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Curcumin, a naturally occurring poorly water-soluble polyphenol with a broad spectrum, is a typical BCS IV drug. The objective of this study was to develop curcumin nanocrystals liposomes with the aim of improving bioavailability. In this study, we prepared cationic curcumin nanocrystals with a particle size of only 29.42 nm; such a phenomenal range of particle sizes is very rare. Moreover, we summarized and evaluated the parameters of the nanocrystal preparation process, including methods, formulations, etc., and the rules we concluded can be generalized to other nanocrystal preparation processes. To counteract the instability of the nanocrystals in the digestive tract, cationic curcumin nanocrystals were loaded into negatively charged liposomes through gravitational force between different charges. Unexpectedly, chitosan oligosaccharide was found to promote the self-assembly process of curcumin nanocrystal liposomes. In vitro and in vivo experiments demonstrated that chitosan-modified curcumin nanocrystal liposomes exhibited enhanced resistance to enzyme barriers, mucus barriers, and cellular barriers, resulting in a 5.4-fold increase in bioavailability compared to crude powder formulations. It can be concluded that cationic nanocrystals liposomes represent an appropriate novel strategy for improving the dissolution rate and bioavailability of poorly soluble natural products such as curcumin. [ABSTRACT FROM AUTHOR]

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α -IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches. [ABSTRACT FROM AUTHOR]

Anthropogenically induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) can contribute to similar patterns, while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5 °C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes is more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia, which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained by the fact that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China's agricultural and animal husbandry production and threaten food security. [ABSTRACT FROM AUTHOR]

Titanium and its alloys have been widely used in the field of oral implants over the past few decades. However, the effect of micro-arc oxidation modified titanium surface on Schwann cells has not been studied, which is of great significance for nerve regeneration around implants and improvement of osseoperception. In this study, the characterization of the titanium surface modified by micro-arc oxidation (MAO) was detected by scanning electron microscope (SEM), XPS and a contact angle measurement system. Schwann cells (SCs) were cultured on titanium surfaces of micro-arc oxidation (MAO) and pure titanium (Ti). At different time points, the morphology and adhesion of SCs on the titanium surfaces were observed by SEM. Cell proliferation activity was detected by the CCK-8 method. The expression levels of mRNA and proteins of nerve growth factor (NGF) and glial-derived neurotrophic factor (GDNF) were detected by RT-PCR, immunofluorescence and western blot. The results of this in vitro study revealed that micro-arc-oxidation-modified titanium surfaces promoted Schwann cell proliferation and secretion of neurotrophic factors compared with pure titanium. CCK-8 results showed that the number of Schwann cells on MAO surfaces was significantly higher than that of the Ti group on day 7. The mRNA expressions of Ngf and Gdnf were up-regulated in both groups from day 1 to day 7. On day 3 and day 7, the gene expression of Ngf in the MAO group was significantly higher than that of the Ti group. On day 7, the MAO group appeared significantly up-regulated in gene expression level of Gdnf. The results of western blot were consistent. Micro-arc oxidation modification provides an accurate and effective method for promoting nerve regeneration of titanium microtopography coatings, which have potential significance for promoting patients' osseoperception ability in clinical practice. [ABSTRACT FROM AUTHOR]

Enhancing energy efficiency is globally regarded as an effective way to reduce carbon emissions. In recent years, the energy efficiency of China has gradually improved; however, energy consumption and CO2 emissions are still increasing. To better understand the reasons for this, we evaluated the energy rebound effect (RE) of 30 provinces in China over the period 2001–2017 by employing stochastic frontier analysis (SFA) and the system generalized method of moments (system-GMM) approach, and explored the extent to which the RE affects CO2 emissions. Asymmetric and regional heterogeneity analyses were also conducted. The results indicate that the national average RE was 90.47% in the short run, and 78.17% in the long run, during the sample period. Most of the provinces experienced a partial RE, with a backfire effect occurring in some provinces such as Guangxi and Henan. The RE was associated with significant increases in CO2 emissions; specifically, a 1% increase in the short-run RE led to an increase in CO2 emissions of approximately 0.818%, and a 1% increase in the long-run RE resulted in an increase in CO2 emissions of approximately 0.695%. Moreover, significant regional differences existed in the impact of the RE on CO2 emissions; in regions with high emissions and a high RE, the CO2 reduction effect from the marginal decline in the RE was much more pronounced than that in other regions. [ABSTRACT FROM AUTHOR]

Homogeneous mixture models are widely used to predict the hydrodynamic cavitation. In this study, the constant-transfer coefficient model is implemented into a homogeneous cavitation model to predict the heat and mass diffusion. Modifications are made to the average bubble temperature and the Peclet number for thermal diffusivity in the constant-transfer coefficient model. The evolutions of a spherical bubble triggered by negative pressure pulse are simulated to evaluate the prediction of heat and mass diffusion by the homogeneous model. The evolutions of three bubbles inside a rectangular tube are simulated, which show good accuracy of the homogeneous model for multibubbles in stationary liquid. [ABSTRACT FROM AUTHOR]