172 research outputs found
Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
Integrating scRNA and bulk-RNA sequencing develops a cell senescence signature for analyzing tumor heterogeneity in clear cell renal cell carcinoma
IntroductionCellular senescence (CS) plays a critical role in cancer development, including clear cell renal cell carcinoma (ccRCC). Traditional RNA sequencing cannot detect precise molecular composition changes within tumors. This study aimed to analyze cellular senescence’s biochemical characteristics in ccRCC using single RNA sequencing (ScRNA-seq) and traditional RNA sequencing (Bulk RNA-seq).MethodsResearchers analyzed the biochemical characteristics of cellular senescence in ccRCC using ScRNA-seq and Bulk RNA-seq. They combined these approaches to identify differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Genes from these pathways were used to identify molecular subtypes associated with senescence, and a new risk model was constructed. The function of the gene DUSP1 in ccRCC was validated through biological experiments.ResultsThe combined analysis of ScRNA-seq and Bulk RNA-seq revealed significant differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Researchers identified genes from these pathways to identify molecular subtypes associated with senescence, constructing a new risk model. Different subgroups showed significant differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity.DiscussionSenescence signature markers are practical biomarkers and predictors of molecular typing in ccRCC. Differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity between different subgroups indicate that this approach could provide valuable insights into senescence-related treatment options and prognostic assessment for patients with ccRCC. The function of the gene DUSP1 in ccRCC was validated through biological experiments, confirming its feasibility as a novel biomarker for ccRCC. These findings suggest that targeted therapies based on senescence-related mechanisms could be an effective treatment option for ccRCC
Diesel degradation capability and environmental robustness of strain Pseudomonas aeruginosa WS02
Petroleum hydrocarbon (PHC) degrading bacteria have been frequently discovered. However, in practical application, a single species of PHC degrading bacterium with weak competitiveness may face environmental pressure and competitive exclusion due to the interspecific competition between petroleum-degrading bacteria as well as indigenous microbiota in soil, leading to a reduced efficacy or even malfunction. In this study, the diesel degradation ability and environmental robustness of an endophytic strain Pseudomonas aeruginosa WS02, were investigated. The results show that the cell membrane surface of WS02 was highly hydrophobic, and the strain secreted glycolipid surfactants. Genetic analysis results revealed that WS02 contained multiple metabolic systems and PHC degradation-related genes, indicating that this strain theoretically possesses the capability of oxidizing both alkanes and aromatic hydrocarbons. Gene annotation also showed many targets which coded for heavy metal resistant and metal transporter proteins. The gene annotation-based inference was confirmed by the experimental results: GC-MS analysis revealed that short chain PHCs (C10–C14) were completely degraded, and the degradation of PHCs ranging from C15–C22 were above 90% after 14 d in diesel-exposed culture; Heavy metal (Mn2+, Pb2+ and Zn2+) exposure was found to affect the growth of WS02 to some extent, but not its ability to degrade diesel, and the degradation efficiency was still maintained at 39–59%. WS02 also showed a environmental robustness along with PHC-degradation performance in the co-culture system with other bacterial strains as well as in the co-cultured system with the indigenous microbiota in soil fluid extracted from a PHC-contaminated site. It can be concluded that the broad-spectrum diesel degradation efficacy and great environmental robustness give P. aeruginosa WS02 great potential for application in the remediation of PHC-contaminated soil.<br/
Localized Random Lasing Modes and a New Path for Observing Localization
We demonstrate that a knowledge of the density-of-states and the eigenstates
of a random system without gain, in conjunction with the frequency profile of
the gain, can accurately predict the mode that will lase first. Its critical
pumping rate can be also obtained. It is found that the shape of the
wavefunction of the random system remains unchanged as gain is introduced.
These results were obtained by the time-independent transfer matrix method and
finite-difference-time-domain (FDTD) methods. They can be also analytically
understood by generalizing the semi-classical Lamb theory of lasing in random
systems. These findings provide a new path for observing the localization of
light, such as looking for mobility edge and studying the localized states.
%inside the random systems..Comment: Sent to PRL. 3 figure
S3E: A Large-scale Multimodal Dataset for Collaborative SLAM
With the advanced request to employ a team of robots to perform a task
collaboratively, the research community has become increasingly interested in
collaborative simultaneous localization and mapping. Unfortunately, existing
datasets are limited in the scale and variation of the collaborative
trajectories, even though generalization between inter-trajectories among
different agents is crucial to the overall viability of collaborative tasks. To
help align the research community's contributions with realistic multiagent
ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset
captured by a fleet of unmanned ground vehicles along four designed
collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor
sequences that each exceed 200 seconds, consisting of well temporal
synchronized and spatial calibrated high-frequency IMU, high-quality stereo
camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous
attempts regarding dataset size, scene variability, and complexity. It has 4x
as much average recording time as the pioneering EuRoC dataset. We also provide
careful dataset analysis as well as baselines for collaborative SLAM and single
counterparts. Data and more up-to-date details are found at
https://github.com/PengYu-Team/S3E
Finding Bursty Topics From Microblogs
Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneously captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) posts published by the same user are more likely to have the same topic. The former helps find eventdriven posts while the latter helps identify and filter out “personal ” posts. Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model than an LDA baseline and two degenerate variations of our model. We also show some case studies that demonstrate the importance of considering both the temporal information and users ’ personal interests for bursty topic detection from microblogs.
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