INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
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2025
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IF 8.5
Fang-Fang Liu,Hai-Yan Ren,Feng Liu,Wen-Xuan Qi,Chao-Peng Liang,Bang-Xian Zhang,Xiang-Jun Rao
Insects mount robust immune responses to reduce the efficacy of biopesticides. Identification of critical immune targets for biocontrol strategies is thus essential. However, immune targets in the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), a major pest of corn, remain largely unexplored. Here, we investigated the immune response of S. frugiperda to the entomopathogen Beauveria bassiana (Hypocreales: Cordycipitaceae). Time-series RNA sequencing revealed 863, 717, and 165 differentially expressed genes (DEGs) at 24, 48, and 60 h post-infection, respectively. GO and KEGG enrichment analyses revealed that DEGs were significantly enriched in immune-related terms and pathways. Among the DEGs, the gene encoding immulectin SfIML-1 , a C-type lectin with dual carbohydrate recognition domains, was significantly upregulated during the early and intermediate stages of infection. Genome-wide analysis identified nine IML genes, with SfIML-1 as the only one highly responsive to B. bassiana . Recombinant SfIML-1 binds pathogen-associated molecular patterns and pathogens, enhancing cellular responses. RNAi of SfIML-1 decreased survival after fungal infection, while injection of recombinant SfIML-1 increased survival. Suppression of the NF-κB and JAK-STAT pathways diminished SfIML-1 expression in both fat body and Sf9 cells. These results identify SfIML-1 as a key pattern recognition receptor and a promising target for developing novel biocontrol strategies.
研究领域:
昆虫学
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分子生物学
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生物防治
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产品关联
DOI
PubMed
高影响因子
PLoS One
·
2015
·Vol. 10
·期 3
·e0116832
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IF 3.234
Yan-Ling Zheng,Li-Ping Zhang,Xue-Liang Zhang,Kai Wang,Yu-Jian Zheng
Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1) 12 model and the combined ARIMA (1, 1, 2) (1, 1, 1) 12 -ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1) 12 -ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.
研究领域:
流行病学
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传染病学
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生物统计学
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公共卫生
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预防医学
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DOI
高影响因子
Acta Cardiologica Sinica
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2018
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IF 0.927
Li-Tan Yang,Yao-Mao Chang,Tsung-Han Hsieh,Wen-Hsuan Hou,Chung-Yi Li
Background We conducted a time-series analysis of daily ambient temperature and all-cause, cardiovascular, and respiratory disease mortality in Taiwan, which is generally neither extremely hot nor cold. Methods Data on all-cause daily mortality rates (excluding accidents, suicide, and homicide), and mortality rates due to respiratory and cardiovascular diseases between 2008 and 2010 were obtained from the Taiwan Death Registry. The daily temperature for that period was averaged from 33 monitoring stations nationwide. A generalized least square model was constructed to assess the relationship between the time-series trends of temperature and mortality, and the cross-correlation function was used to determine the possible time lag for the effect of temperature on mortality. Results As the average temperature increased, the daily all-cause (β = -0.006) and respiratory disease (β = -0.012) mortality rates decreased. On the other hand, an inverse relationship (β = -0.028) between average daily temperature and cardiovascular disease mortality was observed only for a temperature between 12.91 °C and 26.36 °C. The time lag for all-cause and cardiovascular disease mortality was similar at 4-6 days, while the lag for respiratory disease was longer at 13-16 days. Conclusions We found inverse associations between average temperature and all-cause and respiratory mortality. An inverse association between temperature and cardiovascular disease mortality was observed only from 12.91 °C to 26.36 °C.
研究领域:
流行病学
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环境医学
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公共卫生
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心血管病学
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呼吸病学
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DOI
PubMed
高影响因子
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
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2022
·
IF 5.895
Hui-Ying Wang,Cheng Qu,Meng-Ning Li,Chao-Ran Li,Run-Zhou Liu,Zifan Guo,Ping Li,Wen Gao,Hua Yang
Efficient characterization of xenobiotic metabolites and their dynamics in a changing complex matrix remains difficult. Herein, we proposed a time-series-dependent global data filtering strategy for the rapid and comprehensive characterization of xenobiotic metabolites and their dynamic variation based on metabolome data. A set of data preprocessing methods was used to screen potential xenobiotic metabolites, considering the differences between the treated and control groups and the fluctuations over time. To further identify metabolites of the target, an in-house accurate mass database was constructed by potential metabolic pathways and applied. Taking the extract of Ginkgo biloba (EGB) co-incubated with gut microbiota as an example, 107 compounds were identified as flavonoid-derived metabolites (including 67 original from EGB and 40 new) from 7468 ions. Their temporal metabolic profiles and regularities were also investigated. This study provided a systematic and feasible method to elucidate and profile xenobiotic metabolism.
研究领域:
代谢组学
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药理学
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计算生物学
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产品关联
DOI
PubMed
高影响因子
SCIENCE OF THE TOTAL ENVIRONMENT
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2022
·
IF 10.753
Kai Cai,Yongpeng Zhao,Zongjing Kang,Shuling Wang,Alan L. Wright,Xianjun Jiang
Pseudotargeted metabolomics is achieved by introducing an algorithm designed to choose ions for selected ion monitoring from identified metabolites. This method integrates the advantages of both untargeted and targeted metabolomics. In this study, environmental pseudotargeted metabolomics was established to analyze the soil metabolites, based on microwave assisted derivatization followed by gas chromatography–mass spectrometry analysis. The method development included the optimization of extraction factors and derivatization conditions, evaluation of silylation reagent types and matrix-dependent behaviors. Under the optimal conditions, the microwave oximation and silylation were completed in 5 min and 9 min. A total of 184 metabolites from 26 chemical classifications were identified in soil matrices. The method validation demonstrated excellent performance in terms of linearity (correlation coefficient > 0.99), repeatability (relative standard deviation ( RSD ) < 20 %), reproducibility ( RSD < 25 %), stability (relative difference < 10 % within 18 h), and sensitivity (16–110 times higher signal-to-noise ratio). This developed method was applied to characterize the metabolite compositions and metabolic profiling in a 1000-year paddy soil chronosequence . The relative abundance of trehalose was highest in 6-(40.3 %), 60-(55.8 %), 300-(67.7 %)and 1000-(61.7 %)years paddy soil, respectively, but long-chain fatty acids were most abundant in marine sediment (57.4 %). Forty-two characteristic metabolites were considered as primarily responsible for discriminating and characterizing the paddy soil chronosequences development and seven major metabolic pathways were altered. In addition, GC–MS metabolite profile presented better discriminating power in paddy soil ecosystem changes than phospholipid fatty acids (PLFAs). Overall, environmental pseudotargeted metabolomics can provide a high throughout and wide coverage approach for performing metabolic profiling in the soil research.
研究领域:
环境科学
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代谢组学
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产品关联
DOI
PubMed
高影响因子
BMC INFECTIOUS DISEASES
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2022
·Vol. 22
·期 1
·1-12
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IF 3.667
An Qingyu,Wu Jun,Meng Jun,Zhao Zhijie,Bai Jin Jian,Li Xiaofeng
Background Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS’s development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model’s performance. Methods The monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models. Meanwhile, using yearly HIV data to established ARIMA and using monthly HIV data to established BPNN, and SARIMA (seasonal autoregressive integrated moving average) model to compare the predictive ability with EMD-BPNN model. Results From 2004 to 2017, 3310 cases of HIV were reported in Dalian, including 101 fatal cases. The monthly HIV data series are decomposed into four relatively stable IMFs and one residue item by EMD, and the residue item showed that the incidence of HIV increases firstly after declining. The mean absolute percentage error value for the EMD-BPNN, BPNN, SARIMA (1,1,2) (0,1,1) 12 in 2018 is 7.80%, 10.79%, 9.48% respectively, and the mean absolute percentage error value for the ARIMA (3,1,0) model in 2017 and 2018 is 8.91%. Conclusions The EMD-BPNN model was effective and reliable in predicting the incidence of HIV for annual incidence, and the results could furnish a scientific reference for policy makers and health agencies in Dalian.
研究领域:
流行病学
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生物统计学
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传染病学
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公共卫生
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计算生物学
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医学预测建模
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DOI
高影响因子
Landslides
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2019
·Vol. 16
·期 7
·1313-1324
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IF 4.252
Yang Wentao,Wang Yunqi,Sun Shao,Wang Yujie,Ma Chao
Detecting slope movement before landslides occur in mountain regions is crucial for disaster reduction. In October 2018, a gigantic landslide occurred on the Jinsha River, causing dammed-lake breach flood 500 km downstream. In this work, we used 25 Sentinel-2 images from November 2015 to August 2018 to explore the capability of this high temporal resolution optical images in detecting slope movement before the Jinsha River landslide. Normalized difference vegetation index (NDVI) was calculated to composite temporal profiles using all Sentinel-2 images. With this NDVI time series, unsupervised K-means classifier was applied to initially classify the study area and find the best thresholds for automatically extracting landslide scars in the image series. These extracted landslide scars were validated using interpreted results from two high spatial resolution images of similar dates in 2015 (user’s accuracy 89.7%, producer’s accuracy 83.6%) and 2018 (user’s accuracy 90.8%, producer’s accuracy 74.9%). After validation, extracted landslide scars of different years were counted and displayed in an RGB composite image to highlight slope movement. In addition, monotonous decrease/increase of NDVI was also observed, indicating continuous expansion of landslide scarps and movement of landslide head along the slope on the landslide surface. This work demonstrated the capability of Sentinel-2 time series images to capture slope movement with short revisit time at low cost. By incorporating other environmental information (such as elevation), this proposed method has the potential to consistently map pre-landslide slope movements over a large region.
研究领域:
遥感地质学
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环境灾害监测
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地理信息系统
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山地灾害防治
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地球观测
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DOI
高影响因子
GEODERMA
·
2021
·
IF 6.114
Kai Cai,Yongpeng Zhao,Zongjing Kang,Rui Ma,Alan L. Wright,Xianjun Jiang
Phospholipid fatty acids (PLFAs) are widely used to assess soil microbial community and metabolic activity in various environmental samples. However, the transesterification of phospholipids into PLFAs still utilize homogeneous acid/alkaline catalyst, which may increase environment risk. This study developed and validated an accurate method for the determination of PLFAs in soil utilizing pyrolysis-assisted transesterification with diatomaceous earth. The thermolysis behavior of phospholipids, optimal pyrolysis conditions, tolerance against impurities, and recyclability of porous material were evaluated. Under optimal conditions, the transesterification can complete in only 8 min, with high impurity tolerance. The repeatability (relative standard deviation (RSD) between 2.59% and 12.12%), reproducibility (RSD between 5.22% and 16.22%), and linearity ( r between 0.9259 and 0.9999) were excellent. This method was applied to analyze the PLFAs to evaluate microbial community of 1000-years paddy soil chronosequences and the relationships among soil chemical properties, enzyme activities and microbial community composition. Soil microbial biomass PLFAs increased significantly over time followed by slight fluctuation at 60–1000 years, but the ratios of soil fungal to bacterial and gram-positive to gram-negative PLFAs remained constant. In addition to soil total N and organic matter, soil enzyme activities related to N cycling metabolism also positively significantly influenced microbial communities. Overall, the developed PLFAs method using pyrolysis-assisted transesterification was environmentally friendly, efficient, accurate, stable, and suitable alternative to acid/alkaline transesterification of phospholipids.
研究领域:
环境科学
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土壤微生物学
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分析化学
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生态学
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）