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Cell fate can be directly reprogrammed from accessible cell types (e.g., fibroblasts) into functional cell types by exposure to small molecule stimuli. However, no chemical reprogramming method has been reported to date that successfully generates functional hepatocyte-like cells that can repopulate liver tissue, casting doubt over the feasibility of chemical reprogramming approaches to obtain desirable cell types for therapeutic applications.Here, through chemical induction of phenotypic plasticity, we provide a proof-of-concept demonstration of the direct chemical reprogramming of mouse fibroblasts into functional hepatocyte-like cells using exposure to small molecule cocktails in culture medium to successively stimulate endogenous expression of master transcription factors associated with hepatocyte development, such as hepatocyte nuclear factor 4a, nuclear receptor subfamily 1, group I, member 2, and nuclear receptor subfamily 1, group H, member 4. RNA sequencing analysis, metabolic assays, and in vivo physiological experiments show that chemically induced hepatocytes (CiHeps) exhibit comparable activity and function to primary hepatocytes, especially in liver repopulation to rescue liver failure in fumarylacetoacetate hydrolaseOur findings show that direct chemical reprogramming can generate hepatocyte-like cells with high-quality physiological properties, providing a paradigm for establishing hepatocyte identity in fibroblasts and demonstrating the potential for chemical reprogramming in organ/tissue repair and regeneration therapies.

Mouse somatic cells can be chemically reprogrammed into pluripotent stem cells (CiPSCs) through an intermediate extraembryonic endoderm (XEN)-like state. However, it is elusive how the chemicals orchestrate the cell fate alteration. In this study, we analyze molecular dynamics in chemical reprogramming from fibroblasts to a XEN-like state. We find that Sox17 is initially activated by the chemical cocktails, and XEN cell fate specialization is subsequently mediated by Sox17 activated expression of other XEN master genes, such as Sall4 and Gata4. Furthermore, this stepwise process is differentially regulated. The core reprogramming chemicals CHIR99021, 616452 and Forskolin are all necessary for Sox17 activation, while differently required for Gata4 and Sall4 expression. The addition of chemical boosters in different phases further improves the generation efficiency of XEN-like cells. Taken together, our work demonstrates that chemical reprogramming is regulated in 3 distinct “prime–specify–transit” phases initiated with endogenous Sox17 activation, providing a new framework to understand cell fate determination., Yang, Xu, Gu et al. demonstrate that activation of endogenous Sox17 pushes fibroblasts to an extraembryonic endoderm-like state in chemically induced reprogramming of somatic cells into stem cells. This study provides insights into how chemicals prime the transition of somatic cells into stem cells.

Coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2 cause respiratory diseases with remarkably heterogeneous progression. This in part reflects the viral ability to influence the cytokine secretion and thereby the innate immune system. Especially the viral interference of IFN-I signaling and the subsequent deficiency of innate immune response in the early phase have been associated with rapid virus replication and later excessive immune responses. We propose a mathematical framework to analyze IFN-I signaling and its impact on the interaction motif between virus, NK cells and macrophages. The model recapture divergent dynamics of coronavirus infections including the possibility for elevated secretion of IL-6 and IFN-γas a consequence of exacerbated macrophage activation. Dysfunction of NK cells recruitment increase disease severity by leading to a higher viral load peak, the possibility for excessive macrophage activation, and an elevated risk of the cytokine storm. Thus the model predicts that delayed IFN-I signaling could lead to pathogenicity in the latter stage of an infection. Reversely, in case of strong NK recruitment from infected cells we predict a possible chronic disease state with moderate and potentially oscillating virus/cytokine levels.

The development of graphitic carbon nitride (CN) based photocatalysts towards efficient visible light-driven H2 evolution is highly desired for solar energy conversion. It is well-known that bulk CN materials possess three intrinsic problems, namely, high charge recombination loss, low specific surface area, and limited sunlight harvesting range. To simultaneously overcome the abovementioned drawbacks of CN, we report an innovative multiple modification strategy, involving molecular grafting of the CN network, exfoliation to ultrathin nanosheets, and hybridization with TiO2 photocatalysts. The visible light utilization ability, specific surface area, and charge separation efficiency of the CN materials improved accordingly. As expected, the TiO2/CNX-NS heterojunction photocatalyst exhibited remarkably enhanced visible light-driven H2 production rate of 138.4 μmol h-1, which was about 4.6 times higher than that of pristine CN. The excellent photocatalytic performance under visible light confirmed the successful improvement in the corresponding drawbacks of CN by each modification. In this study, we propose the possibility of combining multiple modifications in the same system to synthesize an excellent visible light-driven photocatalyst for solar-to-fuel conversion.

A TiO2 heterostructure modified with carbon nitride nanosheets (CN-NSs) has been synthesized via a direct interfacial assembly strategy. The CN-NSs, which have a unique two-dimensional structure, were favorable for supporting TiO2 nanoparticles (NPs). The uniform dispersion of TiO2 NPs on the surface of the CN-NSs creates sufficient interfacial contact at their nanojunctions, as was confirmed by electron microscopy analyses. In comparison with other reported metal oxide/CN composites, the strong interactions of the ultrathin CN-NSs layers with the TiO2 nanoparticles restrain their re-stacking, which results in a large specific surface area of 234.0 m2·g–1. The results indicate that the optimized TiO2/CN-NSs hybrid exhibits remarkably enhanced photocatalytic efficiency for dye degradation (with k of 0.167 min–1 under full spectrum) and H2 production (with apparent quantum yield = 38.4% for λ = 400 ± 15 nm monochromatic light). This can be ascribed to the improved surface area and quantum efficiency of the hybrid, with a controlled ratio that reaches the appropriate balance between producing sufficient nanojunctions and absorbing enough photons. Furthermore, based on the identification of the main active species for photodegradation, and the confirmation of active sites for H2 evolution, the charge transfer pathway across the TiO2/CN-NSs interface under simulated solar light is proposed.

The development of graphitic carbon nitride (CN) based photocatalysts towards efficient visible light-driven H2 evolution is highly desired for solar energy conversion. It is well-known that bulk CN materials possess three intrinsic problems, namely, high charge recombination loss, low specific surface area, and limited sunlight harvesting range. To simultaneously overcome the abovementioned drawbacks of CN, we report an innovative multiple modification strategy, involving molecular grafting of the CN network, exfoliation to ultrathin nanosheets, and hybridization with TiO2 photocatalysts. The visible light utilization ability, specific surface area, and charge separation efficiency of the CN materials improved accordingly. As expected, the TiO2/CNX-NS heterojunction photocatalyst exhibited remarkably enhanced visible light-driven H2 production rate of 138.4 μmol h-1, which was about 4.6 times higher than that of pristine CN. The excellent photocatalytic performance under visible light confirmed the successful improvement in the corresponding drawbacks of CN by each modification. In this study, we propose the possibility of combining multiple modifications in the same system to synthesize an excellent visible light-driven photocatalyst for solar-to-fuel conversion.

As a promising photocatalyst, the large bandgap (3.2 eV) of anatase TiO2 seriously limits its light absorption of the UV portion of the solar spectrum, making it less applicable in industrial fields. A popular approach for enhancing visible light activity by narrowing the bandgap is doping, however, the dopant-induced defect states in the TiO2 lattice may act as recombination centers for the photogenerated charge carriers. Here we report a facile soft-chemical route to engineer the surface properties of TiO2 crystals using ethanol as the sole organic solvent. The individual TiO2 nanocrystals synthesized in the first step, possessing high affinity with ethanol molecules, tend to assemble together by interfacial Ti–Ti bonding during the following ethanol evaporation induced self-assembly process. Formation of Ti–Ti bonds at the interface simultaneously brings about the decrease of surface oxygen atoms in the TiO2 structural unit, which dramatically alters the electronic structure and extends the light absorption to ∼550 nm. Such a dopant-/additive-free TiO2 assembly exhibits considerable photocatalytic activity under visible light due to its narrower bandgap than individual nanocrystals. Further, an electron paramagnetic resonance measurement is used to confirm the capability of generating reactive ˙OH radicals on the surface of assembled TiO2 under visible-light irradiation.

Efficient metal-free visible photocatalysts with high stability are highly desired for sufficient utilization of solar energy. In this work, the popular carbon nitride (CN) photocatalyst is rationally modified by acid exfoliation of molecular grafted CN, achieving improved visible-light utilization and charge carriers mobility. Moreover, the modification process tuned the surface electrical property of CN, which enabled it to be readily coupled with the oppositely charged graphene oxide during the following photo-assisted electrostatic assembly. Detailed characterizations indicate the formation of well-contacted 2D/2D heterostructure with strong interfacial interaction between the modified CN nanosheets (CNX-NSs) and reduced graphene oxide (RGO). The optimized hybrid (with a RGO ratio of 20%) exhibits the best photocatalytic performance toward MB degradation, which is almost 12.5 and 7.0 times of CN under full spectrum and visible-light irradiation, respectively. In addition, the hybrid exhibits high stability after five successive cycles with no obvious change in efficiency. Unlike pure CNX-NSs, the dye decomposition mostly depends on the H2O2 generation by a two-electron process due to the electron reservoir property of RGO. Thus the enhancement in photocatalytic activity could be ascribed to the improved light utilization and increased charge transfer ability across the interface of CNX-NSs/RGO heterostructure.

Chemical reprogramming provides a powerful platform for exploring the molecular dynamics that lead to pluripotency. Although previous studies have uncovered an intermediate extraembryonic endoderm (XEN)-like state during this process, the molecular underpinnings of pluripotency acquisition remain largely undefined. Here, we profile 36,199 single-cell transcriptomes at multiple time points throughout a highly efficient chemical reprogramming system using RNA-sequencing and reconstruct their progression trajectories. Through identifying sequential molecular events, we reveal that the dynamic early embryonic-like programs are key aspects of successful reprogramming from XEN-like state to pluripotency, including the concomitant transcriptomic signatures of two-cell (2C) embryonic-like and early pluripotency programs and the epigenetic signature of notable genome-wide DNA demethylation. Moreover, via enhancing the 2C-like program by fine-tuning chemical treatment, the reprogramming process is remarkably accelerated. Collectively, our findings offer a high-resolution dissection of cell fate dynamics during chemical reprogramming and shed light on mechanistic insights into the nature of induced pluripotency.

Graphitic carbon nitride (CN) is an emerging photocatalyst with promising prospect, but presently it still falls short on photocatalytic efficiency and photoresponsive range. We herein constructed a novel ternary heterostructure by hybridization of conducting polymer and semiconductor with CN. The exfoliated two dimension CN nanosheets (CN-NSs) are superior to bulk CN as both catalysts and supporting materials. Most recently, there are few reports involving the construction of heterojunction photocatalysts using CN-NSs as supports. The improvement of charge separation efficiency, specific surface area and visible light harvesting is simultaneously achieved in such a novel ternary heterostructure due to the synergetic effect of polyaniline (PANI) and ZnO coupling. As a result, the CN-NS/PANI/ZnO photocatalyst possesses excellent visible photocatalytic performance for MB and 4-CP degradation with a rate constant of 0.026 and 0.0049min(-1), which is about 3.6 and 3.3 times of CN, respectively. The enhanced mechanism is proposed based on the confirmation of OH and h(+) as main oxidative species. Overall, this work can not only yield high-efficient visible photocatalysts but also provide deeper insight into the enhanced mechanisms of CN-NS-based ternary heterostructure.

Leaf Area Index (LAI) is an essential parameter for process-based ecological and climate models. Spectral vegetation indices calculated from remote sensing data are widely used for LAI retrieval at large scales. The applicability of two vegetation indices, namely Simple Ratio (SR) and Reduced Simple Ration (RSR), for retrieving LAI at Maoershan mountain in Heilongjiang province of China was investigated through analyzing the correlations of SR and RSR calculated from Landsat-5 TM data acquired on 24 June, 2009 with LAI measured at 23 typical plots with Li-Cor LAI-2000 during 12 to 20 July, 2009. The fitted model with SR as the predator captured 54.7% of variations of LAI among these 23 plots while the variations of LAI explained by the fitted model with RSR as the predictor increased to 75.4%, indicating the better performance of RSR over SR in retrieving forest LAI in the study area owing to the ability of RSR to reduce the influence of soil background reflectance through the incorporation of the reflectance of shortwave infrared wavelength into SR. LAI derived from models based on SR and RSR was well correlated (R2=0.7219, N=180932). The mean of LAI estimated using the SR-based model was 0.37 larger than that estimated using the RSR-based model for the entire study area. LAI estimated using the former model was smaller than that estimated by the latter model when LAI estimated by the latter was larger than 5.6, indicating that RSR saturates slower than SR under the condition of high LAI. However, RSR is more sensitive to the influence of topography and shadows of clouds than SR.