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دراسات حول الفضاء العالمي و\الحزام والطريق\ : (مجلد السياحة)
إن استراتيجية \"الحزام والطريق\" هي عملية طويلة الأمد، ولا يمكن وصف دورها في تعزيز تنمية السياحة ببساطة. يحلل \"الفضاء العالمي و\"حزام واحد وطريق واحد\" حجم السياحة\" الوضع الحالي لتنمية السياحة العالمية في سياق استراتيجية \"حزام واحد وطريق واحد\"، ويقدم بالتفصيل تطور السياحة العالمية والخلفية السياحية. \"حزام وطريق\"، بما في ذلك نظرة عامة على صناعة السياحة العالمية، والاتجاهات الجديدة في تنمية السياحة العالمية ونمط السياحة العالمية، وما إلى ذلك، واستنادا إلى تحليل فرص وتحديات تنمية السياحة العالمية والصينية، والتأثير الترويجي لـ \"حزام واحد وطريق واحد\"، وتم تفسير استراتيجية \"الحزام والطريق\" لتنمية السياحة.
Book
Analysis of influencing factors on review efficiency of multidisciplinary scientific research projects using DEMATEL with a 5-point scale
In the wake of advancing technology and the convergence of diverse disciplines, collaborative research across academic sectors has become instrumental in fostering innovation and tackling multifaceted challenges. The inherent complexity of such multidisciplinary endeavors, characterized by a myriad of research trajectories and a spectrum of expertise, poses significant challenges to effective review. This study aims to identify and analyze the factors influencing the review efficiency of multidisciplinary scientific research projects to ensure their smooth development and improved quality. To address this challenge, we employ the Decision Making Trial and Evaluation Laboratory (DEMATEL) method with a 5-point scale. First, we introduce an indexing framework to systematically identify factors influencing the appraisal of multidisciplinary efforts. This framework is then complemented by expert-driven questionnaires, harnessing domain-specific insights to ascertain the significance and interconnectedness of these factors. Using the DEMATEL method, we distill the data to identify key influencing factors that enhance review efficiency for multidisciplinary projects. Our findings provide pragmatic strategies and policy guidance, equipping institutional bodies and program leads with tools to refine the review process of multidisciplinary scientific research projects.
Journal Article
Long-read-based human genomic structural variation detection with cuteSV
Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at
https://github.com/tjiangHIT/cuteSV
.
Journal Article
Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers
Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.
In cancer, long noncoding RNAs (lncRNAs) can regulate immune-related pathways. Here, the authors present ImmLnc, an algorithm that can help prioritise immune-related lncRNAs in cancer immunotherapy research
Journal Article
Self-Supervised LiDAR Desnowing with 3D-KNN Blind-Spot Networks
Light Detection and Ranging (LiDAR) is fundamental to autonomous driving and robotics, as it provides reliable 3D geometric information. However, snowfall introduces numerous spurious reflections that corrupt range measurements and severely degrade downstream perception. Existing desnowing techniques either rely on handcrafted filtering rules that fail under varying snow densities, or require paired snowy–clean scans, which are nearly impossible to collect in real-world scenarios. Self-supervised LiDAR desnowing approaches address these challenges by projecting raw 3D point clouds into 2D range images and jointly training a point reconstruction network (PR-Net) and a reconstruction difficulty network (RD-Net). Nevertheless, these methods remain limited by their reliance on the outdated Noise2Void training paradigm, which restricts reconstruction quality. In this paper, we redesign PR-Net with a blind-spot architecture to overcome the limitation. Specifically, we introduce a 3D-KNN encoder that aggregates neighborhood features directly in Euclidean 3D space, ensuring geometrically consistent representations. Additionally, we integrate residual state-space blocks (RSSB) to capture long-range contextual dependencies with linear computational complexity. Extensive experiments on both synthetic and real-world datasets, including SnowyKITTI and WADS, demonstrate that our method outperforms state-of-the-art self-supervised desnowing approaches by up to 0.06 IoU while maintaining high computational efficiency.
Journal Article
Deciphering anoikis resistance and identifying prognostic biomarkers in clear cell renal cell carcinoma epithelial cells
This study tackles the persistent prognostic and management challenges of clear cell renal cell carcinoma (ccRCC), despite advancements in multimodal therapies. Focusing on anoikis, a critical form of programmed cell death in tumor progression and metastasis, we investigated its resistance in cancer evolution. Using single-cell RNA sequencing from seven ccRCC patients, we assessed the impact of anoikis-related genes (ARGs) and identified differentially expressed genes (DEGs) in Anoikis-related epithelial subclusters (ARESs). Additionally, six ccRCC RNA microarray datasets from the GEO database were analyzed for robust DEGs. A novel risk prognostic model was developed through LASSO and multivariate Cox regression, validated using BEST, ULCAN, and RT-PCR. The study included functional enrichment, immune infiltration analysis in the tumor microenvironment (TME), and drug sensitivity assessments, leading to a predictive nomogram integrating clinical parameters. Results highlighted dynamic ARG expression patterns and enhanced intercellular interactions in ARESs, with significant KEGG pathway enrichment in MYC + Epithelial subclusters indicating enhanced anoikis resistance. Additionally, all ARESs were identified in the spatial context, and their locational relationships were explored. Three key prognostic genes—TIMP1, PECAM1, and CDKN1A—were identified, with the high-risk group showing greater immune infiltration and anoikis resistance, linked to poorer prognosis. This study offers a novel ccRCC risk signature, providing innovative approaches for patient management, prognosis, and personalized treatment.
Journal Article
SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network
Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug–drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug–drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug–drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug–drug interactions. Five out of the top ten predicted new drug–drug interactions are verified from the latest database, which proves the credibility of SmileGNN.
Journal Article
The Differential Victimization Associated With Depression and Anxiety in Cross-Cultural Perspective
Traditional bullying and cyberbullying have become serious worldwide issues. The meta-analysis in this article took a cross-cultural perspective to explore whether there were any differences between the effects of cyber victimization and traditional victimization on the presence of depression and anxiety in children and adolescents and to examine the effects of moderators in explaining these differences/similarities. Fifty-six empirical studies (generating 148 independent samples) were included with a total sample size of 214,819 participants. The results indicated that the effects of cyber victimization and the subtypes of traditional victimization on anxiety were significantly different, and there was a marginally significant difference for depression. The moderating effects of country of origin were found to be significant for depression, with the mean effect size in North America being significantly higher than in China and Europe, which suggested that culture was an important factor. The moderating effects of age were also found to be significant for the relationships between traditional victimization and depression, traditional victimization and anxiety, cyber victimization and depression, and cyber victimization and anxiety. In addition, the effect size for cyber victimization and depression has increased in more recent publication years.
Journal Article
BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis
Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (
B
inning
A
cross a
S
eries of
A
ssemb
l
ies
T
oolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.
Binning is an essential step in genome-resolved metagenomic analysis in which assembled contigs originating from the same source population are clustered. However it is challenging, especially for low abundance microbial species. Here the authors introduce a toolkit that integrates multiple prominent binning tools and AI for efficient and high-resolution recovery of non-redundant bins from short- and long-read metagenomic sequencing datasets.
Journal Article
TUBA1C orchestrates the immunosuppressive tumor microenvironment and resistance to immune checkpoint blockade in clear cell renal cell carcinoma
Clear cell renal cell carcinoma (ccRCC) poses substantial treatment challenges, especially in advanced stages where the efficacy of immune checkpoint blockade (ICB) therapy varies significantly. Elevated expression of the oncogene
has been correlated with poor prognosis in various cancers, however, its role in ccRCC is unclear, especially concerning ICB resistance.
Single-cell analysis was used to examine gene expression variations in malignant cells post-ICB therapy. This included investigating TUBA1C expression across different ICB response groups and its relationship with CD274. A general module of action was identified through pan-cancer and pan-tissue analysis. TUBA1C expression and its association with clinical characteristics and prognosis was further validated. Multiple algorithms were employed to explore immune cell infiltration levels, and the DepMap database was utilized to assess gene dependency and mutation status in kidney cancer cell lines. The
knockout of TUBA1C was performed using deep learning model, complemented by immunohistochemical assays, clinical cohort and functional assays validations.
TUBA1C expression is elevated in malignant cells following ICB therapy and is correlated with ICB resistance in ccRCC. High TUBA1C expression activates PI3K/AKT pathway and is associated with increased infiltration of regulatory T cells and myeloid-derived suppressor cells, which contributes to an immunosuppressive microenvironment in ccRCC. Patients with high TUBA1C expression exhibit a greater tumor mutation burden and increased genetic variation, which causes a worse prognosis. Additionally, TUBA1C dependency and its effects were evident in kidney cancer cell lines, where mutations conferred resistance to anti-PD-L1 therapy.
knockout analyses indicated that treatment targeting TUBA1C shifted malignant cells to a state responsive to ICB therapy. Immunohistochemistry, RT-qPCR and clinical cohort validation further confirmed that TUBA1C expression was upregulated and contributed to poorer outcome in ccRCC. Finaly, wound healing and CCK-8 assays demonstrated the potent oncogenic function of TUBA1C.
TUBA1C is a pivotal regulator in ccRCC, affecting both disease progression and the effectiveness of ICB therapy by fostering an immunosuppressive microenvironment mediated by the PI3K/AKT pathway. Additionally, TUBA1C holds promise, both as a prognostic biomarker and a therapeutic target, for enhancing responsiveness to ICB.
Journal Article