Oscillating Magnetic Field Regulates Cell Adherence and Endothelialization Based on Magnetic Nanoparticle-Modified Bacterial Cellulose.

Despite the widely explored biomaterial scaffolds in vascular tissue engineering applications lately, no ideal platform has been provided for small diameter synthetic vascular grafts mainly due to the thrombosis issue. Endothelium is the only known completely non-thrombogenic material; so, functional endothelialization onto vascular biomaterials is critical in maintaining the patency of vascular networks. Bacterial cellulose (BC) is a natural biomaterial with superior biocompatibility and appropriate hydrophilicity as potential vascular grafts. In previous studies, surface modification of active peptides such as Arg-Gly-Asp (RGD) sequences onto biomaterials has been proven to achieve accelerated and selective endothelial cell (EC) adhesion. In our study, we demonstrated a new strategy to remotely regulate the adhesion of endothelial cells based on an oscillating magnetic field and achieve successful endothelialization on the modified BC membranes. In details, we synthesized bacterial cellulose (BC), magnetic BC (MBC), and RGD peptide-grafted magnetic BC (RMBC), modified with the HOOC-PEG-COOH-coated iron oxide nanoparticles (PEG-IONs). The endothelial cells were cultured on the three materials under different frequencies of an oscillating magnetic field, including "stationary" (0 Hz), "slow" (0.1 Hz), and "fast" (2 Hz) groups. Compared to BC and MBC membranes, the cells on RMBC membranes generally show better adhesion and proliferation. Meanwhile, the "slow" frequency of a magnetic field promotes this phenomenon on RMBC and achieves endothelialization after culture for 4 days, whereas "fast" inhibits the cellular attachment. Overall, we demonstrate a non-invasive and convenient method to regulate the endothelialization process, with promising applications in vascular tissue engineering.

Denoising the space-borne high-spectral-resolution lidar signal with block-matching and 3D filtering.

The constituents and structures of the atmosphere directly or indirectly affect the radiative energy budget of the Earth; thus, there is an urgent need to measure these components. Space-borne lidar is a powerful instrument for depicting the global atmosphere. Several space-borne lidars with spectral discrimination filters are proposed and even currently being developed, including the Chinese Aerosol-Cloud High-Spectral-Resolution Lidar (ACHSRL) onboard the Aerosol Carbon Detection Lidar satellite. However, the long distance from the satellite to the atmosphere near the Earth surface weakens the signal strength and debilitates the detection accuracy of space-borne lidar. Furthermore, due to absorption of Rayleigh scattering when it passes through the spectral discrimination filter, the signal-to-noise ratio in the molecular channel decreases. The traditional denoising method is to average the echo signals both vertically and horizontally, but the high speed of the satellite (7.5 km/s) and the varying atmosphere structure will blur detected layer features. A novel method to reduce the signal noise level of ACHSRL is proposed in this paper. A state-of-the-art algorithm for imaging denoising, block matching 3D filtering (BM3D), is employed. As ACHSRL has not been launched, a simulation study is performed. In the simulation experiment, we connect adjacent lidar signal profiles into one 2D matrix and treat it as an image. Unlike the existing lidar denoising algorithm which uses neighboring profiles to smooth, BM3D performs frequency domain transformation of the signal image and then searches for a similar patch in a given block to conduct collaborative filtering. This algorithm not only achieves denoising, but also preserves aerosol/cloud feature details. After denoising by BM3D, the peak signal-to-noise ratios of echo signals in all channels are improved and the retrieval accuracy of particulate optical properties is also refined, especially for the retrieval of the extinction coefficient.

Design of a high-spectral-resolution lidar for atmospheric temperature measurement down to the near ground.

In this paper, a high-spectral-resolution lidar (HSRL) for profiling atmospheric temperature from the ground to 10 km is proposed. A double Nd:YAG laser produces the transmitted laser at 532 nm. The backscattering lidar signal is passed through two different saturated iodine-vapor filters and thus obtains molecular scattering signals that can be employed to determine the temperature. A coaxial postposition transceiver is constructed with an off-axis aspheric reflective telescope (OART). The design of the transceiver and that of the OART are demonstrated. With this transceiver, the lidar blind zone where the overlap factor is zero can be reduced greatly, and accurate temperature measurement for full elevation can be achieved. The whole system is optimized with theoretical models based on geometrical optics and statistical error analyses. Monte Carlo simulations display the performance of the designed HSRL, showing that the all-day temperature retrieval error is better than 1.4 K from the ground to 10 km. The proposed HSRL is expected to provide more accurate atmospheric auxiliary parameters for the detection of aerosols' optical characteristics.

Retrieving the microphysical properties of opaque liquid water clouds from CALIOP measurements.

Cloud droplet effective radius (CER) and number concentration (CDNC) are two critical microphysical properties of liquid water clouds, which play essential roles in the Earth's radiative energy balance and atmospheric hydrological cycle. Even though many satellite remote sensing techniques have been developed to obtain these two properties, the observations are often limited to the daytime. In this study, a method to estimate CER and CDNC of liquid water clouds over global ocean area during both daytime and nighttime from CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) measurements is presented. The size sensitivity of the dual-wavelength (532 nm & 1064 nm) layer-integrated attenuated backscattering signals from CALIOP is checked and information content for liquid water cloud CER retrieval is found. Taking use of the artificial neural network (ANN) technique, the CER and then the CDNC are estimated from CALIOP by combining the polarization ratio and the dual wavelength attenuated backscattering signals. The comparisons with CER and CDNC estimated from MODIS (Moderate Resolution Imaging Spectroradiometer) during daytime demonstrate the feasibility of this new method. Both the daytime and nighttime CER and CDNC derived from CALIOP observations are presented in this paper and the day-night variation of liquid water cloud is discussed which would provide useful day-night variation of liquid water cloud properties.

Multiple scattering effects on the return spectrum of oceanic high-spectral-resolution lidar.

The return spectrum of the oceanic high-spectral-resolution lidar (HSRL) is simulated with a semianalytic spectral Monte Carlo (MC) method. The results show that the spectrum is similar to the single scattering spectrum at the water surface but broadens with the depth due to multiple scattering. Therefore, if the non-spectral MC method that ignores the spectrum broadening is used, deviations will be introduced into the HSRL retrieval, e.g., the effective particulate 180° volume scattering function (backscatter) and lidar attenuation coefficient (attenuation). The simulation indicates that the backscatter and attenuation deviations are within 10% and 2%, respectively, when the HSRL discriminator is the iodine absorption cell, and are within 3% and 1%, respectively, when the discriminator is changed to the field-widened Michelson interferometer.

Performance estimation of space-borne high-spectral-resolution lidar for cloud and aerosol optical properties at 532 nm.

Cloud and aerosol contribute with great uncertainty in Earth's radiative budget. There is an urgent need for global 3-D observation of these atmospheric constituents. High-spectral-resolution Lidar (HSRL) can obtain vertical atmosphere profile with high accuracy, hence several space-borne HSRLs are planned to launch in few years. However, as far as we know, the performance evaluation of space-borne HSRL has not been reported yet. In this paper, we present the characteristics of a new designed space-borne HSRL for aerosol and cloud optical property profiling (ACHSRL), which is part of the Aerosol & Carbon Detection Lidar (ACDL) developed in China. The ACHSRL is essentially similar to the famous Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which is on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Moreover, the ACHSRL employs an iodine absorption filter as the spectral discriminator. The atmospheric optical properties data observed by CALIOP is used to estimate the performance of ACHSRL. We chose the level 2 profile data (version 4.10) in South Japan in June 2015 to compare the detection uncertainty of ACHSRL and CALIOP. The simulation calculates the uncertainties of ACHSRL and makes a statistic analysis. The analysis result demonstrates that 73.63% of the backscatter coefficient uncertainties are below 40% for ACHSRL. By contrast, the number is 30.72% for CALIOP. As for absolute extinction coefficient errors, the statistics shows that 76.01% of the extinction coefficient uncertainties are lower than 0.2 km-1 for ACHSRL, while that for CALIOP are 56.97%. The assessment shows that ACHSRL could measure the particulate optical properties with better accuracy and compared with CALIOP. The estimation in this study reveals that the next generation space-borne HSRLs have a promising future.

Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar.

A detailed assessment is carried out in relation to the influence of the uncertainties associated with the input auxiliary atmospheric state parameters on retrieving aerosol optical properties from high-spectral-resolution lidar (HSRL) observations. The study starts from a review of the main spectral structure of the Rayleigh backscattering followed by evaluating the temperature effects on a backscattering cross section of atmospheric molecules based on numerical simulation. It shows that the transmittance of the background interference filter should be taken into account, depending on the full width at half maximum, although overall temperature dependence is negligible. Based on the Taylor expansion of the Tenti S6 model, the systematic errors arising from input temperature and pressure profiles are analyzed. It is demonstrated that the atmospheric pressure profiles have limited effects on the inversion results of aerosol optical parameters, as the atmospheric pressure is usually quite stable. The relative errors of the aerosol backscatter coefficient mainly stem from temperature profile errors and highly depend on the aerosol concentration. Quantitatively, the aerosol backscatter coefficient error could be larger than 5% with a 3 K deviation of temperature when the backscatter ratio is larger than 1.1. The accuracy of aerosol extinction coefficient retrieval is affected not only by the error in temperature, but also by the error in temperature lapse rate; the retrieval accuracy is more sensitive to the latter than the former. Further analysis based on the sounding temperature data shows that the variation of the temperature inversion layer during the night could induce a bias larger than 0.04 km-1 on the aerosol extinction coefficient retrieval. Therefore, the time resolution of temperature measurement from sounding balloons twice per day is too low to obtain an accurate retrieval of the aerosol optical properties from the HSRL.