Automated Author ProfileChen, L
Chen, L
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We present the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection, by using the in situ measurements of the Magnetospheric Multiscale mission in the Earth's magnetotail on 26 July 2017. The structures are selected by identifying a bipolar signature in the magnetic field and categorized as plasmoids or current sheets via an automated algorithm which examines current density and plasma flow. The size of the plasmoids forms a decaying exponential distribution ranging from subelectron up to ion scales. The presence of substantial number of current sheets is consistent with a physical picture of dynamic production and merging of plasmoids during turbulent reconnection. The magnetic structures are locations of significant energy dissipation via electric field parallel to the local magnetic field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.
Authors
- Bergstedt, K ;
- Ji, H ;
- Jara-Almonte, J ;
- Yoo, J ;
- Ergun, R ;
- Chen, L
The transcriptome available here was generated from the same sample of peripheral blood mononuclear cells (PBMCs) from a consented donor (Homo sapiens) whose genome was deciphered in the YH project. YH is an anonymous male Han Chinese individual who has no known genetic diseases, and whose genome also serves as an Asian reference genome. These data were used to detect RNA-editing events using a pipeline that filtered and compared RNA-seq transcriptome and whole genome sequencing data (doi:10.1038/nbt.2122).RNA was extracted from viable lymphoblastoid cell line of the YH individual, treated with DNase I to remove residual DNA, and double-stranded cDNA was synthesized from these RNA samples using random hexamer-primer and reverse transcriptase. For some of the libraries, samples were treated with Duplex-Specific thermostable nuclease (DSN) enzyme prior to cluster generation.Sequencing libraries for strand specific transcriptome analysis were synthesized from fragmented RNA with random hexamer primers. After purification to remove dNTPs, second-strand synthesis was performed. Upon ligation with the Illumina PE adapters, the products were gel-recovered and subsequently digested with N-Glycosylase to remove the second-strand cDNA. Samples were then amplified by 15 cycles of PCR with Phusion polymerase and PCR primers with barcode sequence.The libraries were prepared based on the Illumina pair end library protocol and subsequently sequenced by the Illumina HiSeq 2000 platform. Eight lanes of the flow cell were applied to the poly(A)+ RNA library, which was sequenced for 75 or 100 bp; five lanes were used for the poly(A) RNA library, which was sequenced for 90 bp.
Authors
- Tian, Zhijian ;
- Chen, L ;
- Ou, Y ;
- Hu, Xueda