Gene therapy and viral vaccine are becoming attractive therapeutic options for the treatment of different malignant diseases. Viral vector productions are often using static culture vessels and small volume stainless steel bioreactors (SSB). However, the yield of each vessel can be relatively low and multiple vessels often need to be operated simultaneously. This significantly increases labor intensity, production costs, contamination risks, and limits its ability to be scaled up, thus, creating challenges to meet the quantities required once the gene therapy or viral vaccine medicine goes into clinical phases or to market. Single-use bioreactor combining with microcarrier provides a good option for viral vector and vaccine production. The goal of the present studies was to develop the microcarrier bead-to-bead expansion and transfer process for HEK293T cells and Vero cells and scale-up the cultures to 50–200 l single-use bioreactors. Following microcarrier bead-to-bead transfer, the peak cell concentration of HEK293T cells reached 1.5 × 10 6 cells/ml in XDR-50 bioreactor, whereas Vero cells reached 3.1 × 10 6 cells/ml and 3.3 × 10 6 cells/ml in XDR-50 bioreactor and XDR-200 bioreactor, respectively. The average growth rates reached 0.61–0.68/day. The successful microcarrier-based scaleup of these two cell lines in single-use bioreactors demonstrates potential large-scale production capabilities of viral vaccine and vector for current and future vaccines and gene therapy.
A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.
To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index (NDVI) time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil-Sen median trend analysis and the Mann-Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000-2005 than in 2006-2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20-30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive: regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.
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