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| 1 | Classification of the Gut Microbiota of Patients in Intensive Care Units During Development of Sepsis and Septic Shock显示文摘The gut microbiota of intensive care unit(ICU)patients displays extreme dysbiosis associated with increased susceptibility to organ failure,sepsis,and septic shock.However,such dysbiosis is difficult to characterize owing to the high dimensional complexity of the gut microbiota.We tested whether the concept of enterotype can be applied to the gut microbiota of ICU patients to describe the dysbiosis.We collected 131 fecal samples from 64 ICU patients diagnosed with sepsis or septic shock and performed 16S rRNA gene sequencing to dissect their gut microbiota compositions.During the development of sepsis or septic shock and during various medical treatments,the ICU patients always exhibited two dysbiotic microbiota patterns,or ICU-enterotypes,which could not be explained by host properties such as age,sex,and body mass index,or external stressors such as infection site and antibiotic use.ICU-enterotype I(ICU E1)comprised predominantly Bacteroides and an unclassified genus of Enterobacteriaceae,while ICU-enterotype II(ICU E2)comprised predominantly Enterococcus.Among more critically ill patients with Acute Physiology and Chronic Health Evaluation II(APACHE II)scores>18,septic shock was more likely to occur with ICU E1(P=0.041).Additionally,ICU E1 was correlated with high serum lactate levels(P=0.007).Therefore,different patterns of dysbiosis were correlated with different clinical outcomes,suggesting that ICU-enterotypes should be diagnosed as independent clinical indices.Thus,the microbial-based human index classifier we propose is precise and effective for timely monitoring of ICU-enterotypes of individual patients.This work is a first step toward precision medicine for septic patients based on their gut microbiota profiles. | Wanglin Liu Mingyue Cheng Jinman Li Peng Zhang Hang Fan Qinghe Hu Maozhen Han Longxiang Su Huaiwu He Yigang Tong Kang Ning Yun Long | 2020 | Genomics, Proteomics & Bioinformatics2020，18，6： | 7 |
| 2 | DNA Extraction Protocol for Biological Ingredient Analysis of Liuwei Dihuang Wan显示文摘Traditional Chinese medicine(TCM) preparations are widely used for healthcare and clinical practice. So far, the methods commonly used for quality evaluation of TCM preparations mainly focused on chemical ingredients. The biological ingredient analysis of TCM preparations is also important because TCM preparations usually contain both plant and animal ingredients,which often include some mis-identified herbal materials, adulterants or even some biological contaminants.For biological ingredient analysis, the efficiency of DNA extraction is an important factor which might affect the accuracy and reliability of identification. The component complexity in TCM preparations is high, and DNA might be destroyed or degraded in different degrees after a series of processing procedures. Therefore, it is necessary to establish an effective protocol for DNA extraction from TCM preparations. In this study, we chose a classical TCM preparation,Liuwei Dihuang Wan(LDW), as an example to develop a TCM-specific DNA extraction method.An optimized cetyl trimethyl ammonium bromide(CTAB) method(TCM-CTAB) and three commonlyused extraction kits were tested for extraction of DNA from LDW samples. Experimental results indicated that DNA with the highest purity and concentration was obtained by using TCM-CTAB. To further evaluate the different extraction methods, amplification of the second internal transcribed spacer(ITS2) and the chloroplast genome trnL intron was carried out.The results have shown that PCR amplification was successful only with template of DNA extracted by using TCM-CTAB. Moreover, we performed high-throughput 454 sequencing using DNA extracted by TCM-CTAB. Data analysis showed that 3–4 out of 6 prescribed species were detected from LDW samples, while up to 5 contaminating species were detected, | Xinwei Cheng Xiaohua Chen Xiaoquan Su Huanxin Zhao Maozhen Han Cunpei Bo Jian Xu Hong Bai Kang Ning | 2014 | Genomics, Proteomics & Bioinformatics2014，12，3： | 5 |
| 3 | Short-term Forecasting of Individual Residential Load Based on Deep Learning and K-means Clustering显示文摘In order to currently motivate a wide range of various interactions between power network operators and electricity customers,residential load forecasting plays an increasingly important role in demand side response(DSR).Due to high volatility and uncertainty of residential load,it is significantly challenging to forecast it precisely.Thus,this paper presents a short-term individual residential load forecasting method based on a combination of deep learning and k-means clustering,which is capable of effectively extracting the similarity of residential load and performing residential load forecasting accurately at the individual level.It first makes full use of k-means clustering to extract similarity among residential load and then employs deep learning to extract complicated patterns of residential load.The presented method is tested and validated on a real-life Irish residential load dataset,and the experimental results suggest that it can achieve a much higher prediction accuracy,in comparison with a published benchmark method. | Fujia Han Tianjiao Pu Maozhen Li Gareth Taylor | 2021 | CSEE Journal of Power and Energy Systems2021，7，2： | 3 |
| 4 | Agricultural Risk Factors Influence Microbial Ecology in Honghu Lake显示文摘Agricultural activities, including stock-farming, planting industry, and fish aquaculture,can affect the physicochemical and biological characters of freshwater lakes. However, the effects of pollution producing by agricultural activities on microbial ecosystem of lakes remain unclear.Hence, in this work, we selected Honghu Lake as a typical lake that is influenced by agriculture activities. We collected water and sediment samples from 18 sites, which span a wide range of areas from impacted and less-impacted areas. We performed a geospatial analysis on the composition of microbial communities associated with physicochemical properties and antibiotic pollution of samples. The co-occurrence networks of water and sediment were also built and analyzed. Our results showed that the microbial communities of impacted and less-impacted samples of water were largely driven by the concentrations of TN, TP, NO_3^--N, and NO_2^--N, while those of sediment were affected by the concentrations of Sed-OM and Sed-TN. Antibiotics have also played important roles in shaping these microbial communities: the concentrations of oxytetracycline and tetracycline clearly reflected the variance in taxonomic diversity and predicted functional diversity between impacted and less-impacted sites in water and sediment samples, respectively. Furthermore, for samples from both water and sediment, large differences of network topology structures between impacted and less-impacted were also observed. Our results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity; and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu Lake. | Maozhen Han Melissa Dsouza Chunyu Zhou Hongjun Li Junqian Zhang Chaoyun Chen Qi Yao Chaofang Zhong Hao Zhou Jack A Gilbert Zhi Wang Kang Ning | 2019 | Genomics, Proteomics & Bioinformatics2019，17，1： | 3 |
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