Quotients of S2×S2S^2\times{S^2}

We consider closed topological 4-manifolds $M$ with universal cover ${S^2\times{S^2}}$ and Euler characteristic $\chi(M) = 1$. All such manifolds with $\pi=\pi_1(M)\cong {\mathbb Z}/4$ are homotopy equivalent. In this case, we show that there are four homeomorphism types, and propose a candidate for a smooth example which is not homeomorphic to the geometric quotient. If $\pi\cong {\mathbb Z}/2 \times {\mathbb Z}/2$, we show that there are three homotopy types (and between 6 and 24 homeomorphism types).Comment: 18 page

Regulation of Telomerase Activity by Rab5 Guanine Nucleotide Exchange Factors

Many cancerous cells display abnormalities in the signal transduction pathways responsible for responding to extracellular growth factors. Growth factors, such as insulin-like growth factor I, represent a major class of mitogenic ligands that can initiate the mitogen activated protein kinase (MAPK) pathway. The role of the MAPK pathway in transducing growth signals to the interior of the cell and subsequently stimulating cell growth and proliferation is highlighted by the fact that roughly one quarter of all human tumors contain mutant forms of Ras proteins. Ras interference 1 (Rin1) is involved in key steps of receptor mediated endocytosis and can potentially moderate signaling through the MAPK pathway. The possible connection between Rin1, an effector of the active form of Ras, and subsequent telomerase gene expression and activity is of particular interest as telomerase is active in roughly 85% of all cancers. In this study, we determine the effect of the expression of Rin1 on cellular proliferation as well as on telomerase gene expression and activity in several different human cancer cells. MDA-MB 231, MCF7, and MCF-12A breast cell lines expressing Rin1 exhibit reduced levels of proliferation of up to 31% following exposure to the IGF-1 growth factor. Telomerase activity and gene expression were also reduced by factors of up to 1.5 and 2.55 fold in the three breast cell lines studied when compared to control cells. Similarly, overexpression of Rin1 in human Yusik melanoma cells leads to reduced telomerase activity. Telomerase activity was determined by the telomeric repeat amplification protocol (TRAP) assay while gene expression was measured by RT-qPCR. Furthermore, our observations suggest that overexpression of the Rin1 Y561F mutant and Rin1 delta splice variant in MDA-MB 231 cells results in increased proliferation and telomerase activity in these cancer cells when compared to control cells. MDA-MB 231 cells expressing wild type Rin1 displayed lowered levels of phosphorylation for the p-44/42 (ERK), STAT3, and Ets2 transcription factors. Additionally, overexpression of the C-terminus region of Rin1 in these cells greatly reduced ERK phosphorylation. In summary, Rin1 may play a novel tumor suppressor role in modulating signaling through the Ras/MAPK pathway upon IGF-1 stimulation

Heartbeat stars and the ringing of tidal pulsations

With the advent of high precision photometry from satellites such as Kepler and CoRoT, a whole new layer of interesting and astounding astronomical objects has been revealed: heartbeat stars are an example of such objects. Heartbeat stars are eccen- tric ellipsoidal variables that undergo strong tidal interactions when the stars are almost in contact at the time of closest approach. These interactions deform of the stars and cause a notable light curve variation in the form of a tidal pulse. A subset of these objects (∼20%) show prominent tidally induced pulsations: pulsations forced by the binary orbit. We now have a fully functional code that models binary star features (using phoebe) and stellar pulsations simultaneously, enabling a complete and accurate heartbeat star model to be determined. In this paper we show the results of our new code, which uses emcee, a variant of mcmc, to generate a full set of stellar parameters. We further highlight the interesting features of KIC 8164262, including its tidally induced pulsations and resonantly locked pulsations

Validation of the frequency modulation technique applied to the pulsating Sct- Dor eclipsing binary star KIC 8569819

KIC 8569819 is an eclipsing binary star with an early F primary and G secondary in a 20.85-d eccentric orbit. The primary is a δ Sct–γ Dor star pulsating in both p modes and g modes. Using four years of Kepler Mission photometric data, we independently model the light curve using the traditional technique with the modelling code PHOEBE, and we study the orbital characteristics using the new frequency modulation technique. We show that both methods provide the equivalent orbital period, eccentricity and argument of periastron, thus illustrating and validating the FM technique. In the amplitude spectrum of the p-mode pulsations, we also discovered an FM signal compatible with a third body in the system, a low-mass M dwarf in an 861-d orbit around the primary pair. However, the eclipses show no timing variations, indicating that the FM signal is a consequence of the intrinsic change in pulsation frequency, thus providing a cautionary tale. Our analysis shows the potential of the FM technique using Kepler data, and we discuss the prospects to detect planets and brown dwarfs in Kepler data for A and F stars even in the absence of transits and with no spectroscopic radial velocity curves. This opens the possibility of finding planets orbiting hotter stars that cannot be found by traditional techniques

Advances in item response theory and applications: an introduction

Test theories can be divided roughly into two categories. The first is classical test theory, which dates back to Spearman’s conception of the observed test score as a composite of true and error components, and which was introduced to psychologists at the beginning of this century. Important milestones in its long and venerable tradition are Gulliksen’s Theory of Mental Tests (1950) and Lord and Novick’s Statistical Theories of Mental Test Scores (1968). The second is item response theory, or latent trait theory, as it has been called until recently. At the present time, item response theory (IRT) is having a major impact on the field of testing. Models derived from IRT are being used to develop tests, to equate scores from nonparallel tests, to investigate item bias, and to report scores, as well as to address many other pressing measurement problems (see, e.g., Hambleton, 1983; Lord, 1980). IRT differs from classical test theory in that it assumes a different relation of the test score to the variable measured by the test. Although there are parallels between models from IRT and psychophysical models formulated around the turn of the century, only in the last 10 years has IRT had any impact on psychometricians and test users. Work by Rasch (1980/1960), Fischer (1974), 9 Birnbaum (1968), ivrighi and Panchapakesan (1969), Bock (1972), and Lord (1974) has been especially influential in this turnabout; and Lazarsfeld’s pioneering work on latent structure analysis in sociology (Lazarsfeld, 1950; Lazarsfeld & Henry, 1968) has also provided impetus. One objective of this introduction is to review the conceptual differences between classical test theory and IRT. A second objective is to introduce the goals of this special issue on item response theory and the seven papers. Some basic problems with classical test theory are reviewed in the next section. Then, IRT approaches to educational and psychological measurement are presented and compared to classical test theory. The final two sections present the goals for this special issue and an outline of the seven invited papers

Introducing a framework to assess newly created questions with Natural Language Processing

Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., learning path customization, drop-out prediction). However, the questions have to be assessed as well and, although it is possible to estimate with IRT the characteristics of questions that have already been answered by several students, this technique cannot be used on newly generated questions. In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices. We implement one model using this framework and test it on a real-world dataset provided by CloudAcademy, showing that it outperforms previously proposed models, reducing by 6.7% the RMSE for difficulty estimation and by 10.8% the RMSE for discrimination estimation. We also present the results of an ablation study performed to support our features choice and to show the effects of different characteristics of the questions' text on difficulty and discrimination.Comment: Accepted at the International Conference of Artificial Intelligence in Educatio