Optimal Correction for Guessing in Multiple-Choice Tests

Building on Item Response Theory we introduce students’ optimal behavior in multiple-choice tests. Our simulations indicate that the optimal penalty is relatively high, because although correction for guessing discriminates against risk-averse subjects, this effect is small compared with the measurement error that the penalty prevents. This result obtains when knowledge is binary or partial, under different normalizations of the score, when risk aversion is related to knowledge and when there is a pass-fail break point. We also find that the mean degree of difficulty should be close to the mean level of knowledge and that the variance of difficulty should be high.

On the strategic equivalence of multiple-choice test scoring rules

A disadvantage of multiple-choice tests is that students have incentives to guess. To discourage guessing, it is common to use scoring rules that either penalize wrong answers or reward omissions. These scoring rules are considered equivalent in psychometrics, although experimental evidence has not always been consistent with this claim. We model students' decisions and show, first, that equivalence holds only under risk neutrality and, second, that the two rules can be modified so that they become equivalent even under risk aversion. This paper presents the results of a field experiment in which we analyze the decisions of subjects taking multiple-choice exams. The evidence suggests that differences between scoring rules are due to risk aversion as theory predicts. We also find that the number of omitted items depends on the scoring rule, knowledge, gender and other covariates.

Increased expression of cystine/glutamate antiporter in multiple sclerosis

Background: Glutamate excitotoxicity contributes to oligodendrocyte and tissue damage in multiple sclerosis (MS). Intriguingly, glutamate level in plasma and cerebrospinal fluid of MS patients is elevated, a feature which may be related to the pathophysiology of this disease. In addition to glutamate transporters, levels of extracellular glutamate are controlled by cystine/glutamate antiporter x(c)(-), an exchanger that provides intracellular cystine for production of glutathione, the major cellular antioxidant. The objective of this study was to analyze the role of the system x(c)(-) in glutamate homeostasis alterations in MS pathology. -- Methods: Primary cultures of human monocytes and the cell line U-937 were used to investigate the mechanism of glutamate release. Expression of cystine glutamate exchanger (xCT) was quantified by quantitative PCR, Western blot, flow cytometry and immunohistochemistry in monocytes in vitro, in animals with experimental autoimmune encephalomyelitis (EAE), the animal model of MS, and in samples of MS patients. -- Results and discussion: We show here that human activated monocytes release glutamate through cystine/glutamate antiporter x(c)(-) and that the expression of the catalytic subunit xCT is upregulated as a consequence of monocyte activation. In addition, xCT expression is also increased in EAE and in the disease proper. In the later, high expression of xCT occurs both in the central nervous system (CNS) and in peripheral blood cells. In particular, cells from monocyte-macrophage-microglia lineage have higher xCT expression in MS and in EAE, indicating that immune activation upregulates xCT levels, which may result in higher glutamate release and contribution to excitotoxic damage to oligodendrocytes. -- Conclusions: Together, these results reveal that increased expression of the cystine/glutamate antiporter system x(c)(-) in MS provides a link between inflammation and excitotoxicity in demyelinating diseases.

Multiple SNP Markers Reveal Fine-Scale Population and Deep Phylogeographic Structure in European Anchovy (Engraulis encrasicolus L.)

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Multiple table factor analysis applied to the higher education competences

Due to the recent implantation of the Bologna process, the definition of competences in Higher Education is an important matter that deserves special attention and requires a detailed analysis. For that reason, we study the importance given to severa! competences for the professional activity and the degree to which these competences have been achieved through the received education. The answers include also competences observed in two periods of time given by individuals of multiple characteristics. In this context and in order to obtain synthesized results, we propose the use of Multiple Table Factor Analysis. Through this analysis, individuals are described by severa! groups, showing the most important variability factors of the individuals and allowing the analysis of the common structure ofthe different data tables. The obtained results will allow us finding out the existence or absence of a common structure in the answers of the various data tables, knowing which competences have similar answer structure in the groups of variables, as well as characterizing those answers through the individuals.

Fine mapping and functional analysis of the multiple sclerosis risk gene CD6

CD6 has recently been identified and validated as risk gene for multiple sclerosis (MS), based on the association of a single nucleotide polymorphism (SNP), rs17824933, located in intron 1. CD6 is a cell surface scavenger receptor involved in T-cell activation and proliferation, as well as in thymocyte differentiation. In this study, we performed a haptag SNP screen of the CD6 gene locus using a total of thirteen tagging SNPs, of which three were non-synonymous SNPs, and replicated the recently reported GWAS SNP rs650258 in a Spanish-Basque collection of 814 controls and 823 cases. Validation of the six most strongly associated SNPs was performed in an independent collection of 2265 MS patients and 2600 healthy controls. We identified association of haplotypes composed of two non-synonymous SNPs [rs11230563 (R225W) and rs2074225 (A257V)] in the 2nd SRCR domain with susceptibility to MS (Pmax(T) permutation=161024). The effect of these haplotypes on CD6 surface expression and cytokine secretion was also tested. The analysis showed significantly different CD6 expression patterns in the distinct cell subsets, i.e. – CD4+ naı¨ve cells, P = 0.0001; CD8+ naı¨ve cells, P,0.0001; CD4+ and CD8+ central memory cells, P = 0.01 and 0.05, respectively; and natural killer T (NKT) cells, P = 0.02; with the protective haplotype (RA) showing higher expression of CD6. However, no significant changes were observed in natural killer (NK) cells, effector memory and terminally differentiated effector memory T cells. Our findings reveal that this new MS-associated CD6 risk haplotype significantly modifies expression of CD6 on CD4+ and CD8+ T cells.

Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields, surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built: (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model (1961-1996) and a test period (19972001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributed to the analogues-calculation stage.