Freshwater forcing control on early-Holocene South American monsoon

Climate anomalies due to Lake Agassiz outbursts and Hudson Bay ice dome melting are commonly considered triggers of North American atmospheric cooling. However, in the Southern Hemisphere, these freshwater fluxes are mostly associated with increased precipitation and a possible intensification of the South American Monsoon System (SAMS). Here, we tested how the SAMS responded to early-Holocene meltwater events. Based on both proxy data and simulations, we find that sea surface temperatures (SSTs) and precipitation indicate a freshwater-driven strengthening of the SAMS due to a weakening of the South Atlantic subtropical dipole. Simulated SAMS strengthening accounts for up to 50% of the variance in early-Holocene precipitation in South America. In turn, changes in the South Atlantic Subtropical Dipole accounts for up to 31% of the variance in South Atlantic SSTs. Additionally, we propose that the stronger SAMS in the early Holocene might have been due to a freshwater-driven weakening of the southeasterly trade winds. Slower trade winds weaken the zonal and meridional surface water transport, concentrating warm waters in the northeastern South Atlantic

The Communicability of Graphical Alternatives to Tabular Displays of Statistical Simulation Studies

Simulation studies are often used to assess the frequency properties and optimality of statistical methods. They are typically reported in tables, which may contain hundreds of figures to be contrasted over multiple dimensions. To assess the degree to which these tables are fit for purpose, we performed a randomised cross-over experiment in which statisticians were asked to extract information from (i) such a table sourced from the literature and (ii) a graphical adaptation designed by the authors, and were timed and assessed for accuracy. We developed hierarchical models accounting for differences between individuals of different experience levels (under- and post-graduate), within experience levels, and between different table-graph pairs. In our experiment, information could be extracted quicker and, for less experienced participants, more accurately from graphical presentations than tabular displays. We also performed a literature review to assess the prevalence of hard-to-interpret design features in tables of simulation studies in three popular statistics journals, finding that many are presented innumerately. We recommend simulation studies be presented in graphical form

Allocation to Groups: Examples of Lord\u27s Paradox

Background Educational and developmental psychologists often examine how groups change over time. Two analytic procedures – analysis of covariance (ANCOVA) and the gain score model – each seem well suited for the simplest situation, with just two groups and two time points. They can produce different results, what is known as Lord\u27s paradox. Aims Several factors should influence a researcher\u27s analytic choice. This includes whether the score from the initial time influences how people are assigned to groups. Examples are shown, which will help to explain this to researchers and students, and are of educational relevance. It is shown that a common method used to measure school effectiveness is biased against schools that serve students from groups that are historically poor performing. Methods and results The examples come from sports and measuring educational effectiveness (e.g., for teachers or schools). A simulation study shows that if the covariate influences group allocation, the ANCOVA is preferred, but otherwise, the gain score model may be appropriate. Regression towards the mean is used to account for these findings. Conclusions Analysts should consider the relationship between the covariate and group allocation when deciding upon their analytic method. Because the influence of the covariate on group allocation may be complex, the appropriate method may be complex. Because the influence of the covariate on group allocation may be unknown, the choice of method may require several assumptions

NMDAR inhibition-independent antidepressant actions of ketamine metabolites

Major depressive disorder afflicts ~16 percent of the world population at some point in their lives. Despite a number of available monoaminergic-based antidepressants, most patients require many weeks, if not months, to respond to these treatments, and many patients never attain sustained remission of their symptoms. The non-competitive glutamatergic N-methyl-D-aspartate receptor (NMDAR) antagonist, (R,S)-ketamine (ketamine), exerts rapid and sustained antidepressant effects following a single dose in depressed patients. Here we show that the metabolism of ketamine to (2S,6S;2R,6R)-hydroxynorketamine (HNK) is essential for its antidepressant effects, and that the (2R,6R)-HNK enantiomer exerts behavioural, electroencephalographic, electrophysiological and cellular antidepressant actions in vivo. Notably, we demonstrate that these antidepressant actions are NMDAR inhibition-independent but they involve early and sustained α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor activation. We also establish that (2R,6R)-HNK lacks ketamine-related side-effects. Our results indicate a novel mechanism underlying ketamine’s unique antidepressant properties, which involves the required activity of a distinct metabolite and is independent of NMDAR inhibition. These findings have relevance for the development of next generation, rapid-acting antidepressants

On the Use of Semantic-Based AIG to Automatically Generate Programming Exercises

In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality

Empathy Manipulation Impacts Music-Induced Emotions: A Psychophysiological Study on Opera

This study investigated the effects of voluntarily empathizing with a musical performer (i.e., cognitive empathy) on music-induced emotions and their underlying physiological activity. N = 56 participants watched video-clips of two operatic compositions performed in concerts, with low or high empathy instructions. Heart rate and heart rate variability, skin conductance level (SCL), and respiration rate (RR) were measured during music listening, and music-induced emotions were quantified using the Geneva Emotional Music Scale immediately after music listening. Listening to the aria with sad content in a high empathy condition facilitated the emotion of nostalgia and decreased SCL, in comparison to the low empathy condition. Listening to the song with happy content in a high empathy condition also facilitated the emotion of power and increased RR, in comparison to the low empathy condition. To our knowledge, this study offers the first experimental evidence that cognitive empathy influences emotion psychophysiology during music listening