Use of system identification techniques for improving airframe finite element models using test data

A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory

Use of system identification techniques for improving airframe finite element models using test data

A method for using system identification techniques to improve airframe finite element models using test data was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in the total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all of the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory

Hierarchical accompanying and inhibiting patterns on the spatial arrangement of taxis' local hotspots

Due to the large volume of recording, the complete spontaneity, and the flexible pick-up and drop-off locations, taxi data portrays a realistic and detailed picture of urban space use to a certain extent. The spatial arrangement of pick-up and drop-off hotspots reflects the organizational space, which has received attention in urban structure studies. Previous studies mainly explore the hotspots at a large scale by visual analysis or some simple indexes, where the hotspots usually cover the entire central business district, train stations, or dense residential areas, reaching a radius of hundreds or even thousands of meters. However, the spatial arrangement patterns of small-scale hotspots, reflecting the specific popular pick-up and drop-off locations, have not received much attention. Using two taxi trajectory datasets in Wuhan and Beijing, China, this study quantitatively explores the spatial arrangement of fine-grained pick-up and drop-off local hotspots with different levels of popularity, where the sizes are adaptively set as 90m*90m in Wuhan and 105m*105m in Beijing according to the local hotspot identification method. Results show that popular hotspots tend to be surrounded by less popular hotspots, but the existence of less popular hotspots is inhibited in regions with a large number of popular hotspots. We use the terms hierarchical accompany and inhibiting patterns for these two spatial configurations. Finally, to uncover the underlying mechanism, a KNN-based model is proposed to reproduce the spatial distribution of other less popular hotspots according to the most popular ones. These findings help decision-makers construct reasonable urban minimum units for precise traffic and disease control, as well as plan a more humane spatial arrangement of points of interest

Nanoparticle Delivery of miR-34a Eradicates Long-Term-Cultured Breast Cancer Stem Cells via Targeting C22ORF28 Directly

Rationale: Cancer stem cells (CSCs) have been implicated as the seeds of therapeutic resistance and metastasis, due to their unique abilities of self-renew, wide differentiation potentials and resistance to most conventional therapies. It is a proactive strategy for cancer therapy to eradicate CSCs. Methods: Tumor tissue-derived breast CSCs (BCSC), including XM322 and XM607, were isolated by fluorescence-activated cell sorting (FACS); while cell line-derived BCSC, including MDA-MB-231.SC and MCF-7.SC, were purified by magnetic-activated cell sorting (MACS). Analyses of microRNA and mRNA expression array profiles were performed in multiple breast cell lines. The mentioned nanoparticles were constructed following the standard molecular cloning protocol. Tissue microarray analysis has been used to study 217 cases of clinical breast cancer specimens. Results: Here, we have successfully established four long-term maintenance BCSC that retain their tumor-initiating biological properties. Our analyses of microarray and qRT-PCR explored that miR-34a is the most pronounced microRNA for investigation of BCSC. We establish hTERT promoter-driven VISA delivery of miR-34a (TV-miR-34a) plasmid that can induce high throughput of miR-34a expression in BCSC. TV-miR-34a significantly inhibited the tumor-initiating properties of long-term-cultured BCSC in vitro and reduced the proliferation of BCSC in vivo by an efficient and safe way. TV-miR-34a synergizes with docetaxel, a standard therapy for invasive breast cancer, to act as a BCSC inhibitor. Further mechanistic investigation indicates that TV-miR-34a directly prevents C22ORF28 accumulation, which abrogates clonogenicity and tumor growth and correlates with low miR-34 and high C22ORF28 levels in breast cancer patients. Conclusion: Taken together, we generated four long-term maintenance BCSC derived from either clinical specimens or cell lines, which would be greatly beneficial to the research progress in breast cancer patients. We further developed the non-viral TV-miR-34a plasmid, which has a great potential to be applied as a clinical application for breast cancer therapy

Strong [O III] {\lambda}5007 Compact Galaxies Identified from SDSS DR16 and Their Scaling Relations

Green pea galaxies are a special class of star-forming compact galaxies with strong [O III]{\lambda}5007 and considered as analogs of high-redshift Ly{\alpha}-emitting galaxies and potential sources for cosmic reionization. In this paper, we identify 76 strong [O III]{\lambda}5007 compact galaxies at z < 0.35 from DR1613 of the Sloan Digital Sky Survey. These galaxies present relatively low stellar mass, high star formation rate, and low metallicity. Both star-forming main sequence relation (SFMS) and mass-metallicity relation (MZR) are investigated and compared with green pea and blueberry galaxies collected from literature. It is found that our strong [O III] {\lambda}5007 compact galaxies share common properties with those compact galaxies with extreme star formation and show distinct scaling relations in respect to those of normal star-forming galaxies at the same redshift. The slope of SFMS is higher, indicates that strong [O III]{\lambda}5007 compact galaxies might grow faster in stellar mass. The lower MZR implies that they may be less chemically evolved and hence on the early stage of star formation. A further environmental investigation confirms that they inhabit relatively low-density regions. Future largescale spectroscopic surveys will provide more details on their physical origin and evolution.Comment: 12 pages, 8 figures, 1 table. Published in A

Instability Mechanism of Osimertinib in Plasma and a Solving Strategy in the Pharmacokinetics Study

Osimertinib is a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and a star medication used to treat non-small-cell lung carcinomas (NSCLCs). It has caused broad public concern that osimertinib has relatively low stability in plasma. We explored why osimertinib and its primary metabolites AZ-5104 and AZ-7550 are unstable in rat plasma. Our results suggested that it is the main reason inducing their unstable phenomenon that the Michael addition reaction was putatively produced between the Michael acceptor of osimertinib and the cysteine in the plasma matrix. Consequently, we identified a method to stabilize osimertinib and its metabolite contents in plasma. The assay was observed to enhance the stability of osimertinib, AZ-5104, and AZ-7550 significantly. The validated method was subsequently applied to perform the pharmacokinetic study for osimertinib in rats with the newly established, elegant, and optimized ultra-performance liquid chromatography–tandem mass spectrometer (UPLC-MS/MS) strategy. The assay was assessed for accuracy, precision, matrix effects, recovery, and stability. This study can help understand the pharmacological effects of osimertinib and promote a solution for the similar problem of other Michael acceptor-contained third-generation EGFR-TKI

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

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