Search for: All records

ABSTRACT How multipotent progenitors give rise to multiple cell types in defined numbers is a central question in developmental biology. Epigenetic switches, acting at single gene loci, can generate extended delays in the activation of lineage-specifying genes and impact lineage decisions and cell type output. Here, we analyzed a timed epigenetic switch controlling expression of mouse Bcl11b, a transcription factor that drives T-cell commitment, but only after a multi-day delay. To investigate roles for this delay in controlling lineage decision making, we analyzed progenitors with a deletion in a distal Bcl11b enhancer, which extends this delay by ∼3 days. Strikingly, delaying Bcl11b activation reduces T-cell output but enhances innate lymphoid cell (ILC) generation in the thymus by redirecting uncommitted progenitors to the ILC lineages. Mechanistically, delaying Bcl11b activation promoted ILC redirection by enabling upregulation of the ILC-specifying transcription factor PLZF. Despite the upregulation of PLZF, committed ILC progenitors could subsequently express Bcl11b, which is also needed for type 2 ILC differentiation. These results show that epigenetic switches can control the activation timing and order of lineage-specifying genes to modulate cell type numbers and proportions. 

The random forest can reduce the variance of regression predictors through bagging while leaving the bias mostly unchanged. In general, the bias is not negligible and consequently bias correction is necessary. The default bias correction method implemented in the R package randomForest often works poorly. Several approaches have been developed which in general outperform the R default. However, little work has been done to com- prehensively evaluate the performance of these methods and thus guide users to select an appropriate method for bias correction. This paper fills this gap by providing an informa- tive ranking of these bias correction methods based on an extensive numerical study. We further offered practical suggestions on the application of the winner of these methods and suggested a visualization technique to help users decide when bias correction is needed. Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 115-131 

Underground Nuclear Astrophysics Experiment in China (JUNA) has been commissioned by taking the advantage of the ultra-low background in Jinping underground lab. High current mA level 400 KV accelerator with an ECR source and BGO detectors were commissioned. JUNA studies directly a number of nuclear reactions important to hydrostatic stellar evolution at their relevant stellar energies. In the first quarter of 2021, JUNA performed the direct measurements of 25 Mg(p, γ ) 26 Al, 19 F(p, α ) 16 O, 13 C( α ,n) 16 O and 12 C( α , γ ) 16 O near the Gamow window. The experimental results reflect the potential of JUNA with higher statistics, precision and sensitivity of the data. The preliminary results of JUNA experiment and future plan are given.