Synthesis of Mg(OH)2, MgO, and Mg nanoparticles using laser ablation of magnesium in water and solvents

laser ablation of magnesium in deionized water (OW), solutions of OW and sodium dodecyl sulfate (50S) with different concentrations, acetone and 2-propanol has been conducted, The results showed that ablation in acetone and 2-propanol yielded MgO and Mg nanocrystallites as isolated particles and agglomerated chains probably intermixed with organic residues resulting from the alterationj decomposition of the solvents under the high-energy conditions. Brucite-like Mg(OH)2 particles were mainly produced by laser ablation of Mg in either OW or OW~SOS solutions. Ablation in OW yielded particles of fiber-like shapes having a diameter of about 5-lOnm and length as long as 150nm. Materials produced in DW-SOS solutions were composed of various size and shape particles, Some had rough surfaces with irregular shapes. Small particles were about 20-30nm and larger particles were about 120 nm. Particles with rod-like, triangular, and plate-like shapes were also observed

MUC1 epithelial mucin (CD227) is expressed by activated dendritic cells

The MUC1 mucin (CD227) is a cell surface mucin originally thought to be restricted to epithelial tissues. We report that CD227 is expressed on human blood dendritic cells (DC) and monocyte-derived DC following in vitro activation. Freshly isolated murine splenic DC had very low levels of CD227; however, all DC expressed CD227 following in vitro culture. In the mouse spleen, CD227 was seen on clusters within the red pulp and surrounding the marginal zone in the white pulp. Additionally, we confirm CD227 expression by activated human T cells and show for the first time that the CD227 cytoplasmic domain is tyrosine-phosphorylated in activated T cells and DC and is associated with other phosphoproteins, indicating a role in signaling. The function of CD227 on DC and T cells requires further elucidation

Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine

Efficient management of chronic diseases is critical in modern health care. We consider diabetes mellitus, and our ongoing goal is to examine how machine learning can deliver information for clinical efficiency. The challenge is to aggregate highly heterogeneous sources including demographics, diagnoses, pathologies and treatments, and extract similar groups so that care plans can be designed. To this end, we extend our recent model, the mixed-variate restricted Boltzmann machine (MV.RBM), as it seamlessly integrates multiple data types for each patient aggregated over time and outputs a homogeneous representation called "latent profile" that can be used for patient clustering, visualisation, disease correlation analysis and prediction. We demonstrate that the method outperforms all baselines on these tasks - the primary characteristics of patients in the same groups are able to be identified and the good result can be achieved for the diagnosis codes prediction