Effect of milling time on microstructure, crystallite size and dielectric properties of Srtio3 ceramic synthesized via mechanical alloying method

SrTiO3 sample has been successfully prepared by mechanical alloying (MA) method. The effect of milling time on microstructure, crystallite size and dielectric properties of SrTiO3 were studied. The results revealed that the mean crystallite size of milled powders decreased from 84.56 to 12.87 nm with increasing milling time. However, the average lattice strain of milled powders increased from 0.2 to 0.93% with increasing milling time. A single phase SrTiO3 could not be formed with milling alone and required annealing process. A transformation of anatase-TiO2 to rutile-TiO2 was observed at 16 h of milling. After the milled powders were subjected to sintering process at 1200°C, formation of single-phase SrTiO3-type cubic (Pm-3m) perovskite structure was observed. The peak intensities of the sintered SrTiO3 samples decreased as the milling time was increased. For microstructural observations, the average grain size of the sintered SrTiO3 sample milled for 8 h showed the largest. For dielectric measurements, the dielectric constant of the sintered SrTiO3 sample milled for 8 h showed the highest among others. This could be due to the largest grain size obtained for sintered SrTiO3 sample milled for 8 h. The decrease in the grain size with increasing milling time resulted to the decrease in dielectric constant

Managing inter-agency co-ordination : an analysis of district level administration in Hong Kong

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools

The use of tibial Less Invasive Stabilization System (LISS) plate [AO-ASIF] for the treatment of paediatric supracondylar fracture of femur: a case report

Paediatric supracondylar fractures of the femur are not common. The treatment options depend on the age of child, the site of the fracture, the pattern of injury and the surgeon's preference. We report a case of an 11-year old boy who sustained a comminuted displaced supracondylar fracture of the femur and was treated with indirect reduction and internal fixation with the Less Invasive Stabilization System (LISS) tibial plate

Clinical significance of SOX9 in human non-small cell lung cancer progression and overall patient survival

<p>Abstract</p> <p>Background</p> <p>Sex determining region Y (SRY)-related high mobility groupbox 9 (SOX9) is an important transcription factor required for development, which regulates the expression of target genes in the associated pathway. The aim of this study was to describe the expression of SOX9 in human non-small cell lung cancer (NSCLC) and to investigate the association between SOX9 expression and progression of NSCLC.</p> <p>Methods</p> <p>SOX9 protein and mRNA expression in normal human pneumonocytes, lung cancer cell lines, and eight pairs of matched lung cancer tissues and their adjacent normal lung tissues were detected by Western blotting and real-time reverse transcription-polymerase chain reaction (RT-PCR). Immunohistochemistry was used to determine SOX9 protein expression in 142 cases of histologically characterized NSCLC. Statistical analyses were applied to test for prognostic and diagnostic associations.</p> <p>Results</p> <p>SOX9 in lung cancer cell lines was upregulated at both mRNA and protein levels, and SOX9 mRNA and protein were also elevated in NSCLC tissues compared with levels in corresponding adjacent non-cancerous lung tissues. Immunohistochemical analysis demonstrated a high expression of SOX9 in 74/142 (52.1%) paraffin-embedded archival lung cancer biopsies. Statistical analysis indicated that upregulation of SOX9 was significantly correlated with the histological stage of NSCLC (<it>P </it>= 0.017) and that patients with a high SOX9 level exhibited a shorter survival time (<it>P </it>< 0.001). Multivariate analysis illustrated that SOX9 upregulation might be an independent prognostic indicator for the survival of patients with NSCLC.</p> <p>Conclusions</p> <p>This work shows that SOX9 may serve as a novel and prognostic marker for NSCLC, and play a role during the development and progression of the disease.</p

A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling, RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show that RotEasy possesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria