The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Summary The ocean is the primary source of seismic ambient noise. Therefore, seismic recordings at seafloor stations should reveal noise characteristics more directly than land stations. However, due to a lack of broadband seismic instrumentation, seafloor noise studies using seafloor stations have been inadequate compared to land-based instrumentation. In this study, we use seismic data collected at the South China Sea (SCS) seafloor by newly developed Ocean Bottom Seismographs (OBSs) to analyze the ambient noise features in this marginal sea. The broadband OBS, dubbed ‘Pankun’, has unique shielding to isolate its sensor from the influences of bottom currents. A side-by-side land test between the OBS sensor unit and a standalone seismometer showed that the self-noise caused by the gimbal and the pressure case is insignificant. The recordings on the SCS seafloor have distinct noise spectra. The Double Frequency Microseisms (DFMs) have a single instead of double peak like that seen for Pacific stations. The peak appears in a lower period range (1–5 s) than in the global noise model, indicating that the primary source region for the DFM is the SCS itself. The high-frequency content of the DFM is attenuated more as it propagates from its source region (seafloor) to land stations. The Single Frequency Microseism (SFM) peak on the spectrum is weak, reflecting that SFMs, generated in shallow water along the coast, have difficulties propagating back into the deep ocean due to the substantial increase in seafloor depth. A long-period Earth's hum signal is also identifiable on the vertical component at periods greater than 50 s, probably due to the anti-current design of the OBS. Although the seasonal sea state mainly affects the noise level, extreme events such as typhoons can produce short-term abnormally high DFMs in the basin. However, the DFM highs caused by such events exhibit complex patterns, depending on the wind speed, duration, and area covered by the events.

The chronological biography or ‘Nianpu’ is a special style of Chinese historical writing formed by a person’s life events in time order. It provides significant records for the research of historical events. However, from syntactic collation, structural, and semantic aspects, the heterogeneousness of Nianpu materials caused the lack of systematical information collation, which could hinder the research of historical events. The article presents a semantic WebGIS platform based on an entity-oriented search and visualization system to reorganize unstructured information. It delivers: (1) a Chinese historical event database to store and manage Chinese historical information derived from the chronological biography; (2) an ontology to integrate different historical datasets; and (3) a web geographic information system to visualize and analyze information of historical events. This platform achieves the reorganization of historical event information at spatial and temporal levels by the Web. It enables humanities researchers to explore the relationships of historical entities and their evolution visually and interactively. The platform is available at https://nianpu.pkudh.org/.

A key problem for crowd-sourcing systems is motivating contributions from participants and ensuring the quality of these contributions. Games have been suggested as a motivational approach to encourage contribution, but attracting participation through game play rather than scientific interest raises concerns about the quality of the data provided, which is particularly important when the data are to be used for scientific research. To assess whether these concerns are justified, we compare the quality of data obtained from two citizen science games, one a "gamified" version of a species classification task and one a fantasy game that used the classification task only as a way to advance in the game play. Surprisingly, though we did observe cheating in the fantasy game, data quality (i.e., classification accuracy) from participants in the two games was not significantly different. As well, the quality of data from short-time contributors was at a usable level of accuracy. These findings suggest that various approaches to gamification can be useful for motivating contributions to citizen science projects.