Learning to live in thick interface

As media platforms shift towards more dynamic interfaces, the separation between user and content grows infinitely. While advertised as thin, light, and seamless, these platforms mask a thick and complicated space in which society must navigate. This is what I call the “Thick Interface.” The Thick Interface is the portal we use to toggle back and forth and through which we communicate. It is solid and porous, physical and digital, enhancing and diminishing. It may also be a combination of these things simultaneously, or none at all. My work highlights—rather than masks—the complexity of this space through interaction, participation, and analogy. I visualize and reveal the relationship between the decisions we make in contemporary media platforms and the ramifications of those decisions. Throughout this thesis, slowness and disruption are valued over speed and invisibility. Inside the Thick Interface, I argue that the most valuable tool is not a specific software or markup language; it is the glitch. The glitch is the moment where the thickness of the interface is revealed. Defined as a temporary disruption that provides resistance, has materiality, and leaves a residue of its existence, the glitch agitates the entanglement of our digital and physical experiences. Through designing for and expanding glitches, my work enhances and uncovers the materiality of the surfaces and spaces with which we interact. Offering alternative methods for graphic design thinking, it facilitates understanding of the relationship between tactile and virtual moments, crafting experiences that migrate between environments and add layers of interference to reveal that which goes unnoticed. The graphic designer is more than just a stylist of the edges, the data, and the periphery of these systems. He is an interface in his own right, visualizing the reality of the systems themselves. In this context, the practice of graphic design expands beyond the page as a position of establishing frameworks for how we see, clarify, understand, and interact in evolving environments through narrative, tactility, and spatial metaphors

Comparison of Forest Stand Edges in Riparian and Mesic Habitats Along Watts Bar Reservoir Shoreline

Approximately fifty years ago, the landscape upslope from the natural, riverine position banks of the Tennessee River was inundated by the closing of Watts Bar Dam. On the currently forested shoreline, habitats within the direct influence of the reservoir pool are riparian. Those above that influence are mesic. The purpose of this research was to determine compositional and structural differences between edges of mature forest stands established in a riparian habitat and those established in a mesic habitat along Watts Bar Reservoir shoreline. Thirty quadrats were placed on shoreline sites with mature, minimally-disturbed forest stands: 15 in a riparian habitat and 15 in a mesic habitat. Riparian and mesic habitats were distinguished by the hydric influence of the depth-to-subsurface lateral pool flow. A habitat was identified as riparian if subsurface lateral pool flow was estimated to be less than 0.5 m to soil surface (i.e., a low-lying area) and mesic if greater than or equal to 0.5 m (a topographically-elevated area). Each quadrat was 4 m wide x 25 m long and was located along the pool with the lengthwise edge being the summer pool line. Forest stand characteristics that were compared included vegetation structure (e.g., basal area, canopy height, canopy and edge closures) and composition (e.g., species diversity and richness, species importance values). Nonparametric statistics were employed for this comparison with supporting data provided by Two Way Indicator Species Analysis (TWINSPAN}, a clustering technique. Detrended Correspondence Analysis (DCA), an ordination technique, was further employed to determine whether any predominant underlying environmental gradient could be detected among the quadrats based on canopy species distribution. Results showed that sampled stands in riparian and mesic habitats were similar in productivity based on basal area, but differed significantly in their structure and composition. Stands in the mesic shoreline habitat exhibited characteristics of unmanaged broadleaf mesic forests. Twenty-nine hardwood taxa were represented in the canopy with a predominance of oaks and hickories. Stands in the riparian shoreline habitat were compositionally similar to regional bottomland forests and were limited to 16 canopy species with a predominance of Acer saccharinum. An assessment of similarity in canopy species of the two habitats yielded a Coefficient of Community of 0.33. The arboreal community in the mesic habitat was also significantly richer and more diverse than the community in the riparian habitat. TWINSPAN and DCA confirmed compositional dissimarilarity in sampled habitat stands. TWINSPAN partitioned mesic and riparian quadrats into two separate clusters. DCA segregated quadrats by habitat along an underlying environmental gradient. Analysis of this gradient indicated that it was related to subsurface lateral pool flow. Separate DCA analyses of mesic and riparian quadrats showed no predominant environmental gradient within either habitat. Structurally, riparian stands were significantly shorter, more open in their canopy, and denser in understory and edge front than mesic stands. Riparian stands characteristically presented a dense curtain-like edge cover composed of three common understory species, Cornus amomum, Alnus serrulata, and Ligustrum sinens

Hijacking the Neuronal NMDAR Signaling Circuit to Promote Tumor Growth and Invasion

SummaryGlutamate and its receptor N-methyl-D-aspartate receptor (NMDAR) have been associated with cancer, although their functions are not fully understood. Herein, we implicate glutamate-driven NMDAR signaling in a mouse model of pancreatic neuroendocrine tumorigenesis (PNET) and in selected human cancers. NMDAR was upregulated at the periphery of PNET tumors, particularly invasive fronts. Moreover, elevated coexpression of NMDAR and glutamate exporters correlated with poor prognosis in cancer patients. Treatment of a tumor-derived cell line with NMDAR antagonists impaired cancer cell proliferation and invasion. Flow conditions mimicking interstitial fluid pressure induced autologous glutamate secretion, activating NMDAR and its downstream MEK-MAPK and CaMK effectors, thereby promoting invasiveness. Congruently, pharmacological inhibition of NMDAR in mice with PNET reduced tumor growth and invasiveness. Therefore, beyond its traditional role in neurons, NMDAR may be activated in human tumors by fluid flow consequent to higher interstitial pressure, inducing an autocrine glutamate signaling circuit with resultant stimulation of malignancy

Charge structure in volcanic plumes: a comparison of plume properties predicted by an integral plume model to observations of volcanic lightning during the 2010 eruption of Eyjafjallajökull, Iceland

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0700-7) contains supplementary material, which is available to authorized users

Hallmarks of Cancer: The Next Generation

The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list—reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the “tumor microenvironment.” Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

An ordinary differential equation model for the multistep transformation to cancer

Cancer is viewed as a multistep process whereby a normal cell is transformed into a cancer cell through the acquisition of mutations. We reduce the complexities of cancer progression to a simple set of underlying rules that govern the transformation of normal cells to malignant cells. In doing so, we derive an ordinary differential equation model that explores how the balance of angiogenesis, cell death rates, genetic instability, and replication rates give rise to different kinetics in the development of cancer. The key predictions of the model are that cancer develops fastest through a particular ordering of mutations and that mutations in genes that maintain genomic integrity would be the most deleterious type of mutations to inherit. In addition, we perform a sensitivity analysis on the parameters included in the model to determine the probable contribution of each. This paper presents a novel approach to viewing the genetic basis of cancer from a systems biology perspective and provides the groundwork for other models that can be directly tied to clinical and molecular data.Comment: 12 pages, submitted to Journal of Theoretical Biolog

The effect of PD-L1 testing on the cost-effectiveness and economic impact of immune checkpoint inhibitors for the second-line treatment of NSCLC

Background: Immune checkpoint inhibitors improve outcomes compared with chemotherapy in lung cancer. Tumor PD-L1 receptor expression is being studied as a predictive biomarker. The objective of this study was to assess the cost-effectiveness and economic impact of second-line treatment with nivolumab, pembrolizumab, and atezolizumab with and without the use of PD-L1 testing for patient selection. Design: We developed a decision-analytic model to determine the cost-effectiveness of PD-L1 assessment and second-line immunotherapy versus docetaxel. The model used outcomes data from randomized clinical trials (RCTs) and drug acquisition costs from the United States. Thereafter, we used epidemiologic data to estimate the economic impact of the treatment. Results: We included four RCTs (2 with nivolumab, 1 with pembrolizumab, and 1 with atezolizumab). The incremental quality-adjusted life year (QALY) for nivolumab was 0.417 among squamous tumors and 0.287 among non-squamous tumors and the incremental cost-effectiveness ratio (ICER) were $155 605 and$187 685, respectively. The QALY gain in the base case for atezolizumab was 0.354 and the ICER was $215 802. Compared with treating all patients, the selection of patients by PD-L1 expression improved incremental QALY by up to 183$98 421. Patient selection also reduced the budget impact of immunotherapy. Conclusion: The use of PD-L1 expression as a biomarker increases cost-effectiveness of immunotherapy but also diminishes the number of potential life-years saved.info:eu-repo/semantics/publishedVersio