Metagenomic Analysis of the Fecal Microbiomes of Wild Asian Elephants Reveals Microflora and Enzymes that Mainly Digest Hemicellulose

( Chengbo Zhang ),( Bo Xu ),( Zunxi Huang ),( Tao Lu ) 한국미생물생명공학회(구 한국산업미생물학회) 2019 Journal of microbiology and biotechnology Vol.29 No.8

To investigate the diversity of gastrointestinal microflora and lignocellulose-degrading enzymes in wild Asian elephants, three of these animals living in the same group were selected for study from the Wild Elephant Valley in the Xishuangbanna Nature Reserve of Yunnan Province, China. Fresh fecal samples from the three wild Asian elephants were analyzed by metagenomic sequencing to study the diversity of their gastrointestinal microbes and cellulolytic enzymes. There were a high abundance of Firmicutes and a higher abundance of hemicellulose-degrading hydrolases than cellulose-degrading hydrolases in the wild Asian elephants. Furthermore, there were a high abundance and a rich diversity of carbohydrate active enzymes (CAZymes) obtained from the gene set annotation of the three samples, with the majority of them showing low identity with the CAZy database entry. About half of the CAZymes had no species source at the phylum or genus level. These indicated that the wild Asian elephants might possess greater ability to digest hemicellulose than cellulose to provide energy, and moreover, the gastrointestinal tracts of these pachyderms might be a potential source of novel efficient lignocellulose-degrading enzymes. Therefore, the exploitation and utilization of these enzyme resources could help us to alleviate the current energy crisis and ensure food security.

Effects of red/blue versus white LED light of different intensities on the growth and organic carbon and autotoxin secretion of hydroponic lettuce

Chengbo Zhou,Qi Wang,Wenke Liu,Baoshi Li,Mingjie Shao,Yubin Zhang 한국원예학회 2022 Horticulture, Environment, and Biotechnology Vol.63 No.2

Light is a crucial signal for plant growth, development, and secondary metabolism. Exploring the effects of light on autotoxin secretion in lettuce can be helpful for improving the utilization efficiency of the nutrient solution in plant factories. The effects of white light (WL) and the combination of red (R) and blue (B) light (RB, 4R:1B) at different intensities (150, 200, and 250 μmol m −2 s −1) on the growth and root exudates of hydroponic lettuce (Lactuca sativa L.) were studied in a closed plant factory. The lettuce biomass and photosynthetic rate increased with the increasing light intensity, and the photosynthetic rate was significantly lower under WL than under RB at both 200 and 250 μmol m −2 s −1 . Lettuce under WL had the longest root length and highest root surface area at 200 μmol m −2 s −1 , while the root length, root surface area, and root volume under RB were the highest at 250 μmol m −2 s −1 . Total organic carbon (TOC) content of root exudates in the nutrient solution based on shoot or root dry weight decreased with the increasing light intensity. With the increase in light intensity, the secretion of four autotoxins (benzoic acid, ferulic acid, gallic acid, and tannic acid) based on shoot dry weight and root dry weight decreased under WL. Compared with RB, WL significantly reduced the secretion of autotoxins at 250 μmol m −2 s −1 . In conclusion, 250 μmol m −2 s −1 white light should be used for high lettuce yield, and it could also decrease the autotoxins in the nutrient solution and the occurrence of autotoxicity.

Precipitation Behavior in Al-Zn-Mg-Cu Alloy After Direct Quenching

Shengdan Liu,Chengbo Li,Yunlai Deng,Xinming Zhang,Qiming Zhong 대한금속·재료학회 2014 METALS AND MATERIALS International Vol.20 No.2

The precipitation behavior in an Al-6.8Zn-1.9Mg-1.0Cu-0.12Zr alloy after direct quenching from solutionheat treatment temperature of 470 °C to 205-355 °C was investigated by means of hardness tests, electricalconductivity tests, and transmission electron microscopy. At temperatures below 265 °C, the hardnessincreased gradually to a peak value and then decreased rapidly with time. At 265 °C, the hardness wasalmost unchanged within the initial 2000 s and then decreased gradually. At higher temperatures, the hardnessdecreased slowly with time. The electrical conductivity started to increase after a certain period oftime and then tended to maintain a constant value at all temperatures. Microstructure examination indicatedheterogeneous precipitation of the η phase at grain boundaries and inside grains during holding at205 °C and 325 °C. Based on the electrical conductivity data, the precipitation kinetics could be describedquite well by the Johnson-Mehl-Avrami-Komolgorov relationship with a n value varying between 0.78 and1.33. The activation energy was estimated to be about 44.9 kJ/mol, which is close to that expected for a dislocationdiffusion mechanism. Time-temperature-transformation diagrams were constructed and the nosetemperature ranged from 295 °C to 325 °C.

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

( Zilong Jin ),( Chengbo Zhang ),( Guanzhe Zhao ),( Yuanfeng Jin ),( Lejun Zhang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.2

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

Effects of constant versus fl uctuating red–blue LED radiation on yield and quality of hydroponic purple-leaf lettuce

Mingjie Shao,Wenke Liu,Lingyan Zha,Chengbo Zhou,Yubin Zhang,Baoshi Li 한국원예학회 2020 Horticulture, Environment, and Biotechnology Vol.61 No.6

This study investigated the eff ects of constant light and alternating relatively high-intensity (500 μmol m −2 s −1 ) and lowintensity(150 μmol m −2 s −1 ) red–blue LEDs (4R:1B) on the biomass production and quality of hydroponic purple-leaf lettuce( Lactuca sativa L. cv. ‘Zishan’) in an environmentally controlled plant factory. Four treatments were set up to separate1 h of high light into four diff erent alternating frequencies in a 24-h light−dark cycle (16/8 h): one time (A1), three times(A3), six times (A6), and twelve times (A12). In addition, one constant light treatment with the same daily light integral(DLI, 9.8 mol m −2 per day) as other treatments was set as the control (CK, 170 μmol m −2 s −1 ). The results indicated thatA6 signifi cantly reduced shoot fresh weight and increased the root–shoot ratio of lettuce compared with CK, but there wasno signifi cant diff erence among other treatments. Alternating light treatments did not promote the accumulation of solublesugar, soluble protein, and phenolic substances compared with CK. Meanwhile, A12 signifi cantly promoted the accumulationof total ascorbate (TA) in lettuce leaves compared with other treatments but decreased ascorbate/TA ratio. Above all, underthe same DLI condition, alternating high and low light did not have obvious positive eff ects on biomass production and theaccumulation of nutrient substance in lettuce under constant light was better than that under alternating light. Therefore,compared with the fl uctuating radiation with the same DLI, constant radiation is a better choice for lettuce production.

Domain Transfer Learning for MCI Conversion Prediction

Cheng, Bo,Liu, Mingxia,Zhang, Daoqiang,Munsell, Brent C.,Shen, Dinggang IEEE 2015 IEEE Transactions on Biomedical Engineering Vol.62 No.7

<P>Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.</P>