Pharmacological studies of the involvement of hypothalamic prostaglandins in the regulation of thyrotropin secretion.

A case is made for the involvement of pituitary prostaglandins (PGs) in the regulation of thyrotropin (TSH) secretion by citing recent evidence that TSH release in vivo and in vitro is enhanced by treatment with exogenous PGs and is inhibited by drugs (e.g., indomethacin) that block PG synthesis. Pharmacological studies were then performed to test the hypothesis that hypothalamic PGs also affect TSH secretion indirectly via the appropriate hypothalamic hormones that regulate pituitary secretion. The inhibition of thyroidectomy-induced TSH secretion was used as an endpoint in choosing the best of several drugs purported to inhibit PG synthesis. The established effectiveness of indomethacin and aspirin were used for reference in testing the following drugs: naproxen, mefenamic acid, tranylcypromine, and phenelzine. Only naproxen was found to be effective, but since it was no more potent than indomethacin, the latter drug was used for subsequent work. Indomethacin was stereotaxically implanted into several hypothalamic regions known to regulate TSH secretion, and sequential plasma samples were analyzed for TSH by radioimmunoassay. Bilateral implants of indomethacin in the anterior hypothalamic area increased TSH secretion throughout the 72 hr period of study. Sham inplants at this site and indomethacin implants in other nearby sites were ineffective. These findings suggest that endogenous PGs play an inhibitory role in the hypothalamic regulation of pituitary secretion

Activity Classification Using Unsupervised Domain Transfer from Body Worn Sensors

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso

Factors modulating the secretion of thyrotropin and other hormones of the thyroid axis.

The first portion of this paper is devoted to an overview of the normal function of the hypothalamo-pituitary-thyroid axis. This section emphasizes areas of current research interest and it identifies several sites and mechanisms that are potentially important interfaces with toxins or toxic mechanisms. We then describe an in vitro technique for the continuous superfusion of enzymatically dispersed pituitary cells; this approach is particularly valuable in studying the dynamics of the TSH responses to the factors known (or suspected) to regulate TSH secretion in vivo. Using this technique, we have found that 10(-5)M prostaglandin (PG)I2 stimulates TSH secretion without altering the response to TRH (10(-8)M), and that this stimulation is not due to its rapid conversion to 6-keto PGF1 alpha. In contrast PGs of the E series (PGE1 and PGE2, 10(-5)M) increase responsiveness to TRH but have no effect alone. We found no effects of any of the other prostanoids tested (PGs A2, B2, F1 alpha, F2 alpha, thromboxanes A2 and B2, and the endoperoxide analog, U-44069. Somatostain (10(-9)M inhibits TRH-induced TSH secretion, but does not alter the responsiveness to PGI2. These findings suggest that somatostatin blocks TSH secretion at a point that is functionally prior to the involvement of the PGs, and perhaps does so by blocking synthesis or limiting availability of selected PGs

Using eye movements to explore switch costs in working memory.

Updating object locations in working memory (WM) is faster when the same object is updated twice in a row compared to updating another object. In analogy to repetition priming effects in perceptual attention, this object-switch cost in WM is thought of as being due to the necessity to shift attention internally from one object to another. However, evidence for this hypothesis is only indirect. Here, we used eye tracking and a classic model of perceptual attention to get a more direct handle on the different processes underlying switch costs in spatial WM. Eye-movement data revealed three different contributors to switch costs. First, overt attention was attracted initially towards locations of the previously updated object. Second, longer fixation periods preceded eye movements between locations of different objects as compared to (previous and new) locations of the same object, most likely due to disengaging and reorienting focal attention between objects. Third, longer dwell times at the to-be-updated location preceded manual responses for switch updates as compared to repeats, probably indicating increased uncertainty between competing sources of activity after the actual attention shift. Results can easily be interpreted with existing (perceptual) attention models that propose competitive activation in an attention map for target objects

Test-retest reliability for common tasks in vision science

Historically, research in cognitive psychology has sought to evaluate cognitive mechanisms according to the average response to a manipulation. Differences between individuals have been dismissed as “noise” with an aim toward characterising an overall effect and how it can inform human cognition. More recently, research has shifted toward appreciating the value of individual differences between participants and the insight gained by exploring the impacts of between-subject variation on human cognition. However, recent research has suggested that many robust, well-established cognitive tasks suffer from surprisingly low levels of test-retest reliability (Hedge, Powell, & Sumner, 2018). While the tasks may produce reliable effects at the group level (i.e., they are replicable), they may not produce a reliable measurement of a given individual. If individual performance on a task is not consistent from one time point to another, the task is therefore unfit for the assessment of individual differences. To evaluate the reliability of commonly used tasks in vision science, we tested a large sample of undergraduate students in two sessions (separated by 1-3 weeks). Our battery included tasks that spanned the range of visual processing from basic sensitivity (motion coherence) to transient spatial attention (useful field of view) to sustained attention (multiple-object tracking) to visual working memory (change detection). Reliabilities (intraclass correlations) ranged from 0.4 to 0.7, suggesting that most of these measures suffer from lower reliability than would be desired for research in individual differences. These results do not detract from the value of the tasks in an experimental setting; however, higher levels of test-retest reliability would be required for a meaningful assessment of individual differences. Implications for using tools from vision science to understand processing in both healthy and neuropsychological populations are discussed

The mapping between transformed reaction time costs and models of processing in aging and cognition.

Older adults tend to have slower response times (RTs) than younger adults on cognitive tasks. This makes the examination of domain-specific deficits in aging difficult, as differences between conditions in raw RTs (RT costs) typically increase with slower average RTs. Here, we examine the mapping between 2 established approaches to dealing with this confound in the literature. The first is to use transformed RT costs, with the z-score and proportional transforms both being commonly used. The second is to use mathematical models of choice RT behavior, such as the drift-diffusion model (Ratcliff, 1978). We simulated data for younger and older adults from the drift-diffusion model under 4 scenarios: (a) a domain specific deficit, (b) general slowing, (c) strategic slowing, and (d) a slowing of nondecision processes. In each scenario we varied the size of the difference between younger and older adults in the model parameters, and examined corresponding effect sizes and Type I error rates in the raw and transformed RT costs. The z-score transformation provided better control of Type I error rates than the raw or proportional costs, though did not fully control for differences in the general slowing and strategic slowing scenarios. We recommend that RT analyses are ideally supplemented by analyses of error rates where possible, as these may help to identify the presence of confounds. To facilitate this, it would be beneficial to include conditions that elicit below ceiling accuracy in tasks

The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.

Individual differences in cognitive paradigms are increasingly employed to relate cognition to brain structure, chemistry, and function. However, such efforts are often unfruitful, even with the most well established tasks. Here we offer an explanation for failures in the application of robust cognitive paradigms to the study of individual differences. Experimental effects become well established – and thus those tasks become popular – when between-subject variability is low. However, low between-subject variability causes low reliability for individual differences, destroying replicable correlations with other factors and potentially undermining published conclusions drawn from correlational relationships. Though these statistical issues have a long history in psychology, they are widely overlooked in cognitive psychology and neuroscience today. In three studies, we assessed test-retest reliability of seven classic tasks: Eriksen Flanker, Stroop, stop-signal, go/no-go, Posner cueing, Navon, and Spatial-Numerical Association of Response Code (SNARC). Reliabilities ranged from 0 to .82, being surprisingly low for most tasks given their common use. As we predicted, this emerged from low variance between individuals rather than high measurement variance. In other words, the very reason such tasks produce robust and easily replicable experimental effects – low between-participant variability – makes their use as correlational tools problematic. We demonstrate that taking such reliability estimates into account has the potential to qualitatively change theoretical conclusions. The implications of our findings are that well-established approaches in experimental psychology and neuropsychology may not directly translate to the study of individual differences in brain structure, chemistry, and function, and alternative metrics may be required

Accounting for test reliability in student progression: the reliable change index

Context Developed by Jacobson and Truax, the reliable change index (RCI) provides a measure of whether the change in an individual's score over time is within or beyond that which might be accounted for by measurement variability. In combination with measures of whether an individual's final score is closer to those of one population or another, this provides useful individual-level information that can be used to supplement traditional analyses. Objectives This article aims to highlight the potential of the RCI for use within medical education, particularly as a novel means of monitoring progress at the student level across successive test occasions or academic years. Methods We provide an example of how the RCI can be applied informatively to assessment evaluation, and discuss its wider usage. Conclusions The RCI approach can be used to identify and support failing students, as well as to determine best teaching and learning practices by identifying high-performing students. Furthermore, the individual-level nature of the RCI makes it well suited for educational research with small cohorts, as well as for tracking individual profiles within a larger cohort or addressing questions about individual performance that may be unanswerable at group level