Chitosan oligosaccharide alleviates the growth inhibition caused by physcion and synergistically enhances resilience in maize seedlings

The use of biopesticides has gradually become essential to ensure food security and sustainable agricultural production. Nevertheless, the use of single biopesticides is frequently suboptimal in agricultural production given the diversity of biotic and abiotic stresses. The present study investigated the effects of two biopesticides, physcion and chitosan-oligosaccharide (COS), alone and in combination, on growth regulation and antioxidant potential of maize seedlings by seed coating. As suggested from the results, physcion significantly inhibited the growth of the shoots of maize seedlings due to the elevated respiration rate. However, COS significantly reduced the growth inhibition induced by physcion in maize seedlings by lowering the respiration rate and increasing the content of photosynthetic pigments and root vigor, which accounted for lower consumption of photosynthesis products, a higher photosynthetic rate and a greater nutrient absorption rate. Thus, an improved growth was identified. As indicated from the in-depth research, the application of physcion and COS combination is more effective in down-regulated the malondialdehyde (MDA) content by facilitating the activities of the antioxidative enzymes (i.e., superoxide dismutase (SOD), catalase (CAT) and guaiacol peroxidase (G-POD)). Such results indicated that the combined use of physcion and COS neither affected the normal growth of maize seedlings, but also synergistically improved the antioxidant potential of the maize plants, resulting in plants with high stress resistance. Thus, the combined use of physcion and COS by seed coating in maize production has great potential to ensure yield and sustainable production of maize.

Ratio of Intensities of Blue and Red Light at Cultivation Influences Photosynthetic Light Reactions, Respiration, Growth, and Reflectance Indices in Lettuce

LED illumination can have a narrow spectral band; its intensity and time regime are regulated within a wide range. These characteristics are the potential basis for the use of a combination of LEDs for plant cultivation because light is the energy source that is used by plants as well as the regulator of photosynthesis, and the regulator of other physiological processes (e.g., plant development), and can cause plant damage under certain stress conditions. As a result, analyzing the influence of light spectra on physiological and growth characteristics during cultivation of different plant species is an important problem. In the present work, we investigated the influence of two variants of LED illumination (red light at an increased intensity, the “red” variant, and blue light at an increased intensity, the “blue” variant) on the parameters of photosynthetic dark and light reactions, respiration rate, leaf reflectance indices, and biomass, among other factors in lettuce (Lactuca sativa L.). The same light intensity (about 180 µmol m−2s−1) was used in both variants. It was shown that the blue illumination variant increased the dark respiration rate (35–130%) and cyclic electron flow around photosystem I (18–26% at the maximal intensity of the actinic light) in comparison to the red variant; the effects were dependent on the duration of cultivation. In contrast, the blue variant decreased the rate of the photosynthetic linear electron flow (13–26%) and various plant growth parameters, such as final biomass (about 40%). Some reflectance indices (e.g., the Zarco-Tejada and Miller Index, an index that is related to the core sizes and light-harvesting complex of photosystem I), were also strongly dependent on the illumination variant. Thus, our results show that the red illumination variant contributes a great deal to lettuce growth; in contrast, the blue variant contributes to stress changes, including the activation of cyclic electron flow around photosystem I.

Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors

Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

Developing of Algorithms Monitoring Heartbeat and Respiration Rate of a Seated Person with an FMCW Radar

With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.

Vital Signs Prediction for COVID-19 Patients in ICU

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis

The aim of this work is to present a method for accurately estimating heart and respiration rates under different actual conditions based on a mattress which was integrated with an optical fiber sensor. During the estimation, a ballistocardiogram (BCG) signal, which was obtained from the optical fiber sensor, was used for extracting the heart rate and the respiration rate. However, due to the detrimental effects of the differential detector, self-interference, and variation of installation status of the sensor, the ballistocardiogram (BCG) signal was difficult to detect. In order to resolve the potential concerns of individual differences and body interferences, adaptive regulations and statistical classifications spectrum analysis were used in this paper. Experiments were carried out to quantify heart and respiration rates of healthy volunteers under different breathing and posture conditions. From the experimental results, it could be concluded that (1) the heart rates of 40–150 beats per minute (bpm) and respiration rates of 10–20 breaths per minute (bpm) were measured for individual differences; (2) for the same individuals under four different posture contacts, the mean errors of heart rates were separately 1.60 ± 0.98 bpm, 1.94 ± 0.83 bpm, 1.24 ± 0.59 bpm, and 1.06 ± 0.62 bpm, in contrast, the mean errors of the polar beat device were 1.09 ± 0.96 bpm, 1.44 ± 0.99 bpm, and 1.78 ± 0.94 bpm. Furthermore, the experimental results were validated by conventional counterparts which used skin-contacting electrodes as their measurements. It was reported that the heart rate was 0.26 ± 2.80 bpm in 95% confidence intervals (± 1.96SD) in comparison with Philips sure-signs VM6 medical monitor, and the respiration rate was 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) in comparison with ECG-derived respiratory (EDR) measurements for respiration rates. It was indicated that the developed system using adaptive regulations and statistical classifications spectrum analysis performed better and could easily be used under complex environments.