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The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.

Mortalin (mot-2) induces inactivation of the tumor suppressor p53s transcriptional and apoptotic functions by cytoplasmic sequestration of p53 in select cancers. The mot-2-dependent cytoprotective function enables cancer cells to support malignant transformation. Abrogating the p53-mot-2 interaction can control or slow down the growth of cancer cells. In this study, we report the discovery of a ubiquitin-like (UBX)-domain-containing protein, UBXN2A, which binds to mot-2 and consequently inhibits the binding between mot-2 and p53. Genetic analysis showed that UBXN2A binds to mot-2s substrate binding domain, and it partly overlaps p53s binding site indicating UBXN2A and p53 likely bind to mot-2 competitively. By binding to mot-2, UBXN2A releases p53 from cytosolic sequestration, rescuing the tumor suppressor functions of p53. Biochemical analysis and functional assays showed that the overexpression of UBXN2A and the functional consequences of unsequestered p53 trigger p53-dependent apoptosis. Cells expressing shRNA against UBXN2A showed the opposite effect of that seen with UBXN2A overexpression. The expression of UBXN2A and its apoptotic effects were not observed in normal colonic epithelial cells and p53-/- colon cancer cells. Finally, significant reduction in tumor volume in a xenograft mouse model in response to UBXN2A expression was verified in vivo. Our results introduce UBXN2A as a home defense response protein, which can reconstitute inactive p53-dependent apoptotic pathways. Inhibition of mot-2-p53 interaction by UBXN2A is an attractive therapeutic strategy in mot-2-elevated tumors.

The computational work to perform particle advection‐based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time ("performance") is a critical issue. This state‐of‐the‐art report considers existing optimizations for particle advection, using two high‐level categories: algorithmic optimizations and hardware efficiency. The sub‐categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub‐categories for hardware efficiency all involve parallelism: shared‐memory, distributed‐memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations. [ABSTRACT FROM AUTHOR]

The engineering educational institutes strive to develop the graduating students to possess the skillsets required by the related industries in the market. However the industries, that seek the engineering graduates as their employees assert that they spend lot of time, money and energy in training these graduates. This fact emphasizes that there exists a gap in the skillsets cultivated in the graduates by the academic institutes and those required at the industries. The main causes behind this have been the differences in their major goals along with the lack of focused collaboration and efforts required to thin the said gap. In this paper we present a model namely 'Tailored Skills Development Model' (TSDM) which proves to be effective in bridging this gap. The model proves to be supportive to the engineering institutions where they are able to cultivate the graduates with advanced skills that are necessitated by the industries in the specific engineering disciplines focused by the institutes. And the model also assists the industries to seek the better manpower with adequate skillsets. The model helps to reduce the training period, related cost and energy involved at the industry levels in search of the anticipated skillsets. The paper discusses the organization and implementation of this model at our institute along with the statistics of its execution. [ABSTRACT FROM AUTHOR]

Data mining and big data analytics are approaches for analyzing data and extracting hidden information. Because big data is complicated and large in volume, traditional techniques to analysis and extraction do not function effectively. Data clustering is a common data mining approach that divides data into groups and makes it simple to extract information from them. Big data can include both organized and semi structured information, and it's becoming increasingly beneficial for companies. Examples include old organized database of inventory level, transactions, and consumer information, as well as non - structured comprehension from the internet, social media platforms, and embedded systems. Numerous schemes have been developed to reach the needed in relation to efficiency and effectiveness, and much study has been committed to Big Data analytics. Nevertheless, a few methodologies, such as clustering algorithms, require further research in regards to performance, usefulness, and other factors, leading to the development of a model which gives proper Big Data Analytics assessment and the impactful use of this methodology to retrieve relevant knowledge. We recorded and analyzed several big data sets in our proposed work, as well as discovered relevant current approaches. In this paper we proposed a new clustering technique using dimensionality reduction approach. For implementation of this work, we used real time streaming data in unstructured form and noisy sometimes. The proposed hybrid clustering techniques that improve the clustering accuracy as well as time for generate effective's clusters on large unstructured data. We confirm the findings by testing the suggested methodology on available information sets and comparing and analyzing the effectiveness of the developed system with that of current systems. [ABSTRACT FROM AUTHOR]