Raman spectroscopy and advanced mathematical modelling in the discrimination of human thyroid cell lines

Raman spectroscopy could offer non-invasive, rapid and an objective nature to cancer diagnostics. However, much work in this field has focused on resolving differences between cancerous and non-cancerous tissues, and lacks the reproducibility and interpretation to be put into clinical practice. Much work is needed on basic cellular differences between malignancy and normal. This would allow the establishment of a clinically relevant cellular based model to translate to tissue classification. Raman spectroscopy provides a very detailed biochemical analysis of the target material and to 'unlock' this potential requires sophisticated mathematical modelling such as neural networks as an adjunct to data interpretation. Commercially obtained cancerous and non-cancerous cells, cultured in the laboratory were used in Raman spectral measurements. Data trends were visualised through PCA and then subjected to neural network analysis based on self-organising maps; consisting of m maps, where m is the number of classes to be recognised. Each map approximates the statistical distribution of a given class. The neural network analysis provided a 95% accuracy for identification of the cancerous cell line and 92% accuracy for normal cell line. In this preliminay study we have demonstrated th ability to distinguish between "normal" and cancerous commercial cell lines. This encourages future work to establish the reasons underpinning these spectral differences and to move forward to more complex systems involving tissues. We have also shown that the use of sophisticated mathematical modelling allows a high degree of discrimination of 'raw' spectral data

The Survey of H5N1 Flu Virus in Wild Birds in 14 Provinces of China from 2004 to 2007

The highly pathogenic H5N1 avian influenza emerged in the year 1996 in Asia, and has spread to Europe and Africa recently. At present, effective monitoring and data analysis of H5N1 are not sufficient in Chinese mainland.)) were obviously higher than those in other 13 provinces. The results of sequence analysis indicated that the 17 strains isolated from wild birds were distributed in five clades (2.3.1, 2.2, 2.5, 6, and 7), which suggested that genetic diversity existed among H5N1 viruses isolated from wild birds. The five isolates from Qinghai came from one clade (2.2) and had a short evolutionary distance with the isolates obtained from Qinghai in the year 2005.We have measured the prevalence of H5N1 virus in 56 species of wild birds in 14 provinces of China. Continuous monitoring in the field should be carried out to know whether H5N1 virus can be maintained by wild birds

The Arabidopsis thaliana F-box gene HAWAIIAN SKIRT is a new player in the microRNA pathway

In Arabidopsis, the F-box HAWAIIAN SKIRT (HWS) protein is important for organ growth. Loss of function of HWS exhibits pleiotropic phenotypes including sepal fusion. To dissect the HWS role, we EMS-mutagenized hws-1 seeds and screened for mutations that suppress hws-1 associated phenotypes. We identified shs-2 and shs-3 (suppressor of hws-2 and 3) mutants in which the sepal fusion phenotype of hws-1 was suppressed. shs-2 and shs-3 (renamed hst-23/hws-1 and hst-24/hws-1) carry transition mutations that result in premature terminations in the plant homolog of Exportin-5 HASTY (HST), known to be important in miRNA biogenesis, function and transport. Genetic crosses between hws-1 and mutant lines for genes in the miRNA pathway, also suppress the phenotypes associated with HWS loss of function, corroborating epistatic relations between the miRNA pathway genes and HWS. In agreement with these data, accumulation of miRNA is modified in HWS loss or gain of function mutants. Our data propose HWS as a new player in the miRNA pathway, important for plant growth

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

Quantum-inspired particle swarm optimization algorithm with performance evaluation of fused images

In order to improve and accelerate the speed of image integration, an optimal and intelligent method for multi-focus image fusion is presented in this paper. Based on particle swarm optimization and quantum theory, quantum particle swarm optimization (QPSO) intelligent search strategy is introduced in salience analysis of a contrast visual masking system, combined with the segmentation technique. The superiority of QPSO is quantum parallelism. It has stronger search ability and quicker convergence speed. When compared with other classical or novel fusion methods, several metrics for image definition are exploited to evaluate the performance of all the adopted methods objectively. Experiments are performed on both artificial multi-focus images and digital camera multi-focus images. The results show that QPSO algorithm is more efficient than non-subsampled contourlet transform, genetic algorithm, binary particle swarm optimization, etc. The simulation results demonstrate that QPSO is a satisfying image fusion method with high accuracy and high speed