Ethnobotanical study on medicinal plants used by Maonan people in China

Abstract Background This paper is based on an ethnobotanical investigation that focused on the traditional medicinal plants used by local Maonan people to treat human diseases in Maonan concentration regions. The Maonan people have relied on traditional medicine since ancient times, especially medicinal plants. The aim of this study is to document medicinal plants used by the Maonans and to report the status of medicinal plants and associated traditional knowledge. Methods Ethnobotanical data were collected from June 2012 to September 2014 in Huanjiang Maonan Autonomous County, northern Guangxi, southwest China. In total, 118 knowledgeable informants were interviewed. Following statistically sampling method, eighteen villages from 5 townships were selected to conduct field investigations. Information was collected through the approache of participatory observation, semi-structured interviews, ranking exercises, key informant interviews, focus group discussions, and participatory rural appraisals. Results A total of 368 medicinal plant species were investigated and documented together with their medicinal uses by the Maonans, most of which were obtained from the wild ecosystems. The plants were used to treat 95 human diseases. Grinding was a widely used method to prepare traditional herbal medicines. There were significant relationships between gender and age, and between gender and informants’ knowledge of medicinal plant use. Deforestation for agricultural purposes was identified as the most destructive factor of medicinal plants, followed by drought and over-harvest. Conclusions The species diversity of medicinal plants used by the Maonans in the study area was very rich. Medicinal plants played a significant role in healing various human disorders in the Maonan communities. However, the conflicts between traditional inheriting system and recent socio-economic changes (and other factors) resulted in the reduction or loss of both medicinal plants and associated indigenous knowledge. Thus, conservation efforts and policies, and innovation of inheriting system are necessary for protecting the medicinal plants and associated indigenous knowledge. Awareness is also needed to be raised among local Maonans focusing on sustainable utilization and management of both medicinal plants and traditional knowledge

Development of a Risk Based Inherent Safety Index Using an Integrated Approach

Copyright © 2018 by Mary Kay O’Connor Process Safety Center Prepared for Presentation at American Institute of Chemical Engineers 2018 Spring Meeting and 14th Global Congress on Process Safety Orlando, Florida April 22 – 25, 2018The growing demand for petrochemical products and the implementation of new process technologies have made the petrochemical plants more complex; therefore, it becomes more challenging to manage the risk. Traditionally, additional layers of protection were added to prevent incidents, which further adds complexity to the existing process. Inherently safer design aims at managing the risk from the design stage of petrochemical plants, which eliminates the hazard in the process rather than control the risk during operation. When designing a new plant or modifying an existing plant, a safety index system will be helpful to assess the risk level of various options effectively. This can be achieved by considering the inherently safer design principles, i.e., elimination, substitution, moderation, and simplification. In this work, a novel safety index system was developed to cover the life cycle of a process design, which includes the research stage, process development stage, and engineering design stage. This safety index will be used to evaluate the risk level of petrochemical facilities by comparing toxic, flammable, explosive, runaway reaction, dust and physical explosion risks and identify the areas where inherently safer design principles can be used to improve the process. A case study on ethanol synthesis process will be presented for the validation of the index system developed.Mary Kay O'Connor Process Safety Center; Texas A&M Engineering Experiment Station(TEES

Experiments on Turbulence Intensity and Bubble Frequency in Self-Aerated Open Channel Flows

Although spillways have been investigated experimentally by various researchers, only a few studies have been conducted on the comprehensive properties of a self-aerated air-water flow. In this study, new experimental data were recorded and discussed for the distribution of the air concentration, air-water velocity, turbulence intensity and bubble frequency in the completely developed regions for spillways. It was observed that both the turbulence intensity and bubble frequency increased from the bottom and subsequently decreased near the free surface. The positions of maximum air bubble frequency and turbulence intensity gradually approached air concentration to 0.50 in the self-aerated developed region. Self-similar relationships between the turbulence intensity and bubble frequency were proposed

Responses of Manila Grass (Zoysia matrella) to chilling stress: From transcriptomics to physiology.

Manila grass (Zoysia matrella), a warm-season turfgrass, usually wilts and browns by late autumn because of low temperature. To elucidate the molecular mechanisms regarding Manila grass responses to cold stress, we performed transcriptome sequencing of leaves exposed to 4°C for 0 (CK), 2h (2h_CT) and 72h (72h_CT) by Illumina technology. Approximately 250 million paired-end reads were obtained and de novo assembled into 82,605 unigenes. A total of 34,879 unigenes were annotated by comparing their sequence to public protein databases. At the 2h- and 72h-cold time points, 324 and 5,851 differentially expressed genes (DEGs) were identified, respectively. Gene ontology (GO) and metabolism pathway (KEGG) enrichment analyses of DEGs indicated that auxin, gibberellins, ethylene and calcium took part in the cold signal transduction in the early period. And in the late cold period, electron transport activities, photosynthetic machinery and activity, carbohydrate and nitrogen metabolism, redox equilibrium and hormone metabolism were disturbed. Low temperature stress triggered high light, drought and oxidative stress. At the physiological level, cold stress induced a decrease in water content, an increase in levels of total soluble sugar, free proline and MDA, and changes in bioactive gibberellins levels, which supported the changes in gene expression. The results provided a large set of sequence data of Manila grass as well as molecular mechanisms of the grass in response to cold stress. This information will be helpful for future study of molecular breeding and turf management

Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm

Field spectroscopy and other efficient hyperspectral techniques have been widely used to measure soil properties, including soil organic carbon (SOC) content. However, reflectance measurements based on field spectroscopy are quite sensitive to uncontrolled variations in surface soil conditions, such as moisture content; hence, such variations lead to drastically reduced prediction accuracy. The goals of this work are to (i) explore the moisture effect on soil spectra with different SOC levels, (ii) evaluate the selection of optimal parameter for external parameter othogonalization (EPO) in reducing moisture effect, and (iii) improve SOC prediction accuracy for semi-arid soils with various moisture levels by combing the EPO with machine learning method. Soil samples were collected from grassland regions of Inner Mongolia in North China. Rewetting laboratory experiments were conducted to make samples moisturized at five levels. Visible and near-infrared spectra (350–2500 nm) of soil samples rewetted were observed using a hand-held SVC HR-1024 spectroradiometer. Our results show that moisture influences the correlation between SOC content and soil reflectance spectra and that moisture has a greater impact on the spectra of samples with low SOC. An EPO algorithm can quantitatively extract information of the affected spectra from the spectra of moist soil samples by an optimal singular value. A SOC model that effectively couples EPO with random forest (RF) outperforms partial least-square regression (PLSR)-based models. The EPO–RF model generates better results with R2 of 0.86 and root-mean squared error (RMSE) of 3.82 g kg−1, whereas a PLSR model gives R2 of 0.79 and RMSE of 4.68 g kg−1.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Enhanced Tribological Properties of Polymer Composite Coating Containing Graphene at Room and Elevated Temperatures

To improve the tribology properties of the polymer coating under elevated temperature, the epoxy coating was reinforced with nano graphene. The micro-hardness, heat conductivity, and thermo-gravimetric properties of the coating were enhanced as filled graphene. The friction and wear properties of the polymer coating were studied using a pin-on-disc tribo-meter under room and elevated temperatures. The results showed that under room temperature, the friction coefficient and the wear rate of the coating adding 4.0 wt % graphene was 80% and 76% lower than that of the neat epoxy coating, respectively. As the test temperature increased, the friction coefficient of the graphene/polymer coatings decreased at first and then slightly increased. The friction coefficient was at its lowest value under 150 °C and then increased as the temperature rose to 200 °C. By adding 4.0 wt % graphene, the friction coefficient and wear rate of the polymer coating were further reduced, especially at elevated temperatures

Bulk Synthesis and Characterization of Ti3Al Nanoparticles by Flow-Levitation Method

A novel bulk synthesis method for preparing high pure Ti3Al nanoparticles was developed by flow-levitation method (FL). The Ti and Al vapours ascending from the high temperature levitated droplet were condensed by cryogenic Ar gas under atmospheric pressure. The morphology, crystalline structure, and chemical composition of Ti3Al nanoparticles were, respectively, investigated by transmission electron microscopy, X-ray diffraction, and inductively coupled plasma atomic emission spectrometry. The results indicated that the Ti3Al powders are nearly spherical-shaped, and the particle size ranges from several nanometers to 100 nm in diameter. Measurements of the d-spacing from X-ray (XRD) and electron diffraction studies confirmed that the Ti3Al nanoparticles have a hexagonal structure. A thin oxidation coating of 2-3 nm in thickness was formed around the particles after exposure to air. Based on the XPS measurements, the surface coating of the Ti3Al nanoparticles is a mixture of Al2O3 and TiO2. The production rate of Ti3Al nanoparticles was estimated to be about 3 g/h. This method has a great potential in mass production of Ti3Al nanoparticles

Blood Glucose Prediction Method Based on Particle Swarm Optimization and Model Fusion

Blood glucose stability in diabetic patients determines the degree of health, and changes in blood glucose levels are related to the outcome of diabetic patients. Therefore, accurate monitoring of blood glucose has a crucial role in controlling diabetes. Aiming at the problem of high volatility of blood glucose concentration in diabetic patients and the limitations of a single regression prediction model, this paper proposes a method for predicting blood glucose values based on particle swarm optimization and model fusion. First, the Kalman filtering algorithm is used to smooth and reduce the noise of the sensor current signal to reduce the effect of noise on the data. Then, the hyperparameter optimization of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) models is performed using particle swarm optimization algorithm. Finally, the XGBoost and LightGBM models are used as the base learner and the Bayesian regression model as the meta-learner, and the stacking model fusion method is used to achieve the prediction of blood glucose values. In order to prove the effectiveness and superiority of the method in this paper, we compared the prediction results of stacking fusion model with other 6 models. The experimental results show that the stacking fusion model proposed in this paper can accurately predict blood glucose values, and the average absolute percentage error of blood glucose prediction is 13.01%, and the prediction error of the stacking fusion model is much lower than that of the other six models. Therefore, the proposed diabetes blood glucose prediction method in this paper has superiority