WATER ENTRY HYDROELASTICITY ANALYSIS OF LATTICE SANDWICH PANEL WITH IMPERFECTION: SIMULATION AND ENGINEERING MODEL

In the present work, the three dimensional (3D) hydroelasticity characteristics of imperfect lattice sandwich panel (ILSP) subjected to water entry via analytical prediction and numerical simulations are proposed. Firstly, numerical investigations are performed on water entry characteristics based on Arbitrary Eulerian-Lagrange (ALE) coupling method for modeling fluid-structure interaction (FSI) at an impact velocity of 5.0m/s. The results show the impact pressure on total FSI surface of ILSP is generally lower than that of the perfect lattice sandwich panel. Then a novel semi-analytical method to calculate the elastic constants of ILSP is introduced. Based on this approach, an engineering computational model is developed to predict the deformation of ILSP, in which the total deformation is separated into two parts; local field deformation and global field deformation. Good agreement between the numerical and analytical results is achieved. And the effects of geometric parameters such as the thickness of face sheet, height of ILSP and relative density of core are discussed

Correlated two-photon imaging with true thermal light

We report the first experimental demonstration of two-photon correlated imaging with true thermal light from a hollow cathode lamp. The coherence time of the source is much shorter than that of previous experiments using random scattered light from a laser. A two-pinhole mask was used as object, and the corresponding thin lens equation was well satisfied. Since thermal light sources are easier to obtain and measure than entangled light it is conceivable that they may be used in special imaging applications.Comment: 3 pages, 5 figures, accepted by Optics Letter

The First Insight into the Tissue Specific Taxus Transcriptome via Illumina Second Generation Sequencing

The First Insight into the Tissue Specific Taxus Transcriptome via Illumina Second Generation SequencingBackground: Illumina second generation sequencing is now an efficient route for generating enormous sequence collections that represent expressed genes and quantitate expression level. Taxus is a world-wide endangered gymnosperm genus and forms an important anti-cancer medicinal resource, but the large and complex genomes of Taxus have hindered the development of genomic resources. The research of its tissue-specific transcriptome is absent. There is also no study concerning the association between the plant transcriptome and metabolome with respect to the plant tissue type

Separation of Normal and Premalignant Cervical Epithelial Cells Using Confocal Light Absorption and Scattering Spectroscopic Microscopy Ex Vivo

Confocal light absorption and scattering spectroscopic (CLASS) microscopy can detect changes in biochemicals and the morphology of cells. It is therefore used to detect high-grade cervical squamous intraepithelial lesion (HSIL) cells in the diagnosis of premalignant cervical lesions. Forty cervical samples from women with abnormal Pap smear test results were collected, and twenty cases were diagnosed as HSIL; the rest were normal or low-grade cervical squamous intraepithelial lesion (LSIL). The enlarged and condensed nuclei of HSIL cells as viewed under CLASS microscopy were much brighter and bigger than those of non-HSIL cells. Cytological elastic scattered light data was then collected at wavelengths between 400 and 1000 nm. Between 600 nm to 800 nm, the relative elastic scattered light intensity of HSIL cells was higher than that of the non-HSIL. Relative intensity peaks occurred at 700 nm and 800 nm. CLASS sensitivity and specificity results for HSIL and non-HSIL compared to cytology diagnoses were 80% and 90%, respectively. This study demonstrated that CLASS microscopy could effectively detect cervical precancerous lesions. Further study will verify this conclusion before the method is used in clinic for early detection of cervical cancer

African Journal of Biotechnology

In this study, we aimed to evaluate the global scientific production of Taxus (yew) research, study the characteristics of Taxus research activities, and identify patterns, tendencies, and regularities of Taxusrelated articles. Data were based on the online version of Science Citation Index Expanded (SCIExpanded), from the Web of Science database. Articles referring to Taxus were assessed by the trend of publication output during 1991 to 2010. Globally, 2,916 papers were published during the 20-year study period. The most productive countries, institutions, Web of Science subject categories, and journals, as well as the most cited articles, were identified. The mainstream research on Taxus was in the plant sciences, biochemistry and molecular biology, cardiac and cardiovascular systems, biotechnology and applied microbiology. The G7 industrial countries, as well as China and India held the majority of total world production. Research on the various economically important Taxus species remained the hotspot during the 20-year study period, whereas that on the related topic "paclitaxel eluting stents" increased dramatically since 2002. With synthetic analysis of word in article title, author keyword, abstract, and key words Plus, it can be concluded that application of compounds derived from Taxus in clinical cardiology, pharmacology and oncology, and research related to Taxus chemistry, metabolism, cytology and microbiology is the ongoing Taxus-related research in the 21st century. Gaps are present in knowledge about the genomics, epigenomics, transcriptomics, proteomics, metabolomics and bioinformatics of Taxus and their endophytic fungi

Building Interpretable Interaction Trees for Deep NLP Models

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley values of words, which are considered as an unbiased estimation of word contributions to the network prediction. Our method is used to quantify word interactions encoded inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental results have provided a new perspective to understand these DNNs, and have demonstrated the effectiveness of our method