Mouse Anti-Human NSE monoclonal antibody, clone JID752

Mesenchymal stem cells (MSCs) are widely used in regenerative medicine and tissue engineering and delivering biological molecules into MSCs has been used to control stem cell behavior. However, the efficient delivery of large biomolecules such as DNA, RNA, and proteins into MSCs using nonviral delivery strategies remains an ongoing challenge. Herein, nanoparticles composed of cationic bioreducible lipid-like materials (lipidoids) are developed to intracellularly deliver mRNA into human mesenchymal stem cells (hMSCs). The delivery efficacy to hMSCs is improved by adding three excipients including cholesterol, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-polyethylene glycol (DSPE-PEG) during lipidoid nanoparticle formulation. Using an optimized lipidoid formulation, Cas9 mRNA and single guide RNA (sgRNA) targeting neuron restrictive silencing factor (NRSF) are delivered to hMSCs, leading to successful neural-like differentiation as demonstrated by the expression of synaptophysin (SYP), brain-derived neurotrophic factor (BDNF), neuron-specific enolase (NSE), and neuron-specific growth-associated protein (SCG10). Overall, a synthetic lipid formulation that can efficiently deliver mRNA to hMSCs is identified, leading to CRISPR-based gene knockdown to facilitate hMSCs transdifferentiation into neural-like lineage.

Multi-modelling aims to make use of the strengths of single hydrologic models to improve the accuracy of simulating the watershed system behavior. Considering hydrological signatures such as the flow duration curve segmentation in the calibration of each hydrologic model leads to a better parameter identifiability. In this study, a novel weighted average model-wrapper based on flow duration curve segmentation is introduced to aggregate the calibrated models into a multi-model. The proposed framework is applied to develop a model-wrapper of the Upper Assiniboine River Basin for flood forecasting upstream of the Shellmouth reservoir in the Prairie region of Canada. The HEC-HMS, HBV-EC, HSPF, and WATFLOOD hydrologic models that are being used at the Hydrologic Forecast Centre of Manitoba Infrastructure for operational inflow forecasting are calibrated using signature-based multi-objective optimization. These models have significantly different structural complexities. The calibration of each of these models is set up as three simulation-optimization problems with different objective functions to balance the model capability in simulating multiple important hydrological signatures. Results show that the model-wrapper outperforms each of the single calibrated models that are of operational use at Manitoba Infrastructure, e.g. NSE improved from 0.44 for the best individual model to 0.76 for the model-wrapper in the calibration period. Moreover, the weights associated with each hydrologic model component indicate the contribution rate of the individual models to the model-wrapper in high-flow, mid-flow, and low-flow portions of streamflow time series. Quantifying the contribution of each model component provides a deeper insight into model selection strategy, especially when a component has minimal or no contribution, e.g. HEC-HMS and HBV-EC in this paper, to the model-wrapper performance in all ranges of streamflow simulation compared to other model components.