Asymmetric tensor field visualization for surfaces

Asymmetric tensor field visualization can provide important insight into fluid flows and solid deformations. Existing techniques for asymmetric tensor fields focus on the analysis, and simply use evenly-spaced hyperstreamlines on surfaces following eigenvectors and dual-eigenvectors in the tensor field. In this paper, we describe a hybrid visualization technique in which hyperstreamlines and elliptical glyphs are used in real and complex domains, respectively. This enables a more faithful representation of flow behaviors inside complex domains. In addition, we encode tensor magnitude, an important quantity in tensor field analysis, using the density of hyperstreamlines and sizes of glyphs. This allows colors to be used to encode other important tensor quantities. To facilitate quick visual exploration of the data from different viewpoints and at different resolutions, we employ an efficient image-space approach in which hyperstreamlines and glyphs are generated quickly in the image plane. The combination of these techniques leads to an efficient tensor field visualization system for domain scientists. We demonstrate the effectiveness of our visualization technique through applications to complex simulated engine fluid flow and earthquake deformation data. Feedback from domain expert scientists, who are also co-authors, is provided.     

The microRNA-202 as a Diagnostic Biomarker and a Potential Tumor Suppressor

MicroRNA-202 (miR-202) is a member of the highly conserved let-7 family that was discovered in Caenorhabditis elegans and recently reported to be involved in cell differentiation and tumor biology. In humans, miR-202 was initially identified in the testis where it was suggested to play a role in spermatogenesis. Subsequent research showed that miR-202 is one of the micro-RNAs that are dysregulated in different types of cancer. During the last decade, a large number of investigations has fortified a role for miR-202 in cancer. However, its functions can be double-edged, depending on context they may be tumor suppressive or oncogenic. In this review, we highlight miR-202 as a potential diagnostic biomarker and as a suppressor of tumorigenesis and metastasis in several types of tumors. We link miR-202 expression levels in tumor types to its involved upstream and downstream signaling molecules and highlight its potential roles in carcinogenesis. Three well-known upstream long non-coding-RNAs (lncRNAs); MALAT1, NORAD, and NEAT1 target miR-202 and inhibit its tumor suppressive function thus fueling cancer progression. Studies on the downstream targets of miR-202 revealed PTEN, AKT, and various oncogenes such as metadherin (MTDH), MYCN, Forkhead box protein R2 (FOXR2) and Kirsten rat sarcoma virus (KRAS). Interestingly, an upregulated level of miR-202 was shown by most of the studies that estimated its expression level in blood or serum of cancer patients, especially in breast cancer. Reduced expression levels of miR-202 in tumor tissues were found to be associated with progression of different types of cancer. It seems likely that miR-202 is embedded in a complex regulatory network related to the nature and the sensitivity of the tumor type and therapeutic (pre)treatments. Its variable roles in tumorigenesis are mediated in part thought its oncogene effectors. However, the currently available data suggest that the involved signaling pathways determine the anti- or pro-tumorigenic outcomes of miR-202’s dysregulation and its value as a diagnostic biomarker.     

In Situ Characterization of Internal Damage in TRIP‐Steel/Mg‐PSZ Composites under Compressive Stress Using X‐Ray Computed Tomography

An experimental setup for in situ investigations under compressive stress using laboratory X‐ray computed tomography (XCT) was developed and successfully tested. Complete deformation curves can be taken. It could be shown that XCT scans are possible during brakes of the stopped in situ experiments. In this way the deformation behavior of defined sample regions can be investigated. This kind of experiments is well suited to investigate the deformation behavior of foams and other samples which are transferable for the X‐rays used. The compression of metal matrix composite foams lead to the cooperative collapse of connected cells. We observed deformation bands arising in regions of smaller cell wall thicknesses. The deformation was dependent on size, shape, and orientation of the cells under consideration. Obviously deformation bands start at bigger cells with a small cell wall thickness and some extension perpendicular to the deformation direction. The rising of this kind of deformation bands can be explained by the dramatic change of the stress distribution in the neighbor cells after the first brake of a cell wall.     

Constructing <Emphasis Type=Italic>n</Emphasis>-gram rules for natural language models through exploring the limitation of the Zipf–Mandelbrot law

The Zipf–Mandelbrot law is widely used to model a power-law distribution on ranked data. One of the best known applications of the Zipf–Mandelbrot law is in the area of linguistic analysis of the distribution of words ranked by their frequency in a text corpus. By exploring known limitations of the Zipf–Mandelbrot law in modeling the actual linguistic data from different domains in both printed media and online content, a new algorithm is developed to effectively construct n-gram rules for building natural language (NL) models required for a human-to-computer interface. The construction of statistically-oriented n-gram rules is based on a new computing algorithm that identifies the area of divergence between Zipf–Mandelbrot curve and the actual frequency distribution of the ranked n-gram text tokens extracted from a large text corpus derived from the online electronic programming guide (EPG) for television shows and movies. Two empirical experiments were carried out to evaluate the EPG-specific language models created using the new algorithm in the context of NL-based information retrieval systems. The experimental results show the effectiveness of the algorithm for developing low-complexity concept models with high coverage for the user’s language models associated with both typed and spoken queries when interacting with a NL-based EPG search interface.