Preparation and Microwave-Absorbing Properties of FeCo Alloys by Condensation Reflux Method.

Five groups of FeCo alloy samples with different atomic ratios of Fe/Co (3:7, 4:6, 5:5, 6:4, 7:3) were prepared using the condensation reflux method. The results indicate that varying the atomic ratios of Fe/Co has a significant impact on the microstructure, electromagnetic parameters, and microwave absorption properties of FeCo alloys. As the Fe atom content increases, the morphology of the FeCo alloys transitions from irregular flower-shaped to uniformly spherical and eventually to lamellar. The attenuation of electromagnetic waves in the five groups of alloys is primarily due to magnetic loss. Among them, Fe6Co4 exhibits the best absorption performance, with a minimum reflection loss (RL) value of -35.56 dB at a frequency of 10.40 GHz when the matching thickness is 7.90 mm. Additionally, at a matching thickness of 5.11 mm, the maximum effective absorption bandwidth (EAB) reached 2.56 GHz (15.44-18 GHz).

Exploring the Cocktail Factor Approach to Generate Salivary Gland Progenitors through Co-Culture Techniques.

BACKGROUND: The derivation of salivary gland (SG) progenitors from pluripotent stem cells (PSCs) presents significant potential for developmental biology and regenerative medicine. However, the existing protocols for inducing SG include limited factors, making it challenging to mimic the in vivo microenvironment of embryonic SGs. METHODS: We reported a cocktail factor approach to promote the differentiation of mouse embryonic stem cell (mESC)-derived oral epithelium (OE) into SG progenitors through a three-dimensional co-culture method. Upon confirming that the embryonic SG can promote the differentiation of mESC-derived OE, we performed RNA sequence analysis to identify factors involved in the differentiation of SG progenitors. RESULTS: Our findings highlight several efficient pathways related to SG development, with frequent appearances of four factors: IFN-γ, TGF-ß2, EGF, and IGF-1. The combined treatment using these cocktail factors increased the expression of key SG progenitor markers, including Sox9, Sox10, Krt5, and Krt14. However, absence of any one of these cocktail factors did not facilitate differentiation. Notably, aggregates treated with the cocktail factor formed SG epithelial-like structures and pre-bud-like structures on the surface. CONCLUSION: In conclusion, this study offers a novel approach to developing a differentiation protocol that closely mimics the in vivo microenvironment of embryonic SGs. This provides a foundation for generating PSC-derived organoids with near-physiological cell behaviors and structures.

Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.