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"Shen, Qian"
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Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression
Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.
•Rumination is strongly and consistently correlated with depression.•Meta-analyze the findings of brain regions regarding to rumination.•Specifically examined the contribution of three DMN subsystems to rumination.•Rumination is specifically correlated with the DMN core regions and the dorsal medial prefrontal cortex subsystem.
Journal Article
Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020
Since early January 2020, after the outbreak of coronavirus infection in Wuhan, China, ≈365 confirmed cases have been reported in Shenzhen, China. The mode of community and intrafamily transmission is threatening residents in Shenzhen. Strategies to strengthen prevention and interruption of these transmissions should be urgently addressed.
Journal Article
Exploring impact of digital economy on sustainable urban development by panel data of 30 underdeveloped cities in China
How the digital economy affects sustainable urban development is a worthwhile research problem. Based on the panel data of 30 underdeveloped cities in China from 2011–2020, the Super-SBM model and dynamic QCA (Qualitative Comparative Analysis) method were combined to analyze the complex relationship between the digital economy and sustainable urban development. From the configuration perspective, exploring how to use the digital economy to enhance the green development level of some underdeveloped cities in northwest China is of great significance for filling this gap, establishing a cross-city green coordinated development mechanism, and formulating sustainable development policies for urban management departments. It has been found that individual digital economy elements are not necessary to enhance the level of sustainable urban development. There exist three different models for achieving green and high-quality development: high penetration effect, high scale-penetration effect, and high scale effect. For the high penetration effect model, policies promoting the development of high digital industries, high levels of economic development, a high degree of openness to the outside world, and non-high industrial structures, can be made as core conditions, with digital infrastructure construction as auxiliary conditions to fully generate high green development efficiency. For the large-scale penetration effect model, policies promoting the development of high digital industries, high digital infrastructure, high digital finance, high economic development level, high industrial structure, and non-high technique progress can generate high green development efficiency. For the large-scale effect model, policies promoting the development of non-high digital industries, high digital finance, high degree of openness to the outside world, high industrial structure, and non-high technique progress can be made as core conditions, with economic development level as auxiliary conditions, can fully generate high green development efficiency.
Journal Article
Academic discourse on ChatGPT in social sciences: A topic modeling and sentiment analysis of research article abstracts
The rapid emergence of ChatGPT has sparked extensive academic discourse across multiple fields. This study focuses on such discourse within the social sciences by examining how scholars frame and evaluate ChatGPT through research article abstracts. Drawing on 1,227 SSCI-indexed abstracts published between 30 November 2022 and 30 November 2024, we adopt a two-step natural language processing approach. First, we apply topic modeling to identify major thematic patterns in academic discussions of ChatGPT. Then, we perform sentiment analysis to examine how scholars’ evaluative attitudes are discursively constructed across these thematic areas. Topic modeling reveals six key themes: artificial intelligence (AI) and technology communication, education and learning tools, user perception and adoption, ethics and academic challenges, human-technology interaction, and computational foundations of Large Language Models (LLMs). Sentiment analysis suggests that approximately 82.97% of abstracts express positive attitudes, particularly regarding ChatGPT’s research potential and pedagogical utility, while around 9.78% reflect more cautious or negative views, often focusing on issues such as academic integrity and misinformation. These sentiment patterns appear to vary across thematic areas, with user adoption and education-related topics showing greater positivity, while ethics-oriented discussions exhibit relatively more critical perspectives. By analyzing academic discourse as reflected in research article abstracts, this study contributes a discourse-level perspective on how ChatGPT is framed, endorsed, and critically examined in the social sciences. It offers a data-driven complement to existing conceptual and survey-based investigations and draws attention to both the thematic and evaluative tendencies shaping scholarly narratives around generative AI.
Journal Article
Stance markers in English medical research articles and newspaper opinion columns: A comparative corpus-based study
Stance markers are critical linguistic devices for writers to convey their personal attitudes, judgments or assessments about the proposition of certain messages. Following Hyland’s framework of stance, this study investigated the distribution of stance markers in two different genres: medical research articles (medical RA) and newspaper opinion columns (newspaper OC). The corpus constructed for the investigation includes 52 medical research articles and 175 newspaper opinion articles, which were both written in English and published from January to April in 2020 with the topic focusing on COVID-19. The findings of this study demonstrated that the occurrences of stance markers in newspaper OC were far more frequent than those in medical RA, indicating the different conventions of these two genres. Despite the significant difference in the occurrences of stance markers between the two sub-corpora, similarities of the most frequent stance markers in two genres were also highlighted. The study indicated that the topic content seems to play an important role in shaping the way of how writers construct their stance. The lack of information or evidence on the topic of COVID-19 could restrain writers from making high degree of commitment to their claims, which make them adopt a more tentative stance to qualify their statements.
Journal Article
Ultra-Dense Uplink UAV Lossy Communications: Trajectory Optimization Based on Mean Field Game
This paper investigates a multiple unmanned aerial vehicle (UAV) enabled network for supporting emergency communication services, where each drone acts as a base station (also called the drone small cell (DSC)). The novelty of this paper is that a mean field game (MFG)-based strategy is conceived for jointly controlling the three-dimensional (3D) locations of these drones to guarantee the distortion requirement of lossy communications, while considering the inter-cell interference and the flight energy consumption of drones. More explicitly, we derive the Hamilton–Jacobi–Bellman (HJB) and Fokker–Planck–Kolmogorov (FPK) equations, and propose an algorithm where both the Lax–Friedrichs scheme and the Lagrange relaxation are invoked for solving the HJB and FPK equations with 3D control vectors and state vectors. The numerical results show that the proposed algorithm can achieve a higher access rate with a similar flight energy consumption.
Journal Article
TLR7 gain-of-function genetic variation causes human lupus
Although circumstantial evidence supports enhanced Toll-like receptor 7 (TLR7) signalling as a mechanism of human systemic autoimmune disease
1
–
7
, evidence of lupus-causing
TLR7
gene variants is lacking. Here we describe human systemic lupus erythematosus caused by a
TLR7
gain-of-function variant. TLR7 is a sensor of viral RNA
8
,
9
and binds to guanosine
10
–
12
. We identified a de novo, previously undescribed missense
TLR7
Y264H
variant in a child with severe lupus and additional variants in other patients with lupus. The
TLR7
Y264H
variant selectively increased sensing of guanosine and 2',3'-cGMP
10
–
12
, and was sufficient to cause lupus when introduced into mice. We show that enhanced TLR7 signalling drives aberrant survival of B cell receptor (BCR)-activated B cells, and in a cell-intrinsic manner, accumulation of CD11c
+
age-associated B cells and germinal centre B cells. Follicular and extrafollicular helper T cells were also increased but these phenotypes were cell-extrinsic. Deficiency of MyD88 (an adaptor protein downstream of TLR7) rescued autoimmunity, aberrant B cell survival, and all cellular and serological phenotypes. Despite prominent spontaneous germinal-centre formation in
Tlr7
Y264H
mice, autoimmunity was not ameliorated by germinal-centre deficiency, suggesting an extrafollicular origin of pathogenic B cells. We establish the importance of TLR7 and guanosine-containing self-ligands for human lupus pathogenesis, which paves the way for therapeutic TLR7 or MyD88 inhibition.
The missense
TLR7
Y264H
gain-of-function genetic variation causes systemic lupus erythematosus in humans and mice.
Journal Article