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The commensal microbiota regulates the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs) in bone marrow. Whether and how the microbiota influences HSPC development during embryogenesis is unclear. Using gnotobiotic zebrafish, we show that the microbiota is necessary for HSPC development and differentiation. Individual bacterial strains differentially affect HSPC formation, independent of their effects on myeloid cells. Early-life dysbiosis in chd8 −/− zebrafish impairs HSPC development. Wild-type microbiota promote HSPC development by controlling basal inflammatory cytokine expression in kidney niche, and chd8 −/− commensals elicit elevated inflammatory cytokines that reduce HSPCs and enhance myeloid differentiation. We identify an Aeromonas veronii strain with immuno-modulatory activities that fails to induce HSPC development in wild-type fish but selectively inhibits kidney cytokine expression and rebalances HSPC development in chd8 −/− zebrafish. Our studies highlight the important roles of a balanced microbiome during early HSPC development that ensure proper establishment of lineal precursor for adult hematopoietic system. [Display omitted] • Microbiota depletion impairs HSPC development during embryogenesis in zebrafish • Disturbed microbiota in chd8 −/− decreases HSPC formation and promotes myelopoiesis • Different strains induce different levels of cytokine expression in HSPC niche • Local cytokine expression controls HSPC formation and myeloid differentiation Zhong et al. show that commensal microbiota regulates normal HSPC development by mediating appropriate inflammatory cytokine expression in HSPC niche. The microbiota of chd8 −/− zebrafish induces enhanced cytokine expression and impairs HSPC development. An Aeromonas strain selectively suppresses excessive cytokines in HSPC niche and restores HSPC development in chd8 −/− zebrafish. [ABSTRACT FROM AUTHOR]

Endocrine disrupting chemicals (EDCs) can act as agonists, antagonists or mixed agonists/antagonists toward estrogen receptor α (ERα) and glucocorticoid receptor (GR) in a tissue- and cell-specific manner. However, the activation/inhibition mechanism by which structurally different chemicals induce various types of disruption remain ambiguous. This unrevealed theory limited the in silico modeling of EDCs and the prioritization of potential EDCs for experimental testing. As a kind of chemical widely used in manufacture, bisphenols (BPs) have attracted great attentions on their potential endocrine disrupting effects. BPs used in this study exhibited pure agonistic, pure antagonistic or mixed agonistic/antagonistic activities toward ERα and/or GR. According to the mechanistic modeling, the pure agonistic and pure antagonistic activities were attributed to a single type of protein conformation induced by BPs-ERα and/or BPs-GR interactions, whereas the mixed agonistic/antagonistic activities were attributed to multiple conformations that concomitantly exist. After interacting with BPs, the active conformation recruits coactivator to induce agonistic activity and the blocked conformation inhibits coactivator to induce antagonistic activity, whereas the concomitantly-existing multiple conformations (active, blocked and competing conformations) recruit coactivator, recruit corepressor and/or inhibit coactivator to dually induce the agonistic and antagonistic activities. Therefore, the in silico modeling in this study can not only predict ERα and GR disrupting activities but also, especially, identify the potential mechanisms. This mechanistic study breaks the current bottleneck of computational toxicology and can be widely used to prioritize potential estrogen/glucocorticoid disruptor for experimental testing in both pre-clinic and clinic studies. Unlabelled Image • The estrogen and glucocorticoid disrupting activities of bisphenols were cell line-dependent. • Molecular mechanisms are related to ER/GR conformations and coregulator recruitment/inhibition. • Mixed agonist/antagonist may result from multiple conformations of ER/GR. • An in silico workflow for prediction of endocrine disruptors was developed. [ABSTRACT FROM AUTHOR]

In nuclear radiation detection, some measured radiation counts are lost due to dead time. Estimation of dead time is necessary to restore the counting information in quantitative analysis. This study aims to propose a method for estimating dead time at high counting rate. First of all, a measurement system of pulse interval distribution was used to estimate the true input counting rate of the detection system and implemented in FPGA. Then, a digital pole-zero cancellation technique was introduced in the measurement system to decrease the decay time constant of pulse. This was aimed at mitigating the impact of pile-up on the pulse interval distribution spectrum, especially at high counting rate. The feasibility of this measurement system at high counting rate was verified by the experimental platform of EDXRF. The dead time was calculated using the measured counting rate and the estimated true input counting rate. The accuracy of the dead time relies on the precision of the estimated true count rate. When the counting rate reaches 600 kCPS, the relative error between the theoretical counting rate and the estimated true input counting rate is less than 5%. • An EDXRF was used to verify the validity of pulse interval distribution measurement system to estimate the dead time. • Estimation of the true input counting rate accurately in the high counting rate scenario. • The measured counting rate will enter the nonlinear region when the dead time exceeds 20%. [ABSTRACT FROM AUTHOR]