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It has been shown that the foliar application of inorganic nano-materials on cereal plants during their growth cycle enhances the rate of plant productivity by providing a micro-nutrient source. We therefore studied the effects of foliarly applied ZnO nanoparticles (ZnO NPs) on Setaria italica L. foxtail millet’s quantitative, nutritional, and physiological parameters. Scanning electron microscopy showed that the ZnO NPs have an average particle size under 20 nm and dominant spherically shaped morphology. Energy dispersive X-ray spectrometry then confirmed ZnO NP homogeneity, and X-ray diffraction verified their high crystalline and wurtzite-structure symmetry. Although plant height, thousand grain weight, and grain yield quantitative parameters did not differ statistically between ZnO NP-treated and untreated plants, the ZnO NP-treated plant grains had significantly higher oil and total nitrogen contents and significantly lower crop water stress index (CWSI). This highlights that the slow-releasing nano-fertilizer improves plant physiological properties and various grain nutritional parameters, and its application is therefore especially beneficial for progressive nanomaterial-based industries.

Antimony (Sb) pollution from industrial activities poses a severe global threat, particularly impacting valuable medicinal crops like linseed, which are highly sensitive to heavy metals. This study reveals the remarkable potential of arbuscular mycorrhizal fungi (AMF) as a sustainable solution to this challenge. Our research demonstrates that while Sb stress significantly impairs linseed growth and photosynthesis, it also triggers oxidative damage. AMF improved photosynthetic performance and water status, and notably enhanced the biosynthesis of crucial phytochemicals like phenolics, flavonoids, and citric acid. These compounds are vital for both plant defence and human health. Furthermore, AMF promoted the accumulation of essential detoxifying agents, leading to a better redox balance and significantly reducing Sb uptake and translocation by 47%. This dual action not only bolsters the plant’s tolerance to Sb but also enhances its medicinal value by boosting health-promoting bioactive metabolites. These promising findings underscore AMF’s dual role: a powerful tool for phytoremediation and a natural enhancer of phytochemical quality. Arbuscular mycorrhizal fungi provide a sustainable, nature-inspired approach to safely cultivate medicinal plants in environments contaminated with heavy metals, underscoring the vital role of plant-microbe interactions in alleviating environmental stresses.

In medical emergency scenarios, conventional single‐parameter monitoring cannot fully assess patient conditions, and current technologies lack intelligent emergency‐state recognition, which delays timely treatment. To overcome these challenges, this study develops a high‐performance flexible thermoelectric textile‐based wearable system that generates a 139.7 mV open‐circuit voltage at ΔT = 30 K. To prevent skin‐thermoelectric contact from distorting sensor readings, a Kirigami spacer is integrated to ensure proper air gaps while maintaining flexibility and breathability. By integrating optical cardiac sensing, the system establishes a thermoelectric‐optical system for wireless real‐time cooperative monitoring of both respiration and cardiac activity. The research systematically investigates the effects of airflow velocity on thermoelectric output, leading to the development of an innovative dynamic airflow‐thermoelectric response theory. This theory enables speech recognition through airflow variations with 98% accuracy. Additionally, the study creates a multi‐source fusion recognition model that combines respiratory patterns, speech‐airflow, and cardiac signals to identify emergency states with 98% accuracy. These advances supplement the theoretical understanding of thermoelectric responses to physiological activities while providing decision support for emergency conditions, demonstrating considerable potential for clinical application. For insufficient medical emergency monitoring, this study develops a wearable system with flexible thermoelectric textiles and thermoelectric‐optical sensing. It establishes a dynamic airflow‐thermoelectric response theory (enabling 98% speech recognition) and a multi‐source fusion model for 98% emergency state recognition, aiding clinical decision‐making.

Using artificial intelligence to assist in diagnosing diseases has become a contemporary research hotspot. Conventional automatic diagnostic method uses a conventional machine learning algorithm to distinguish features from which a professional doctor manually extracts features in diagnostic reports. But it can be difficult to collect large amounts of necessary medical data. Therefore, these methods face challenges with efficiency and accuracy. Here, we proposed an automatic diagnostic system based on a deep learning algorithm to diagnose hyperlipidemia by using human physiological parameters. This model is a neural network which uses technologies of data extension and data correction. Firstly, we corrected and supplemented the original data by the method mentioned previously to solve the problem of lacking data. Secondly, the processed data were used to train a deep learning model. Deep learning model can automatically extract all the available information instead of artificially reducing the raw data. Therefore, it can reduce labor costs. The classifiers classify the data by using features previously mentioned. Finally, the system was evaluated with data from a test dataset. It achieved 91.49% accuracy, 87.50% sensitivity, 93.33% specificity, and 87.50% precision with data from the test dataset. The proposed diagnostic method has a highly robust and accurate performance, and can be used for tentative diagnosis. It can automatically diagnose diseases by using human physiological parameters, thereby reducing labor cost, which results in effective improvement of clinical diagnostic efficiency.

This study was conducted to investigate the ameliorative effect of lactic acid bacteria Lactobacillus casei and Lactobacillus acidophilus against Potassium bromate (25, 50) mg / kg toxicity  by some physiological indicators in 35 of female rats after 21 days. The animals were divided into 7 groups within each group 5 animals weighted 140 – 155 g. The results showed a significant decrease (P<0.05) in value of Red blood cells (RBC), hemoglobin (Hb), White blood cells (WBC), Lymphocyte (LYM) and Platelets (PLT), While increasing the values of Granules (GRN). Also found that the addition of Potassium bromate Potassium bromate led to increase in cholesterol, triglyceride (TG), Low Density Lipoprotein (LDL) and blood glucose, while decreased the values of High Density Lipoprotein (HDL) for rats groups with  increasing the concentration of Potassium bromate compared with control group. The addition of two types of lactic acid bacteria L. casei and L. acidophilus  with Potassium bromate showed a positive effect to reducing the negative effect of  Potassium bromate on blood and lipid profile parameters compared with the control group and Potassium bromate group. It is concluded that the lactic acid bacteria has protective effects and reduces the effects that Potassium bromate.

Castor bean (Ricinus communis L.), a promising bioenergy crop, is readily planted in marginal lands like saline soils. A controlled experiment was conducted to explore the possibility of using gibberellic acid (GA3) as a promoter for caster bean grown under NaCl conditions and to try to determine the most appropriate concentration of GA3 for seedling growth. The seeds of salt-tolerant cultivar Zibi 5 were firstly soaked with 0, 200, 250, and 300 µM GA3 for 12 h and then cultured with 1/2 Hoagland solution containing 0, 50, and 100 mM NaCl in pots filled with sand. Plant height, stem diameter, leaf area, dry mater of each organ, activity of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), soluble protein, and proline content in the leaves were examined. Plant height and stem diameter, SOD, and POD activity was significantly highest in the treatment of 250 µM GA3 under salt concentration of 50 mM NaCl among all the testing days; protein content was highest when GA3 concentration was 250 µM under 100 mM NaCl treatment. This indicated that caster bean seed soaking with 250 µM GA3 could be the most suitable concentration for promoting seedling growth of caster bean, improving their stress resistance.

BackgroundHeart rate (HR) monitors are valuable devices for fitness-orientated individuals. There has been a vast influx of optical sensing blood flow monitors claiming to provide accurate HR during physical activities. These monitors are worn on the arm and wrist to detect HR with photoplethysmography (PPG) techniques. Little is known about the validity of these wearable activity trackers.AimValidate the Scosche Rhythm (SR), Mio Alpha (MA), Fitbit Charge HR (FH), Basis Peak (BP), Microsoft Band (MB), and TomTom Runner Cardio (TT) wireless HR monitors.Methods50 volunteers (males: n=32, age 19–43 years; females: n=18, age 19–38 years) participated. All monitors were worn simultaneously in a randomised configuration. The Polar RS400 HR chest strap was the criterion measure. A treadmill protocol of one 30 min bout of continuous walking and running at 3.2, 4.8, 6.4, 8.0, and 9.6 km/h (5 min at each protocol speed) with HR manually recorded every minute was completed.ResultsFor group comparisons, the mean absolute percentage error values were: 3.3%, 3.6%, 4.0%, 4.6%, 4.8% and 6.2% for TT, BP, RH, MA, MB and FH, respectively. Pearson product-moment correlation coefficient (r) was observed: r=0.959 (TT), r=0.956 (MB), r=0.954 (BP), r=0.933 (FH), r=0.930 (RH) and r=0.929 (MA). Results from 95% equivalency testing showed monitors were found to be equivalent to those of the criterion HR (±10% equivalence zone: 98.15–119.96).ConclusionsThe results demonstrate that the wearable activity trackers provide an accurate measurement of HR during walking and running activities.

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

As an indispensable environment element for crop growth, air temperature has brought challenge for the sustainable development of rice ( Oryza sativa L.) production. Elevated air temperature led to great loss in rice grain yield in many districts suffering from heat stress due to the greenhouse effect worldwide, which has received more and more attention from researchers. A field experiment was conducted to investigate impacts of high air temperature (HAT) after rice heading stage on dynamics of SPAD values, soluble sugar, soluble protein, and malondialdehyde (MDA) contents of flag leaves, and grain yield attributes. The results showed that HAT significantly reduced SPAD values, soluble sugar and protein contents, seed-setting rate, number of filled grains per panicles, 1000-grain weight, and grain yield, while increased MDA content. There exists strong correlation between each physiological parameter and days from heading stage to maturity, which can be simulated by quadratic curve equation or linear regression equation. Under HAT, the enhanced MDA content and decreased soluble sugar content demonstrated the damage of membrane structure and photosynthesis function of rice flag leaves, which was partially attributed to the reduced SPAD value and soluble protein content. In the present experiment, rice seed-setting rate was more vulnerable to HAT than grain weight. The disturbance of physiological metabolism in flag leaves was a fundamental reason for the reduction of rice grain yield under HAT.