Characterisation of GLUT4 trafficking in HeLa cells: comparable kinetics and orthologous trafficking mechanisms to 3T3-L1 adipocytes

Insulin-stimulated glucose transport is a characteristic property of adipocytes and muscle cells and involves the regulated delivery of glucose transporter (GLUT4)-containing vesicles from intracellular stores to the cell surface. Fusion of these vesicles results in increased numbers of GLUT4 molecules at the cell surface. In an attempt to overcome some of the limitations associated with both primary and cultured adipocytes, we expressed an epitope- and GFP-tagged version of GLUT4 (HA–GLUT4–GFP) in HeLa cells. Here we report the characterisation of this system compared to 3T3-L1 adipocytes. We show that insulin promotes translocation of HA–GLUT4–GFP to the surface of both cell types with similar kinetics using orthologous trafficking machinery. While the magnitude of the insulin-stimulated translocation of GLUT4 is smaller than mouse 3T3-L1 adipocytes, HeLa cells offer a useful, experimentally tractable, human model system. Here, we exemplify their utility through a small-scale siRNA screen to identify GOSR1 and YKT6 as potential novel regulators of GLUT4 trafficking in human cells.

Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm3 cm−3. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM.

PARTICLE-BASED TRANSPARENT FUSED VISUALIZATION APPLIED TO MEDICAL VOLUME DATA

This paper proposes a method to create 3D fusion images, such as volume–volume, volume–surface, and surface–surface fusion. Our method is based on the particle-based rendering, which uses tiny particles as rendering primitives. The method can create natural and comprehensible 3D fusion images simply by merging particles prepared for each element to be fused. Moreover, the method does not require particle sorting along the line of sight to realize right depth feel. We apply our method to realize comprehensible visualization of medical volume data.