Resilience in Environmental Risk and Impact Assessment: Concepts and Measurement

Different resilience concepts have different assumptions about system dynamics, which has implications for resilience-based environmental risk and impact assessment. Engineering resilience (recovery) dominates in the risk assessment literature but this definition does not account for the possibility of ecosystems to exist in multiple regimes. In this paper we discuss resilience concepts and quantification methods. Specifically, we discuss when a system fails to show engineering resilience after disturbances, indicating a shift to a potentially undesired regime. We show quantification methods that can assess the stability of this new regime to inform managers about possibilities to transform the system to a more desired regime. We point out the usefulness of an adaptive inference, modelling and management approach that is based on reiterative testing of hypothesis. This process facilitates learning about, and reduces uncertainty arising from risk and impact

Resilience in Environmental Risk and Impact Assessment: Concepts and Measurement

Different resilience concepts have different assumptions about system dynamics, which has implications for resilience-based environmental risk and impact assessment. Engineering resilience (recovery) dominates in the risk assessment literature but this definition does not account for the possibility of ecosystems to exist in multiple regimes. In this paper we discuss resilience concepts and quantification methods. Specifically, we discuss when a system fails to show engineering resilience after disturbances, indicating a shift to a potentially undesired regime. We show quantification methods that can assess the stability of this new regime to inform managers about possibilities to transform the system to a more desired regime. We point out the usefulness of an adaptive inference, modelling and management approach that is based on reiterative testing of hypothesis. This process facilitates learning about, and reduces uncertainty arising from risk and impact

Temporal morphogen gradient-driven neural induction shapes single expanded neuroepithelium brain organoids with enhanced cortical identity

Abstract Pluripotent stem cell (PSC)-derived human brain organoids enable the study of human brain development in vitro. Typically, the fate of PSCs is guided into subsequent specification steps through static medium switches. In vivo, morphogen gradients are critical for proper brain development and determine cell specification, and associated defects result in neurodevelopmental disorders. Here, we show that initiating neural induction in a temporal stepwise gradient guides the generation of brain organoids composed of a single, self-organized apical-out neuroepithelium, termed ENOs (expanded neuroepithelium organoids). This is at odds with standard brain organoid protocols in which multiple and independent neuroepithelium units (rosettes) are formed. We find that a prolonged, decreasing gradient of TGF-β signaling is a determining factor in ENO formation and allows for an extended phase of neuroepithelium expansion. In-depth characterization reveals that ENOs display improved cellular morphology and tissue architectural features that resemble in vivo human brain development, including expanded germinal zones. Consequently, cortical specification is enhanced in ENOs. ENOs constitute a platform to study the early events of human cortical development and allow interrogation of the complex relationship between tissue architecture and cellular states in shaping the developing human brain

Data File 7: qPCR data for DRB experiments

RNA was harvested from cytoplasmic extracts prepared from E1 (Fhit silenced) and D1 induced (Fhit-expressing) cells that had been treated with DRB (5,6-dichloro-1-β-D-ribofuranosylbenzimidazole) for between 0 and 10 hours to block transcription. The decay of individual mRNA species was monitored using RT-qPCR. The RNA samples are remainders from the experiments published in [1]. These data are the numerical values exported from a Biorad CFX Connect qPCR machine. The data are normalized to internal spikes and relative values (vs T0) are calculated for both E1 and D1 cell lines