Implications of MDCK cell heterogeneity in cell-based influenza vaccine production

Influenza is a global public health issue that causes serious illness with high mortality rate. Currently, Madin-Darby canine kidney (MDCK) cell culture-based influenza vaccine production moving up to the front as an inexorable trend for the substitution of egg-based vaccine production, owing to its high degree of flexibility and scalability. However, MDCK cells are a continuous cell line and comprise a heterogeneous pool of non-clonal cells that differ in morphological as well as functional features in influenza virus production. The impurity of cell population may lead to fugacious tendency in virus production, and long-term culture may bring potential risk of unstable viral production or vaccine quality as cells in MDCK subclonal population may encounter unexpected manifestation of chromosomal rearrangement, loss of the virus susceptibility, or reduction of the virus partials packaging capability during the culture. Although many details of the influenza virus life cycle have already been unraveled, little is known about the ability of subclones in virus infection, intracellular replication, and virus release during viral vaccine production process. With the widely utilizing of omics-based approaches and progressively accumulating of omics database, transcriptome profile analysis will be a powerful strategy to explore the mechanism of cell heterogeneity, providing great significance for the development of robust virus producing cell line and robust virus production process. This work aims to explore a deeper understanding on the MDCK cell heterogeneity used in influenza virus production. For this purpose, a MDCK cell line that has been extensively used in industrial production was subcloned and examined for the influenza virus productivity. The virus productivity spread over a wide range of more than 300-fold among different clones, which revealed large variations in their ability to produce progeny viruses. The high and low producer as well as parent cell population were expanded to explore the intracellular virus propagation process, and the expression levels of all the annotated genes were quantified across the different subclones using RNA-seq. The RT-qPCR results showed that the influenza virus RNA synthesis and virus release differed dramatically among subclones during a synchronized single-cycle infection. Pathway analysis performed on the genes indicated that most of the genes are not differentially expressed, but a few key cellular metabolic pathways are differentially expressed among the subclones, especially the genes related to the virus infection, replication and release. These results spurs further hypothesis to improve our mechanistic understanding of cell line stability and virus propagation process, which will have significant impact on rationalizing cell line development of viral vaccine producing mammalian cells

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

Cooperative Spectrum Sensing Algorithm to Overcome Noise Fluctuations Based on Energy Detection in Sensing Systems

In sensing systems, nodes must be able to rapidly detect whether a signal from a primary transmitter is present in a certain spectrum. However, traditional energy-detection algorithms are poorly adapted to treating noisy signals. In this paper, we investigate how rapid energy detection and detection sensitivity are related to detection duration and average power fluctuation in noise. The results indicate that detection performance and detection sensitivity decrease quickly with increasing average power fluctuation in noise and are worse in situations with low signal-to-noise ratio. First, we present a dynamic threshold algorithm based on energy detection to suppress the influence of noise fluctuation and improve the sensing sensitivity. Then, we present a new energy-detection algorithm based on cooperation between nodes. Simulations show that the proposed scheme improves the resistance to average power fluctuation in noise for short detection timescales and provides sensitive detection that improves with increasing numbers of cooperative detectors. In other words, the proposed scheme enhances the ability to overcome noise and improves spectrum sensing performance

Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

Drought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China

Winter remote sensing images are more suitable for forest mapping in Jiangxi Province

ABSTRACTJiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research

Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model

Freestanding Mo3N2 nanotubes for long‐term stabilized 2e− intermediate‐based high energy efficiency Li–CO2 batteries

Abstract Li–CO2 batteries are considered one of the promising power sources owing to ultrahigh energy density and carbon fixation. Nevertheless, the sluggish reaction kinetics of 4e− discharged process (Li2CO3) impede its potential application. One of the efficient strategies for developing cathode catalysts is to stabilize 2e− intermediate Li2C2O4 and improve reaction reversibility. However, long‐term catalysts of stabilized Li2C2O4 are barely achieved, whereas cycle stability is far from satisfactory level. Herein, non‐noble metal–based Mo3N2 is synthesized and employed as freestanding cathodes for Li–CO2 batteries. Owing to rich delocalized electrons of Mo2+ and reversible electron localization structure, freestanding Mo3N2 cathodes exhibit a low charge potential (3.28 V) with an ultralow potential gap (0.64 V), high energy efficiency of up to 80.46%, fast rate capability, and outstanding cycle stability (>910 h). In situ experiments and theoretical calculation verify that Mo3N2 stabilizes 2e− Li2C2O4 intermediate by the interaction of Mo2+ as active sites where Mo2+ promotes the transfer of outer electrons to O, prevents its disproportionation to Li2CO3, and promotes reaction kinetics, contributing to high energy efficiency and outstanding cycle reversibility. In addition, the pouch‐cells deliver ultrahigh energy density of up to 6350.7 W h kg−1 based on the mass of cathode materials

Interactive effects of increased temperature, elevated pCO2 and different nitrogen sources on the coccolithophore Gephyrocapsaoceanica.

As a widespread phytoplankton species, the coccolithophore Gephyrocapsaoceanica has a significant impact on the global biogeochemical cycle through calcium carbonate precipitation and photosynthesis. As global change continues, marine phytoplankton will experience alterations in multiple parameters, including temperature, pH, CO2, and nitrogen sources, and the interactive effects of these variables should be examined to understand how marine organisms will respond to global change. Here, we show that the specific growth rate of G. oceanica is reduced by elevated CO2 (1000 μatm) in [Formula: see text]-grown cells, while it is increased by high CO2 in [Formula: see text]-grown ones. This difference was related to intracellular metabolic regulation, with decreased cellular particulate organic carbon and particulate organic nitrogen (PON) content in the [Formula: see text] and high CO2 condition compared to the low CO2 condition. In contrast, no significant difference was found between the high and low CO2 levels in [Formula: see text] cultures (p > 0.05). The temperature increase from 20°C to 25°C increased the PON production rate, and the enhancement was more prominent in [Formula: see text] cultures. Enhanced or inhibited particulate inorganic carbon production rate in cells supplied with [Formula: see text] relative to [Formula: see text] was observed, depending on the temperature and CO2 condition. These results suggest that a greater disruption of the organic carbon pump can be expected in response to the combined effects of increased [Formula: see text]/[Formula: see text] ratio, temperature, and CO2 level in the oceans of the future. Additional experiments conducted under nutrient limitation conditions are needed before we can extrapolate our findings to the global oceans

Pine polyphenols from Pinus koraiensis prevent injuries induced by gamma radiation in mice

Pine polyphenols (PPs) are bioactive dietary constituents that enhance health and help prevent diseases through antioxidants. Antioxidants reduce the level of oxidative damages caused by ionizing radiation (IR). The main purpose of this paper is to study the protective effect of PPs on peripheral blood, liver and spleen injuries in mice induced by IR. ICR (Institute of Cancer Research) male mice were administered orally with PPs (200 mg/kg b.wt.) once daily for 14 consecutive days prior to 7 Gy γ-radiations. PPs showed strong antioxidant activities. PPs significantly increased white blood cells, red blood cells and platelets counts. PPs also significantly reduced lipid peroxidation and increased the activities of superoxide dismutase, catalase and glutathione peroxidases, and the level of glutathione. PPs reduced the spleen morphologic injury. In addition, PPs inhibited mitochondria-dependent apoptosis pathways in splenocytes induced by IR. These results indicate that PPs are radioprotective promising reagents