Since China's lockdown of major cities in response to COVID-19, different forms of online participatory initiatives led by self-organizing groups of volunteers have greatly contributed to information circulation, patient admission support, and other aspects of coping with the pandemic. Although often overlooked by those studying online cultural production during the pandemic, a massive spontaneous and participatory creative outpouring of individual and collaborative artworks related to "fight the pandemic" are being published through platforms including Kuaishou, TikTok, and WeChat public accounts. This article argues that while these participatory online exhibitions published through WeChat opened up a temporary space of expression that both offset the lack of information and enabled alternative ways of understanding of and expression about the crisis, they were not only subject to pervasive state surveillance, but also co-optation by state media.
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http://dx.doi.org/10.1177/2056305120948232 | DOI Listing |
Radiother Oncol
January 2025
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology Atlanta, GA 30308, USA. Electronic address:
Purpose: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.
Methods And Materials: We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training.
Cancer Chemother Pharmacol
January 2025
Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China.
Purpose: Ovarian clear cell carcinoma is a highly malignant gynecological tumor characterized by a high rate of chemotherapy resistance and poor prognosis. The PI3K/AKT/mTOR pathway is well-known to be closely related to the progression of various malignancies, and recent studies have indicated that this pathway may play a critical role in the progression and worsening of OCCC.
Methods: In this study, we investigated the combined effects of WX390, a dual inhibitor of PI3K/mTOR, and cisplatin on OCCC through both in vitro and in vivo experiments to further elucidate their therapeutic effects.
Cancer Med
January 2025
School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Background: Immune checkpoint inhibitors (ICIs) have achieved great success; however, a subset of patients exhibits no response. Consequently, there is a critical need for reliable predictive biomarkers. Our focus is on CDC42, which stimulates multiple signaling pathways promoting tumor growth.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75 005 Paris, France.
In this contribution, we examine the interplay between target definition, data distribution, featurization approaches, and model architectures on graph-based deep learning models for thermodynamic property prediction. Through consideration of five curated data sets, exhibiting diversity in elemental composition, multiplicity, charge state, and size, we examine the impact of each of these factors on model accuracy. We observe that target definition, i.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Department of Chemical and Bimolecular Engineering, National University of Singapore, 117576 Singapore.
Biogas, primarily composed of methane (CH) and carbon dioxide (CO), is considered an alternative renewable energy resource. Efficient CO/CH separation is essential for biogas upgrading to increase energy density, and in this context, metal-organic frameworks (MOFs) have demonstrated significant potential. Here, we integrate multiscale modeling with cross-diversity machine learning (ML) to unveil MOFs with open copper sites (OCS-MOFs) that exhibit exceptional CO/CH separation performance.
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