Sensory systems are tuned by selection to maximize organismal fitness in particular environments. This tuning has implications for intraspecies communication, the maintenance of species boundaries, and speciation. Tuning of color vision largely depends on the sequence of the expressed opsin proteins. To improve tuning of visual sensitivities to shifts in habitat or foraging ecology over the course of development, many organisms change which opsins are expressed. Changes in this developmental sequence (heterochronic shifts) can create differences in visual sensitivity among closely related species. The genetic mechanisms by which these developmental shifts occur are poorly understood. Here, we use quantitative trait locus analyses, genome sequencing, and gene expression studies in African cichlid fishes to identify a role for the transcription factor Tbx2a in driving a switch between long wavelength sensitive (LWS) and Rhodopsin-like (RH2) opsin expression. We identify binding sites for Tbx2a in the LWS promoter and the highly conserved locus control region of RH2 which concurrently promote LWS expression while repressing RH2 expression. We also present evidence that a single change in Tbx2a regulatory sequence has led to a species difference in visual tuning, providing the first mechanistic model for the evolution of rapid switches in sensory tuning. This difference in visual tuning likely has important roles in evolution as it corresponds to differences in diet, microhabitat choice, and male nuptial coloration.

Legacy machinery poses a specific challenge when integrated into modern manufacturing lines. While modern machinery provides swift methods of integration and inbuilt predictive maintenance (PdM), older machines, while physically fully functional, are less attractive to reuse, a specific reason being their lack of ready-to-implement PdM hardware and models. More sustainable manufacturing operations can be achieved if the useable lifespan of functional older machinery can be extended through retrofittable PdM and modern industrial communication systems. While PdM models can be developed for a class (make/model) of machine with retrofitted sensing, it is often found that legacy machines will deviate greatly from their original form, through nonstandard maintenance and component replacement actions during their lengthy lifespan. This would mean that each legacy machine would require a custom PdM model, a cost often leading to the removal or nonusage of legacy machines. This paper proposes a framework designed for the generation of an efficient PdM algorithm which would allow for the reuse of legacy machines retrofitted with low-cost sensing in modern manufacturing for increased sustainability. Given a limited number of data samples collected from a machine to be maintained, we aim to predict a failure or/and maintenance time by making use of the difference between the characteristics of the variation of the healthy and unhealthy data collected from the machine. We measure the healthiness of the machine by using a Siamese network trained with a public dataset and fine-tuned with data samples obtained from machines with similar characteristics. Although we use different training and testing datasets coming from completely different sources, we obtain reasonable results thanks to the proposed technique. The results of simulations and the statistical analysis enable us to devise a transfer learning technique and a Siamese network employed for failure detection in the machine. The proposed system will allow for the continued use of older machines in modern facilities, enabling more sustainable manufacturing models.

Introduction: Testing facilities for COVID-19 were stood up around the country during the pandemic, but could not handle the demand. This study aimed to combine a mobile application (App) with an at-home test kit to facilitate home-based testing. Methods: After integrating an App with an at-home testing service, we measured the time between sample collection and notification of results. We recruited 92 volunteers to utilize the platform. Results: Sixty-one percent (55/92) responded to the survey. Median sample collection-to-result time was 2.2 days (IQR = 1.3–3.2). Eighty-two percent (45/55) found the self-test kit and App easy to use. Eighty-four percent agreed that the combined solution is an acceptable way to receive health care services. Discussion: Decreasing testing time and providing timely test results improve care access and decrease the risk of infection. Combining a tailored App with an at-home testing service is a feasible solution to reaching that goal.

Extended-duration work shifts (≥ 24 hours), the cornerstone of medical education, have been associated with reduced sleep first-year resident physicians in a single-site study. We compared more senior resident physician work hours and sleep habits in a multi-center clustered-randomized crossover clinical trial that randomized resident physicians to an Extended Duration Work Roster (EDWR) with extended-duration (≥24 hours) shifts or a Rapidly Cycling Work Roster (RCWR) where scheduled shift lengths were limited to no more than 16 consecutive hours. Across six U.S. academic medical centers, we enrolled 302 resident physicians in their second or more senior postgraduate year. They completed 370 one-month pediatric intensive care unit rotations. Sleep was objectively estimated with wrist-worn actigraphs. Work hours and subjective sleep duration were reported in an electronic daily diary. Resident physicians work hours were reduced by 10% during the RCWR (61.9 ± 4.8 hours compared to 68.4 ± 7.4 hours during the EDWR; p<0.0001). During the RCWR, 73% of work hours occurred within shifts of ≤16 consecutive hours. In contrast, during the EDWR 38% of work hours occurred on shifts of ≤16 consecutive hours. Resident physicians obtained significantly more sleep per week on the RCWR (52.9 ± 6.0 hours) compared to the EDWR (49.1 ± 5.8 hours, p<0.0001). The percentage of 24-hour intervals with less than 4 hours of actigraphically measured sleep was 9% on the RCWR and 25% on the EDWR (p<0.0001). During the RCWR, 4% of work hours were preceded by two or fewer hours of sleep in the preceding 24 hours, as compared to 10% of work hours during the EDWR (p16 consecutive hour shifts in more senior resident physicians. Sleep duration was increased and resident physicians were more rested while caring for patients. Additional research is needed to optimize scheduling practices that ensure sufficient sleep prior to all work shifts. ROSTERS supported by National Heart, Lung, and Blood Institute (U01-HL-111478, U01-HL-111691).

This study evaluated the extent to which psychotropic medication experts agreed on psychiatric/behavior diagnoses derived from 30 individuals’ psychotropic medication regimens. Three medication experts reviewed the medication regimens and inferred one or more diagnoses based on the medication listed. Thereafter, we used kappa statistical analyses and category-by-category analyses to evaluate agreement of diagnoses (a) across all three reviewers for two time points (separated by 8 to 14 months) covered by each data set, (b) across each pairing of reviewers at the two time points, and (c) within each reviewer across both time points. Between-reviewer kappa statistical analyses of first- and last-regimen data yielded only five instances with excellent agreement and 13 instances of poor agreement. All remaining instances indicated various levels of disagreement. Similarly, within-expert kappa statistical analyses showed two instances with excellent agreement, four instances with poor agreement, and the remaining instances with various levels of disagreement. Overall, the highest kappa values were attached to low-count regimens, while most scores, regardless of medication count, were low and indicated potential disagreement. The category-by-category analyses yielded similar results. A secondary, conditional analysis revealed higher agreements between and within reviewers when medication regimens contained psychotropic medications typically prescribed to individuals diagnosed with Attention Deficit Hyperactivity Disorder.