Fabrication and Optical Behaviors of Core–Shell ZnS Nanostructures

Novel core–shell nanostructures comprised of cubic sphalerite and hexagonal wurtzite ZnS have been synthesized at 150°C by a simple hydrothermal method. The results of HR-TEM and SAED investigation reveal that the cores of hexagonal wurtzite ZnS (ca. 200 nm in average diameter) are encapsulated by a shell of cubic sphalerite ZnS. The FE-SEM image of the nanomaterials shows a surface tightly packed with nanoparticles (<10 nm in size). The optical properties of the fabricated material have been studied in terms of ultraviolet–visible absorption and photoluminescence. Furthermore, a possible mechanism for the fabrication of the core–shell nanostructures has been presented

Controllable Synthesis of Single-Crystalline CdO and Cd(OH)2Nanowires by a Simple Hydrothermal Approach

Single-crystalline Cd(OH)2 or CdO nanowires can be selectively synthesized at 150 °C by a simple hydrothermal method using aqueous Cd(NO3)2 as precursor. The method is biosafe, and compared to the conventional oil-water surfactant approach, more environmental-benign. As revealed by the XRD results, CdO or Cd(OH)2 nanowires can be generated in high purity by varying the time of synthesis. The results of FESEM and HRTEM analysis show that the CdO nanowires are formed in bundles. Over the CdO-nanowire bundles, photoluminescence at ~517 nm attributable to near band-edge emission of CdO was recorded. Based on the experimental results, a possible growth mechanism of the products is proposed

Risk evaluation of abdominal aortic aneurysms based on both sex and morphology

Aim: To predict the peak wall stress in abdominal aortic aneurysms (AAAs) considering both morphological factors (maximum diameter, asymmetry index, and wall thickness) and sex differences, in order to assess the risk of AAA rupture more accurately.Methods: Basic models of AAA focusing on different sexes with a range of morphological parameters were constructed. Using the Design-expert software for three-factor response surface methodology, 20 experimental models were built as well with the SolidWorks software. Fluid-structure interaction analysis was used to obtain stress distribution along the AAA wall. Polynomial regression equations were fitted to peak stresses in all experimental models.Results: Based on fluid-structure interaction simulation data in the nonlinear polynomial regression model, separate equations for peak wall stress in AAA with regard to males and females were obtained. Morphological factors and sex differences have significant influence on peak wall stress. In some models, even when the maximum AAA diameter was relatively small, the peak wall stress became high. For the same maximal transverse measurement, when the AAA wall was thin and the asymmetry index large, or the former was thick and the latter small, the peak wall stress observed in males was higher than that in females.Conclusion: To evaluate the risk of rupture of AAA more precisely and specifically, the present study proposes a new prediction method (based on equations) that includes more indicators such as sex and morphology, based on numerical biomechanical simulations, which were confirmed as such. This study provides a sex-specific clinical reference to assess the aforementioned risk of AAA rupture

Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases.

Rheumatic diseases have some common symptoms. Extensive gene expression studies, accumulated thus far, have successfully identified signature molecules for each rheumatic disease, individually. However, whether there exist shared factors across rheumatic diseases has yet to be tested.We collected and utilized 6 public microarray datasets covering 4 types of representative rheumatic diseases including rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and osteoarthritis. Then we detected overlaps of differentially expressed genes across datasets and performed a meta-analysis aiming at identifying common differentially expressed genes that discriminate between pathological cases and normal controls. To further gain insights into the functions of the identified common differentially expressed genes, we conducted gene ontology enrichment analysis and protein-protein interaction analysis.We identified a total of eight differentially expressed genes (TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, PRF1), each associated with at least 3 of the 4 studied rheumatic diseases. Meta-analysis warranted the significance of the eight genes and highlighted the general significance of four genes (CX3CR1, LY96, TLR5, and PRF1). Protein-protein interaction and gene ontology enrichment analyses indicated that the eight genes interact with each other to exert functions related to immune response and immune regulation.The findings support that there exist common factors underlying rheumatic diseases. For rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and osteoarthritis diseases, those common factors include TNFSF10, CX3CR1, LY96, TLR5, TXN, TIA1, PRKCH, and PRF1. In-depth studies on these common factors may provide keys to understanding the pathogenesis and developing intervention strategies for rheumatic diseases

A Two-Port Measurement with Mechanically Robust Handhold Probes for Ultra Low PDN Impedance

Measuring the input impedance of a power distribution network of a high-speed printed circuit board is challenging because the port is defined on multiple vias, and the impedance can be below a milliohm. A mechanically robust probe, and a specially designed, low-inductance probe landing pad are designed to measure the power distrubtion network impedance. The measurement is based on a two-port measurement method, and the mutual inductance of the port, which is the main source of error of the method, is characterized. Comparison between measurement and full-wave simulation shows that the presented probes and fixtures are suitable for measuring impedance in the sub-milliohm range

Flowcharts of Data Preparation and Data Analyses.

<p>(A) The selection process of microarray datasets. (B) The analysis process of the microarray datasets.</p

Disease Heatmap Based on Gene Expression Variation Profiles.

<p>This diagram shows correlations between gene expression variation profiles of various rheumatic diseases. (A) Hierarchical cluster with Kendall correlation based on the whole gene expression variation profile; (B) Hierarchical cluster with Spearman correlation based on the whole gene expression variation profile; (C) Hierarchical cluster with Kendall correlation based on the eight common genes; (D) Hierarchical cluster with Spearman correlation based on the eight common genes. Positive and negative correlations between pairs of diseases are shown in blue and pink, respectively.</p