. Typically, this task involves synthesis, coloring, and placement of 3D models within the game scene. To lessen this workload, we can apply machine learning to automate some aspects of the game scene development. Earlier research has already tackled automated generation of the game scene background with machine learning. However, model auto-coloring remains an underexplored problem. The automatic coloring of a 3D model is a challenging task, especially when dealing with the digital representation of a colorful, multipart object. In such a case, we have to "understand" the object's composition and coloring scheme of each part. Existing single-stage methods have their own caveats such as the need for segmentation of the object or generating individual parts that have to be assembled together to yield the final model. We address these limitations by proposing a two-stage training approach to synthesize auto-colored 3D models. In the first stage, we obtain a 3D point cloud representing a 3D object, whilst in the second stage, we assign colors to points within such cloud. Next, by leveraging the so-called triangulation trick, we generate a 3D mesh in which the surfaces are colored based on interpolation of colored points representing vertices of a given mesh triangle. This approach allows us to generate a smooth coloring scheme. Experimental evaluation shows that our two-stage approach gives better results in terms of shape reconstruction and coloring when compared to traditional single-stage techniques.
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