Short range electromagnetic interface using 0.35 µm CMOS blocks for temperature monitoring in isolated areas.

INTRODUCTION: Infra-red (IR) and visible light (VL) based systems developed for transmission of information about physical quantities (e.g. humidity, temperature) out from closed areas, cannot be effectively employed in case of specific conditions in a targeted environment (because of fog or vapor for example). OBJECTIVES: In this work, we introduce a concept of wireless short-range transmitter and receiver to sense physical quantities, for instance temperature, with slow variation. The proposed concept is able to transmit analog-based information from isolated environments (e.g. aquariums or environments for plant growing) with high immunity against vapor and fog that limits standard optical (laser, IR band) methods of communication. METHODS: In this work, a new concept of short range radiofrequency (RF) communication device consisting of transmitting and receiving parts build from active devices fabricated in 0.35 µm I3T25 3.3 VCMOS process and ferrite antennas is selected. RF part uses medium-wave propagation within 10 mm distance at frequency 700 kHz. Such an approach offers minimal path loss of the radiated energy of a signal and low-gain amplification required for restoration of similar levels as available at the transmitting side. RESULTS: The processing of base-band signals of simple (sine wave) and complex (electrocardiogram) character was verified experimentally through the system. Application example of temperature monitoring in a closed environment, based on a temperature sensor (thermistor), verifies operationability in temperature range from 10 °C up to 50 °C. CONCLUSION: Compared to state-of-the-art solution, the presented concept has several advantages, for instance: less complexity; using of simpler type of modulation and demodulation; lower power consumption and significantly reduced issues caused by an environment with special transmission conditions (e.g. fog and vapor). The obtained results are in good agreement with expectations. Among others, the presented system brings beneficial performances for similar applications targeting on monitoring of low-frequency or slowly varying signals.

ln silico simulation of the interaction among autoregulatory mechanisms regulating cerebral blood flow rate in the healthy and systolic heart failure conditions during exercise.

In this study, a computational model was proposed to assess the interaction among systemic arteriolar resistance control, heart rate control, ventricular elastance control, venous compliance control, respiratory control, cerebral autoregulation mechanisms, and cerebral CO2 reactivity for both healthy and heart failure conditions. The aim of the study is to develop a computational model to evaluate cerebral blood flow rate during exercise for both healthy and systolic heart failure conditions. The simulations were performed at rest and during exercise. Furthermore, Monte Carlo analysis was used to estimate the range of the controlled parameters for each condition. The mean arterial pressure increased progressively with respect to workload during exercise in both healthy and heart failure conditions. Total cerebral blood flow rate was found 730 mL/min at rest in the healthy cardiovascular system model. As for the simulation during exercise, the increments in cerebral blood flow rate were 11% at 25 W workload, 20% at 50 W workload, and 24% at 75 W workload. The left ventricular ejection fraction decreased from 54 to 26% in the cardiovascular model simulating heart failure. Also, total cerebral blood flow rate decreased to 604 mL/min at rest in the cardiovascular system model simulating heart failure. The increments in cerebral blood flow rate in the simulation during exercise were 14% at 25 W workload, 24% at 50 W workload, and 30% at 75 W workload in the case of heart failure. The proposed numerical model simulates cerebral blood flow rate within physiological range during exercise and heart failure.

A multi-spectral myelin annotation tool for machine learning based myelin quantification.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.