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"Chen Liu"
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The archaeology of China : from the late paleolithic to the early bronze age
\"Past, present and future \"The archaeological materials recovered from the Anyang excavations ... in the period between 1928 and 1937...have laid a new foundation for the study of ancient China (Li, C. 1977: ix).\" When inscribed oracle bones and enormous material remains were found through scientific excavation in Anyang in 1928, the historicity of the Shang dynasty was confirmed beyond dispute for the first time (Li, C. 1977: ix-xi). This excavation thus marked the beginning of a modern Chinese archaeology endowed with great potential to reveal much of China's ancient history.. Half a century later, Chinese archaeology had made many unprecedented discoveries which surprised the world, leading Glyn Daniel to believe that \"a new awareness of the importance of China will be a key development in archaeology in the decades ahead (Daniel 1981: 211). This enthusiasm was soon shared by the Chinese archaeologists when Su Bingqi announced that \"the Golden Age of Chinese archaeology is arriving (Su, B. 1994: 139--140)\". In recent decades, archaeology has continuously prospered, becoming one of the most rapidly developing fields in social science in China\"-- Provided by publisher.
Book
Prevalence and risk factors of severe postpartum hemorrhage: a retrospective cohort study
Background
Although maternal deaths are rare in developed regions, the morbidity associated with severe postpartum hemorrhage (SPPH) remains a major problem. To determine the prevalence and risk factors of SPPH, we analyzed data of women who gave birth in Guangzhou Medical Centre for Critical Pregnant Women, which received a large quantity of critically ill obstetric patients who were transferred from other hospitals in Southern China.
Methods
In this study, we conducted a retrospective case-control study to determine the prevalence and risk factors for SPPH among a cohort of women who gave birth after 28 weeks of gestation between January 2015 and August 2019. SPPH was defined as an estimated blood loss ≥1000 mL and total blood transfusion≥4 units. Logistic regression analysis was used to identify independent risk factors for SPPH.
Results
SPPH was observed in 532 mothers (1.56%) among the total population of 34,178 mothers. Placenta-related problems (55.83%) were the major identified causes of SPPH, while uterine atony without associated retention of placental tissues accounted for 38.91%. The risk factors for SPPH were maternal age < 18 years (adjusted OR [aOR] = 11.52, 95% CI: 1.51–87.62), previous cesarean section (aOR = 2.57, 95% CI: 1.90–3.47), history of postpartum hemorrhage (aOR = 4.94, 95% CI: 2.63–9.29), conception through in vitro fertilization (aOR = 1.78, 95% CI: 1.31–2.43), pre-delivery anemia (aOR = 2.37, 95% CI: 1.88–3.00), stillbirth (aOR = 2.61, 95% CI: 1.02–6.69), prolonged labor (aOR = 5.24, 95% CI: 3.10–8.86), placenta previa (aOR = 9.75, 95% CI: 7.45–12.75), placenta abruption (aOR = 3.85, 95% CI: 1.91–7.76), placenta accrete spectrum (aOR = 8.00, 95% CI: 6.20–10.33), and macrosomia (aOR = 2.30, 95% CI: 1.38–3.83).
Conclusion
Maternal age < 18 years, previous cesarean section, history of PPH, conception through IVF, pre-delivery anemia, stillbirth, prolonged labor, placenta previa, placental abruption, PAS, and macrosomia were risk factors for SPPH. Extra vigilance during the antenatal and peripartum periods is needed to identify women who have risk factors and enable early intervention to prevent SPPH.
Journal Article
Waste tide
\"Mimi is a 'waste girl', a member of the lowest caste on Silicon Isle. Located off China's southeastern coast, Silicon Isle is the global capital for electronic waste recycling, where thousands like Mimi toil day and night, hoping one day they too will enjoy the wealth they've created for their employers, the three clans who have ruled the isle for generations. Luo Jincheng is the head of one of these clans, a role passed down from his father and grandfather before him. As the government enforces tighter restrictions, Luo in turn tightens the reins on the waste workers in his employ. Ruthlessness is his means of survival. Scott Brandle has come to Silicon Isle representing TerraGreen Recycling, an American corporation that stands to earn ungodly sums if they can reach a deal to modernize the island's recycling process. Chen Kaizong, a Chinese American, travels to Silicon Isle as Scott's interpreter. There, Kaizong is hoping to find his heritage, but finds more questions instead. The home he longs for may not exist. As these forces collide, a dark futuristic virus is unleashed on the island, and war erupts between the rich and the poor; between Chinese tradition and American ambition; between humanity's past and its future\"-- Provided by publisher.
Book
Long short-term memory (LSTM)-based news classification model
In this study, we used unidirectional and bidirectional long short-term memory (LSTM) deep learning networks for Chinese news classification and characterized the effects of contextual information on text classification, achieving a high level of accuracy. A Chinese glossary was created using jieba—a word segmentation tool—stop-word removal, and word frequency analysis. Next, word2vec was used to map the processed words into word vectors, creating a convenient lookup table for word vectors that could be used as feature inputs for the LSTM model. A bidirectional LSTM (BiLSTM) network was used for feature extraction from word vectors to facilitate the transfer of information in both the backward and forward directions to the hidden layer. Subsequently, an LSTM network was used to perform feature integration on all the outputs of the BiLSTM network, with the output from the last layer of the LSTM being treated as the mapping of the text into a feature vector. The output feature vectors were then connected to a fully connected layer to construct a feature classifier using the integrated features, finally classifying the news articles. The hyperparameters of the model were optimized based on the loss between the true and predicted values using the adaptive moment estimation (Adam) optimizer. Additionally, multiple dropout layers were added to the model to reduce overfitting. As text classification models for Chinese news articles, the Bi-LSTM and unidirectional LSTM models obtained f1-scores of 94.15% and 93.16%, respectively, with the former outperforming the latter in terms of feature extraction.
Journal Article
Spatially-informed interpolation for reconstructing lake area time series using semantic neighborhood correlation
Long-term, high-resolution records of lake surface area are essential for characterizing the spatiotemporal dynamics of inland water bodies. Although Synthetic Aperture Radar has substantially improved water extent detection under adverse conditions, optical remote sensing imagery remains the dominant data source owing to its higher spatial resolution. Nevertheless, optical data are frequently compromised by persistent cloud cover and sensor limitations, leading to substantial observational gaps. To effectively address this challenge, this study introduces a novel spatially-informed interpolation method termed Semantic Neighborhood Correlation-based Interpolation (SNCI), which leverages spatial correlations among hydrologically interconnected lakes to reconstruct missing lake area observations. By explicitly modeling the inherent hydrological and climatic coherence among neighboring lakes, SNCI provides robust, accurate, and scalable interpolations even in the presence of extensive temporal data losses. The method was evaluated on monthly lake area data from 54 lakes in the Wuhan region between 2000 and 2020, using the Global Surface Water dataset, and validated against high-resolution Dynamic World observations. Several representative lakes were analyzed in detail to assess SNCI’s robustness across diverse seasonal and interannual conditions. Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. In the case of East Lake, SNCI reduces mean absolute error by 50.1% and root mean square error by 28.3% relative to the best-performing baseline. Across all lakes, SNCI demonstrates superior accuracy and correlation, particularly under data-sparse conditions. These results underscore SNCI’s potential to enhance lake area reconstruction accuracy and support broader applications in hydrological modeling, environmental monitoring, and climate impact assessment.
Journal Article
Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies
Polymorphism in the apolipoprotein E (APOE) gene is a major genetic risk determinant of late-onset Alzheimer disease (AD), with the APOE*ε4 allele conferring an increased risk and the APOE*ε2 allele conferring a decreased risk relative to the common APOE*ε3 allele. Strong evidence from clinical and basic research suggests that a major pathway by which APOE4 increases the risk of AD is by driving earlier and more abundant amyloid pathology in the brains of APOE*ε4 carriers. The number of amyloid-β (Aβ)-dependent and Aβ-independent pathways that are known to be differentially modulated by APOE isoforms is increasing. For example, evidence is accumulating that APOE influences tau pathology, tau-mediated neurodegeneration and microglial responses to AD-related pathologies. In addition, APOE4 is either pathogenic or shows reduced efficiency in multiple brain homeostatic pathways, including lipid transport, synaptic integrity and plasticity, glucose metabolism and cerebrovascular function. Here, we review the recent progress in clinical and basic research into the role of APOE in AD pathogenesis. We also discuss how APOE can be targeted for AD therapy using a precision medicine approach.
Journal Article
Reciprocity, evolution, and decision games in network and data science
\"Learn how to analyze and manage evolutionary and sequential user behaviors in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision-making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behavior and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing\"-- Provided by publisher.
Book
Interaction of Microglia and Astrocytes in the Neurovascular Unit
The interaction between microglia and astrocytes significantly influences neuroinflammation. Microglia/astrocytes, part of the neurovascular unit (NVU), are activated by various brain insults. The local extracellular and intracellular signals determine their characteristics and switch of phenotypes. Microglia and astrocytes are activated into two polarization states: the pro-inflammatory phenotype (M1 and A1) and the anti-inflammatory phenotype (M2 and A2). During neuroinflammation, induced by stroke or lipopolysaccharides, microglia are more sensitive to pathogens, or damage; they are thus initially activated into the M1 phenotype and produce common inflammatory signals such as IL-1 and TNF-α to trigger reactive astrocytes into the A1 phenotype. These inflammatory signals can be amplified not only by the self-feedback loop of microglial activation but also by the unique anatomy structure of astrocytes. As the pathology further progresses, resulting in local environmental changes, M1-like microglia switch to the M2 phenotype, and M2 crosstalk with A2. While astrocytes communicate simultaneously with neurons and blood vessels to maintain the function of neurons and the blood-brain barrier (BBB), their subtle changes may be identified and responded by astrocytes, and possibly transferred to microglia. Although both microglia and astrocytes have different functional characteristics, they can achieve immune \"optimization\" through their mutual communication and cooperation in the NVU and build a cascaded immune network of amplification.
Journal Article