University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

American Brain Tumor Association Adolescent and Young Adult Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008-2012

Abstract: Brain and central nervous system (CNS) tumors found in adolescents and young adults (AYA) are a distinct group of tumors that pose challenges not only to treatment but also to reporting. Overall, cancer that occurs in this age group is biologically distinct from those that occur in both younger and older age groups1,2 posing significant challenges for clinicians. The most commonly diagnosed histologies in AYA vary from those in both children age (0-14 years), and older adults (40+ years).3,4 Prognosis and expected survival also varies between younger and older adults, with those who are diagnosed with brain and CNS tumors at younger ages having significantly longer survival. Despite this survival advantage, recent analyses have reported that while cancer survival has been improving overall, AYA have not experienced these same increases in survival and in some cases may have worse survival than those cancers diagnosed in persons over age 40 years.5 This report provides an in depth analyses of the epidemiology of brain and CNS tumors in adolescents and young adults in the United States (US), and is the first report to provide histology-specific statistics in this population for both malignant and non-malignant brain and other CNS tumors. In 2006, the National Institutes of Health, the National Cancer Institute (NCI) and the LiveStrong Young Adult Alliance conducted a Progress Review Group to investigate AYA Oncology entitled Research and care imperatives for adolescents and young adults with cancer: A Report of the Adolescent and Young Adult Oncology Progress Review Group. This group established the standard age range for the AYA group as 15-39 years. This is the age range used by the Surveillance Epidemiology and End Results (SEER) program of the NCI, as well as in the 2015 CBTRUS Statistical Report.3,6 Brain tumors and other CNS tumors are less common in AYA than in older adults, but they have a higher incidence than brain tumors in children (age 0-14 years).3 Non-malignant tumors are significantly more common in AYA than children (Average annual age adjusted incidence in age 15-39 years: 6.17 per 100,000; age 0-14 years: 0.79 per 100,000), while malignant tumors are slightly more common in those age 0-14 years (Average annual age adjusted incidence in 15-39 years old: 3.26 per 100,000; 0-14 years old: 3.73 per 100,000). While a rare cancer overall, brain and CNS tumors are among the most common cancers occurring in this age group (4.4% of all cancers in those age 15-39 years as compared to 32.4% in children age 0-14 years, and 2.2% of cancers in adults age 40+ years).3,4,7 Malignant brain and CNS tumors are the 11th most common cancer and the 3rd most common cause of cancer death7,8 in the AYA population. Incidence rates of brain tumors overall as well as specific histologies vary significantly by age. It is, therefore, important to provide an accurate statistical assessment of brain and other CNS tumors in the adolescent and young adult population to better understand their impact on the US population and to serve as a reference for afflicted individuals, for researchers investigating new therapies and for clinicians treating patients.

Extraordinary denudation in the Sichuan Basin: Insights from low- temperature thermochronology adjacent to the eastern margin of the Tibetan Plateau

Abstract: The eastern margin of the Tibetan Plateau combines very high relief with almost no Tertiary foreland sedimentation and little evidence of Cenozoic tectonic shortening. While river incision and landscape development at the plateau margin have received significant attention over the last decade, little is known about the Cenozoic development of the adjacent Sichuan Basin. Here we assess the Cenozoic thermal history of this basin using detrital apatite fission track (AFT) and (U-Th)/He techniques and establish the presence of an exhumed AFT paleopartial annealing zone across much of the basin. This observation, combined with stratigraphic and borehole sections and inverse modeling of confined apatite fission tracks, indicates that the strata within the basin have undergone accelerated cooling after similar to 40 Ma, consistent with the widespread erosion of similar to 1 to 4 km of overlying sedimentary material. This regional-scale erosion is most likely a response to changes in the Yangtze River system draining and removing sediment from the basin. The base-level fall associated with this erosion contributed to a relative increase in relief across the Longmen Shan and may have helped drive Miocene-Recent incision and unloading of the plateau margin.

AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back

Abstract: We present AutoTutor and Affective AutoTutor as examples of innovative 21st century interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors. AutoTutor constructs a cognitive model of students' knowledge levels by analyzing the text of their typed or spoken responses to its questions. The model is used to dynamically tailor the interaction toward individual students' zones of proximal development. Affective AutoTutor takes the individualized instruction and human-like interactivity to a new level by automatically detecting and responding to students' emotional states in addition to their cognitive states. Over 20 controlled experiments comparing AutoTutor with ecological and experimental controls such reading a textbook have consistently yielded learning improvements of approximately one letter grade after brief 30--60-minute interactions. Furthermore, Affective AutoTutor shows even more dramatic improvements in learning than the original AutoTutor system, particularly for struggling students with low domain knowledge. In addition to providing a detailed description of the implementation and evaluation of AutoTutor and Affective AutoTutor, we also discuss new and exciting technologies motivated by AutoTutor such as AutoTutor-Lite, Operation ARIES, GuruTutor, DeepTutor, MetaTutor, and AutoMentor. We conclude this article with our vision for future work on interactive and engaging intelligent tutoring systems.

Real-Valued Negative Selection Algorithm with Variable-Sized Detectors

Abstract: A new scheme of detector generation and matching mechanism for negative selection algorithm is introduced featuring detectors with variable properties. While detectors can be variable in different ways using this concept, the paper describes an algorithm when the variable parameter is the size of the detectors in real-valued space. The algorithm is tested using synthetic and real-world datasets, including time series data that are transformed into multiple-dimensional data during the preprocessing phase. Preliminary results demonstrate that the new approach enhances the negative selection algorithm in efficiency and reliability without significant increase in complexity.