This paper addresses and documents a number of issues related to the implementation of an advanced land surface-hydrology model in the Penn State-NCAR fifth-generation Mesoscale Model (MM5). The concept adopted here is that the land surface model should be able to provide not only reasonable diurnal variations of surface heat fluxes as surface boundary conditions for coupled models, but also correct seasonal evolutions of soil moisture in the context of a long-term data assimilation system. In a similar way to that in which the modified Oregon State University land surface model (LSM) has been used in the NCEP global and regional forecast models, it is implemented in MM5 to facilitate the initialization of soil moisture. Also, 1-km resolution vegetation and soil texture maps are introduced in the coupled MM5-LSM system to help identify vegetation/ water/soil characteristics at fine scales and capture the feedback of these land surface forcings. A monthly varying climatological 0.15Њ ϫ 0.15Њ green vegetation fraction is utilized to represent the annual control of vegetation on the surface evaporation. Specification of various vegetation and soil parameters is discussed, and the available water capacity in the LSM is extended to account for subgrid-scale heterogeneity. The coupling of the LSM to MM5 is also sensitive to the treatment of the surface layer, especially the calculation of the roughness length for heat/moisture. Including the effect of the molecular sublayer can improve the simulation of surface heat flux. It is shown that the soil thermal and hydraulic conductivities and the surface energy balance are very sensitive to soil moisture changes. Hence, it is necessary to establish an appropriate soil moisture data assimilation system to improve the soil moisture initialization at fine scales.

[1] This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

In optical microscopy, fine structural details are resolved by using refraction to magnify images of a specimen. Here we report the discovery that, by synthesizing a swellable polymer network within a specimen, it can be physically expanded, resulting in physical magnification. By covalently anchoring specific labels located within the specimen directly to the polymer network, labels spaced closer than the optical diffraction limit can be isotropically separated and optically resolved, a process we call expansion microscopy (ExM). Thus, this process can be used to perform scalable super-resolution microscopy with diffraction-limited microscopes. We demonstrate ExM with effective ~70 nm lateral resolution in both cultured cells and brain tissue, performing three-color super-resolution imaging of ~107 μm3 of the mouse hippocampus with a conventional confocal microscope.

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

Since cancer stem cells (CSCs) were first identified in leukemia in 1994, they have been considered promising therapeutic targets for cancer therapy. These cells have self-renewal capacity and differentiation potential and contribute to multiple tumor malignancies, such as recurrence, metastasis, heterogeneity, multidrug resistance, and radiation resistance. The biological activities of CSCs are regulated by several pluripotent transcription factors, such as OCT4, Sox2, Nanog, KLF4, and MYC. In addition, many intracellular signaling pathways, such as Wnt, NF-κB (nuclear factor-κB), Notch, Hedgehog, JAK-STAT (Janus kinase/signal transducers and activators of transcription), PI3K/AKT/mTOR (phosphoinositide 3-kinase/AKT/mammalian target of rapamycin), TGF (transforming growth factor)/SMAD, and PPAR (peroxisome proliferator-activated receptor), as well as extracellular factors, such as vascular niches, hypoxia, tumor-associated macrophages, cancer-associated fibroblasts, cancer-associated mesenchymal stem cells, extracellular matrix, and exosomes, have been shown to be very important regulators of CSCs. Molecules, vaccines, antibodies, and CAR-T (chimeric antigen receptor T cell) cells have been developed to specifically target CSCs, and some of these factors are already undergoing clinical trials. This review summarizes the characterization and identification of CSCs, depicts major factors and pathways that regulate CSC development, and discusses potential targeted therapy for CSCs.Signal Transduction and Targeted Therapy (2020) 5:8; https://doi.