Lü Liang

University of California, Berkeley

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Peng Gong

University of Hong Kong

Qiyong Liu

National Institute for Communicable Disease Control and Prevention

Jacob Daniels

University of North Texas

Bing Xu

Tsinghua University

Michael Biancardi

University of North Texas

Yuye Zhou

Nanjing University

Yong Luo

Tsinghua University

Le Yu

Tsinghua University

Wenjia Cai

Tsinghua University

Liu Qiyong

National Institute for Communicable Disease Control and Prevention

Cooperative Institutions

Tsinghua University

159

Chinese Academy of Sciences

156

Peking University

101

Sun Yat-sen University

Chinese Academy of Medical Sciences & Peking Union Medical College

Zhejiang University

University of Chinese Academy of Sciences

Capital Medical University

Beijing Normal University

Sichuan University

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Quarterly Reconstructed Texas AOD (95% threshold)

Figshare (2023)

Lü Liang Jacob Daniels Michael Biancardi Yuye Zhou

This is the polished aggregated AOD quarterly composites for the state of Texas, USA from Jan 2010 to Aug 2022, using spatio-temporal LSTM convolutional autoencoder model. This dataset is generated by averaging the monthly layers from "Monthly Reconstructed Texas AOD (95% threshold)".

10.6084/m9.figshare.23589375

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GO and KEGG enrichment Positive Selection result for Siniperca undulata and S. obscura

Figshare (2023)

Lü Liang

GO and KEGG enrichment result for Gene family and Positive Selection

KEGG

10.6084/m9.figshare.21731762

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Yearly Reconstructed Texas AOD

Figshare (2023)

Lü Liang Jacob Daniels Michael Biancardi Yuye Zhou

This is the aggregated AOD annual composites for the state of Texas, USA from Jan 2010 to Aug 2022, using a spatio-temporal LSTM convolutional autoencoder model.

10.6084/m9.figshare.21385401

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Filling Cloud Gaps in Satellite AOD Retrievals Using an LSTM CNN-Autoencoder Model

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2022)

Jacob Daniels Colleen P. Bailey Lü Liang

Satellite imagery enables spatially-temporally continuous monitoring and understanding of global environmental factors for a range of applications. This data, however, often suffers from gaps in retrieval from sensor malfunction or atmospheric interference, particularly dense clouds that obscure parts or all of an area. For dynamic datasets such as the MCD19A2 Aerosol Optical Depth (AOD) dataset, gap filling is especially challenging. The difficulty lies in the often large, continuous blocks of cloudy pixels with missing data that limit the ability of spatial filling and the daily fluctuation in features such as AOD that incur high difficulty in gap filling from temporal trends. In this study, we propose a spatiotemporal long short-term memory (LSTM) convolutional autoencoder method that effectively reconstructs missing data resulting from thick cloud interference for MODIS AOD data. The proposed method outperforms previous methods of reconstructing data lost to thick cloud interference in AOD retrievals with a generalized network achieving a weighted average PSNR, SSIM, and R 2 of 47.2, 0.992, and 0.941, respectively, between original, cloud-free days and those same days masked with simulated thick cloud interference without the need for additional covariates.

Autoencoder

10.1109/igarss46834.2022.9884482

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A new assessment mechanism of primary frequency regulation capability for a supercritical thermal power plant in deep peaking

Energy Science & Engineering (2022)

Feng Hong Weiming Ji Lü Liang Jie Song Junhong Hao

Abstract With the rapid development of renewable energy, the primary frequency control (PFC) is becoming more critical and significant to ensure the stability of the electrical power system. As the main role of PFC, the thermal power unit tends to in a wide operation interval, and the changes in PFC characteristics along with working conditions are inevitable. By decomposing and quantifying the dynamic energy conversion process, this paper proposes a novel mechanism to evaluate the PFC capability for the supercritical thermal power plants that covered 30%–100% of the rated load. In the mechanism, a refined steam turbine model is established to reflect the short‐term dynamic response of PFC by combining it with the coupling characteristics of the main steam valve and main steam pressure in the later phase. Meanwhile, as a new prior assessment index, the equivalent electricity has been put forward to quantify the PFC capability for the power plant. In a case study of a 600 MW supercritical power plant, the capacities of PFC under 30%–50% of the rated load are quantified for the first time. Comparison results between theoretical calculation and real PFC test show that the proposed PFC capability assessment mechanism has acceptable accuracy of 96.1%, which could be effective to assess the frequency response reserve and stability margin in the grid.

10.1002/ese3.1345

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Climate-driven scrub typhus incidence dynamics in South China: A time-series study

Frontiers in Environmental Science (2022)

Wen Li Yanlin Niu Hongyan Ren Wanwan Sun W. F. Mader

Background: Scrub typhus (ST) is a climate-sensitive infectious disease. However, the nonlinear relationship between important meteorological factors and ST incidence is not clear. The present study identified the quantitative relationship between ST incidence and meteorological factors in southern China. Methods: The weekly number of ST cases and simultaneous meteorological variables in central Guangdong Province from 2006 to 2018 were obtained from the National Notifiable Infectious Disease Reporting Information System and the Meteorological Data Sharing Service System, respectively. A quasi-Poisson generalized additive model combined with a distributed lag nonlinear model (DLNM) was constructed to analyze the lag-exposure-response relationship between meteorological factors and the incidence of ST. Results: A total of 18,415 ST cases were reported in the study area. The estimated effects of meteorological factors on ST incidence were nonlinear and exhibited obvious lag characteristics. A J-shaped nonlinear association was identified between weekly mean temperature and ST incidence. A reversed U-shaped nonlinear association was noted between weekly mean relative humidity and ST incidence. The risk of ST incidence increased when the temperature ranged from 24°C to 28°C, the relative humidity was between 78% and 82%, or the precipitation was between 50 mm and 150 mm, using the medians as references. For high temperatures (75th percentile of temperature), the highest relative risk (RR) was 1.18 (95% CI: 1.10–1.27), with a lag effect that lasted 5 weeks. High relative humidity (75th percentile of relative humidity) and high precipitation (75th percentile of precipitation) could also increase the risk of ST. Conclusion: This study demonstrated the nonlinear relationship and the significant positive lag effects of temperature, relative humidity, and precipitation on the incidence of ST. Between particular thresholds, temperature, humidity, and levels of precipitation increased the risk of ST. These findings suggest that relevant government departments should address climate change and develop a meteorological conditions-depend strategy for ST prevention and control.

Distributed lag

10.3389/fenvs.2022.849681

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Additional file 1: of Comparative analysis of rodent and small mammal viromes to better understand the wildlife origin of emerging infectious diseases

Figshare (2018)

Zhìqiáng Wú Lü Liang Jiang Du Li Yang Xianwen Ren

Table S1. Samples of the 55 animal species used in this study and the provinces and dates of collection. Table S2. The reads of virus under each family. Table S3. The reads of mammailan virus under each family. Table S4. Origin and accession number of viruses identified in this study. Table S5. Amino acid identity of rodents Arenavirus and representatives of other species. Table S6. The aa identities (%) of the predicted ORF1a and ORF1b between Rodent Arteriviruses and other known members of the family Arteriviridae (including a tentative member, WPDV). Table S7. The aa identities (%) between these hepaciviruses and other known rodent hepacivirus. Table S8. Amino acid identity (%)of rodents Hepatitis E and representatives of other Genotypes. Table S9. The aa identities (%) between these BtCoVs and other known members of the lineage-A beta-CoVs. Table S10. The aa identities (%) between these BtCoVs and other known members of alpha-CoVs. Table S11. Amino acid identity of rodents picornaviruses and representatives of other genera in P1, P2, and P3 regions. Table S12. Pairwise amino acid identities (%) of predicted gene products of RtAp-ParaV/NX2015 compared to other Jeilongvirus members. Table S13. Amino acid identity of rodents parvovirus and representatives of other genera. (XLSX 149 kb)

Mammal

Marine mammal

10.6084/m9.figshare.7164578

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CCDC 750992: Experimental Crystal Structure Determination

The Cambridge Structural Database (2009)

Lü Liang Weijiang Zhang

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

10.5517/cct6gk0

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Phenological Inference from Times Series Remote Sensing Data

CRC Press eBooks (2018)

Iryna Dronova Lü Liang

Phenology broadly refers to seasonal phenomena associated with biological cycles and activity of plants and animals triggered by changes in temperature, precipitation, daylight, and other environmental factors. While specific schedule and timing of these changes may vary from year to year due to natural variations in climate and weather, directional phenological shifts such as consistently earlier times of leaf flushing and flowering of plants may provide important early warning signals of major climatic transitions and global warming effects (Cleland et al., 2007). Such shifts may be observed not only in natural systems, but also in heavily human-dominated landscapes. For example, urban regions with higher temperatures compared to exurbia and wildlands may also display longer growing seasons (Zipper et al., 2016). The ability to monitor phenological changes is crucial for understanding the underlying physical transitions in the landscapes and dynamics of important ecosystem services such as productivity and uptake of atmospheric greenhouse gas CO2 by plants, agricultural crop pollination, and biological pest control associated with plant-animal seasonal cycles and many other (Cho et al., 2017; Klosterman et al., 2014; Richardson et al., 2012; Toomey et al., 2015).

10.1201/9781315166636-4

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China must reduce fertilizer use too

Nature (2011)

Peng Gong Lü Liang Qiang Zhang

10.1038/473284e

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Citations (66)