University of California, Santa Barbara

About: University of California, Santa Barbara is a education organization based out in Santa Barbara, California, United States. It is known for research contribution in the topics: Population & Laser. The organization has 30281 authors who have published 80852 publications receiving 4626827 citations. The organization is also known as: UC Santa Barbara & UCSB.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Stability and Aggregation of Metal Oxide Nanoparticles in Natural Aqueous Matrices

Abstract: There is a pressing need for information on the mobility of nanoparticles in the complex aqueous matrices found in realistic environmental conditions. We dispersed three different metal oxide nanoparticles (TiO(2), ZnO and CeO(2)) in samples taken from eight different aqueous media associated with seawater, lagoon, river, and groundwater, and measured their electrophoretic mobility, state of aggregation, and rate of sedimentation. The electrophoretic mobility of the particles in a given aqueous media was dominated by the presence of natural organic matter (NOM) and ionic strength, and independent of pH. NOM adsorbed onto these nanoparticles significantly reduces their aggregation, stabilizing them under many conditions. The transition from reaction to diffusion limited aggregation occurs at an electrophoretic mobility from around -2 to -0.8 microm s(-1) V(-1) cm. These results are key for designing and interpreting nanoparticle ecotoxicity studies in various environmental conditions.

Interactive Multimodal Learning Environments Special Issue on Interactive Learning Environments: Contemporary Issues and Trends

Abstract: What are interactive multimodal learning environments and how should they be designed to promote students’ learning? In this paper, we offer a cognitive–affective theory of learning with media from which instructional design principles are derived. Then, we review a set of experimental studies in which we found empirical support for five design principles: guided activity, reflection, feedback, control, and pretraining. Finally, we offer directions for future instructional technology research.

Global life cycle releases of engineered nanomaterials

Abstract: Engineered nanomaterials (ENMs) are now becoming a significant fraction of the material flows in the global economy. We are already reaping the benefits of improved energy efficiency, material use reduction, and better performance in many existing and new applications that have been enabled by these technological advances. As ENMs pervade the global economy, however, it becomes important to understand their environmental implications. As a first step, we combined ENM market information and material flow modeling to produce the first global assessment of the likely ENM emissions to the environment and landfills. The top ten most produced ENMs by mass were analyzed in a dozen major applications. Emissions during the manufacturing, use, and disposal stages were estimated, including intermediate steps through wastewater treatment plants and waste incineration plants. In 2010, silica, titania, alumina, and iron and zinc oxides dominate the ENM market in terms of mass flow through the global economy, used mostly in coatings/paints/pigments, electronics and optics, cosmetics, energy and environmental applications, and as catalysts. We estimate that 63–91 % of over 260,000–309,000 metric tons of global ENM production in 2010 ended up in landfills, with the balance released into soils (8–28 %), water bodies (0.4–7 %), and atmosphere (0.1–1.5 %). While there are considerable uncertainties in the estimates, the framework for estimating emissions can be easily improved as better data become available. The material flow estimates can be used to quantify emissions at the local level, as inputs for fate and transport models to estimate concentrations in different environmental compartments.