Biology subjects

Bai, Y.

Publications and source records attributed to Bai, Y..

201 records · Page 12Linked to original sources

bioRxiv2018

Reversing Glial Scar Back To Neural Tissue Through NeuroD1-Mediated Astrocyte-To-Neuron Conversion

Nerve injury often causes neuronal loss and glial proliferation, disrupting the delicate balance between neurons and glial cells in the brain. Recently, we have developed an innovative technology to convert internal reactive glial cells into functional neurons inside the mouse brain. Here, we further demonstrate that such glia-to-neuron conversion can rebalance neuron-glia ratio and reverse glial scar back to neural tissue. Specifically, using a severe stab injury model in the mouse cortex, we demonstrated that ectopic expression of NeuroD1 in reactive astrocytes significantly reduced glial reactivity and transformed toxic A1 astrocytes into less harmful astrocytes before neuronal conversion. Importantly, astrocytes were not depleted after neuronal conversion but rather repopulated due to its intrinsic proliferation capability. Remarkably, converting reactive astrocytes into neurons also significantly reduced microglia-mediated neuroinflammation. Moreover, accompanying regeneration of new neurons together with repopulation of new astrocytes, blood-brain-barrier was restored and synaptic density was rescued in the injury sites. Together, these results demonstrate that glial scar can be reversed back to neural tissue through rebalancing neuron:glia ratio after glia-to-neuron conversion.

neuroscience↗

bioRxiv2017

The Neural System of Metacognition Accompanying Decision-Making in The Prefrontal Cortex

Decision-making is usually accompanied by metacognition, through which a decision maker monitors the decision uncertainty and consequently revises the decision, even prior to feedback. However, the neural mechanisms of metacognition remain controversial: one theory proposes that metacognition coincides the decision-making process; and another addresses that it entails an independent neural system in the prefrontal cortex (PFC). Here we devised a novel paradigm of \"decision-redecision\" to investigate the metacognition process in redecision, in comparison with the decision process. We here found that the anterior PFC, including dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were exclusively activated after the initial decisions. dACC was involved in decision uncertainty monitoring, whereas lFPC was involved in decision adjustment controlling, subject to control demands of the tasks. Our findings support that the PFC is essentially involved in metacognition and further suggest that functions of the PFC in metacognition are dissociable.

neuroscience↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗