ABSTRACT
When participants sort color samples into piles, Boster showed that their color groupings can resemble the "stages" of Kay & McDaniel's model of color term evolution. Boster concluded that both the unfolding of color piles in a sequential color sorting task and the unfolding of color terms according to Kay & McDaniel's model reveal how human beings understand color. If this is correct, then: (1) pile sorts should be reasonably robust across variations in the palette of colors to be sorted, as long as the palette contains good examples of Berlin & Kay's universal color categories, and (2) pile-sorting should be more related to lexical effects and less related to perceptual processes governed by similarity judgments alone. We report three studies on English speakers and Somali speakers (Study 1 only), where participants sorted colors into 2 6 piles. The three studies used varying numbers of palette colors (25, 30, or 145 colors) and varying chromaticity schemes (mainly hue, widely-separated in hue and lightness, or densely distributed at high chroma). We compared human sorting behavior to Kay & McDaniel's model and to the "optimal" patterns of color sorting predicted by Regier's well-formedness statistic, which quantifies the perceived similarity between colors. Neither hypothesis is confirmed by the results of our studies. Thus, we propose that color sorts are determined by pragmatic influences based on heuristics that are inspired by the palette of colors that are available and the task that the viewer is asked to perform.
ABSTRACT
Depression is a growing global crisis, with females at a higher rate of diagnosis than males. While the percentage of patients on prescribed antidepressants have tripled over the last two decades, we are still at a crossroad where the discrepancy lies between finding a drug to suit a patient and monitoring the abundance of it in the body to prevent unwanted side effects. Liquid Chromatography tandem mass spectrometry (LC-MS/MS) has garnered the attention of clinicians as a technique to accurately monitor therapeutic drugs in human serum with high specificity and accuracy. This may be a potential solution, but the challenge persists in the realm of sample preparation, where a method is automatable. We have developed and validated an LC-MS/MS-based assay for simultaneous quantification of 4 different classes of commonly prescribed antidepressants in women that is automated using a JANUS G3 Robotic Liquid Handler. Our method utilizes a simple sample preparation technique, utilizing only 20 µL of a serum sample, to accurately measure Bupropion, Citalopram, Desipramine, Imipramine, Olanzapine, Sertraline and Vilazodone across a range of 1.0 to 230 ng/mL. Our method exhibits a linearity of R2 ≥ 0.99 when detected in MRM mode and % CV of ≤ 20% for all analytes across the board. In addition, we have designed a prototype that can be utilized at a clinical mass spectrometry lab and assessed the long-term use of this prototype using an accelerated stability study. Overall, our developed method has the potential to be translated to clinical settings to monitor postpartum depression for a large number of patient samples using automation.
