Development and Validation of a HPLC-UV Method with Pre-column Derivatization for Determination of Cinnabar in Jufang Zhibao Pills

In this work, a reliable and accurate high-performance liquid chromatography method with pre-column derivatization was established and validated for determination of cinnabar in Jufang Zhibao pills. Scanning electron microscope (SEM) image was used to identify the types of cinnabar crude drug in Jufang Zhibao pills. The chromatography separation was performed on a Welch XB-C18 column (250 mm × 4.6 mm, 5 μm). The mobile phase consists of water spiked with 0.022 mmol/L sodium diethyldithiocarbamate (A, pH adjusted to 8–9 by ammonia water) and methanol (B, 80:20, v/v) at flow rate of 1.0 ml/min with the detected wavelength was 272 nm. The oven temperature was set at 35°C. The calibration for cinnabar content has good linearity (R2 =0.9999) over the range of 2.43–300 μg/ml and the average recovery was less then 1.90%. The limits of detection and quantification were 0.1127 μg and 0.2065 μg/ml. The results indicated that the proposed method has advantages of high accuracy, good repeatability and stability and can be successfully used for determination of cinnabar in Jufang Zhibao pills. It provides a basis for drug manufacture quality control and proves the feasibility of the pre-column derivatization method during the determination of cinnabar in Jufang Zhibao pills

Defining the role of oxygen tension in human neural progenitor fate.

Hypoxia augments human embryonic stem cell (hESC) self-renewal via hypoxia-inducible factor 2α-activated OCT4 transcription. Hypoxia also increases the efficiency of reprogramming differentiated cells to a pluripotent-like state. Combined, these findings suggest that low O2 tension would impair the purposeful differentiation of pluripotent stem cells. Here, we show that low O2 tension and hypoxia-inducible factor (HIF) activity instead promote appropriate hESC differentiation. Through gain- and loss-of-function studies, we implicate O2 tension as a modifier of a key cell fate decision, namely whether neural progenitors differentiate toward neurons or glia. Furthermore, our data show that even transient changes in O2 concentration can affect cell fate through HIF by regulating the activity of MYC, a regulator of LIN28/let-7 that is critical for fate decisions in the neural lineage. We also identify key small molecules that can take advantage of this pathway to quickly and efficiently promote the development of mature cell types

Thermally reduced graphene oxide/carbon nanotube composite films for thermal packaging applications

Thermally reduced graphene oxide/carbon nanotube (rGO/CNT) composite films were successfully prepared by a high-temperature annealing process. Their microstructure, thermal conductivity and mechanical properties were systematically studied at different annealing temperatures. As the annealing temperature increased, more oxygen-containing functional groups were removed from the composite film, and the percentage of graphene continuously increased. When the annealing temperature increased from 1100 to 1400 \ub0C, the thermal conductivity of the composite film also continuously increased from 673.9 to 1052.1 W m-1 K-1. Additionally, the Young\u27s modulus was reduced by 63.6%, and the tensile strength was increased by 81.7%. In addition, the introduction of carbon nanotubes provided through-plane thermal conduction pathways for the composite films, which was beneficial for the improvement of their through-plane thermal conductivity. Furthermore, CNTs apparently improved the mechanical properties of rGO/CNT composite films. Compared with the rGO film, 1 wt% CNTs reduced the Young\u27s modulus by 93.3% and increased the tensile strength of the rGO/CNT composite film by 60.3%, which could greatly improve its flexibility. Therefore, the rGO/CNT composite films show great potential for application as thermal interface materials (TIMs) due to their high in-plane thermal conductivity and good mechanical properties

SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models

Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this, this paper introduces SmartEdit, a novel approach to instruction-based image editing that leverages Multimodal Large Language Models (MLLMs) to enhance their understanding and reasoning capabilities. However, direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this, we propose a Bidirectional Interaction Module that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training, we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently, we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset, Reason-Edit, specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.Comment: Project page: https://yuzhou914.github.io/SmartEdit