Digital LED Pixels: Instructions for use and a characterization of their properties

This article details how to control light emitting diodes (LEDs) using an ordinary desktop computer. By combining digitally addressable LEDs with an off-the-shelf microcontroller (Arduino), multiple LEDs can be controlled independently and with a high degree of temporal, chromatic, and luminance precision. The proposed solution is safe (can be powered by a 5-V battery), tested (has been used in published research), inexpensive (∼ $60 +$2 per LED), highly interoperable (can be controlled by any type of computer/operating system via a USB or Bluetooth connection), requires no prior knowledge of electrical engineering (components simply require plugging together), and uses widely available components for which established help forums already exist. Matlab code is provided, including a ‘minimal working example’ of use suitable for use by beginners. Properties of the recommended LEDs are also characterized, including their response time, luminance profile, and color gamut. Based on these, it is shown that the LEDs are highly stable in terms of both luminance and chromaticity, and do not suffer from issues of warm-up, chromatic shift, and slow response times associated with traditional CRT and LCD monitor technology

Body composition and body fat distribution are related to cardiac autonomic control in non-alcoholic fatty liver disease patients

BACKGROUND/OBJECTIVES: Heart rate recovery (HRR), a cardiac autonomic control marker, was shown to be related to body composition (BC), yet this was not tested in non-alcoholic fatty liver disease (NAFLD) patients. The aim of this study was to determine if, and to what extent, markers of BC and body fat (BF) distribution are related to cardiac autonomic control in NAFLD patients. SUBJECTS/METHODS: BC was assessed with dual-energy X-ray absorptiometry in 28 NAFLD patients (19 men, 51±13 years, and 9 women, 47±13 years). BF depots ratios were calculated to assess BF distribution. Subjects’ HRR was recorded 1 (HRR1) and 2 min (HRR2) immediately after a maximum graded exercise test. RESULTS: BC and BF distribution were related to HRR; particularly weight, trunk BF and trunk BF-to-appendicular BF ratio showed a negative relation with HRR1 (r 1⁄4 0.613, r 1⁄4 0.597 and r 1⁄4 0.547, respectively, Po0.01) and HRR2 (r 1⁄4 0.484, r 1⁄4 0.446, Po0.05, and r 1⁄4 0.590, Po0.01, respectively). Age seems to be related to both HRR1 and HRR2 except when controlled for BF distribution. The preferred model in multiple regression should include trunk BF-to-appendicular BF ratio and BF to predict HRR1 (r2 1⁄4 0.549; Po0.05), and trunk BF-to-appendicular BF ratio alone to predict HRR2 (r2 1⁄4 0.430; Po0.001). CONCLUSIONS: BC and BF distribution were related to HRR in NAFLD patients. Trunk BF-to-appendicular BF ratio was the best independent predictor of HRR and therefore may be best related to cardiovascular increased risk, and possibly act as a mediator in age-related cardiac autonomic control variation.info:eu-repo/semantics/publishedVersio

Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

The cerebral cortex is an anatomically divided and functionally specialized structure. It includes distinct areas, which work on different states over time. The structural features of spiking activity in sensory cortices have been characterized during spontaneous and evoked activity. However, the coordination among cortical and sub-cortical neurons during spontaneous activity across different states remains poorly characterized. We addressed this issue by studying the temporal coupling of spiking variability recorded from primary sensory cortices and hippocampus of anesthetized or freely behaving rats. During spontaneous activity, spiking variability was highly correlated across primary cortical sensory areas at both small and large spatial scales, whereas the cortico-hippocampal correlation was modest. This general pattern of spiking variability was observed under urethane anesthesia, as well as during waking, slow-wave sleep and rapid-eye-movement sleep, and was unchanged by novel stimulation. These results support the notion that primary sensory areas are strongly coupled during spontaneous activity.project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). NAPV was supported by Centro Universitario do Rio Grande do Norte, Champalimaud Foundation, and Brazilian National Council for Scientific and Technological Development (CNPq, Grant 249991/2013-6), CC-S (SFRH/BD/51992/2012). AJR (IF/00883/2013). SR by UFRN, CNPq (Research Productivity Grant 308775/2015-5), and S. Paulo Research Foundation FAPESP - Center for Neuromathematics (Grant 2013/07699-0)info:eu-repo/semantics/publishedVersio