Search

The article focuses on centrifugal pumps are essential mechanical components for liquid delivery in many biomedical systems whose miniaturization can promote innovative disease treatment approaches. It mentions soft materials and flexible electronics to achieve soft magnetic levitation micropumps (SMLMs) and rotation speed of per min to pump liquids with various viscosities. It also mentions development of SMLMs not only demonstrates the possibility to replace rigid rotating structures.

Large-scale forest disturbance often leads to changes in forest cover and structure, which imposes a great uncertainty in the estimation of the forest carbon cycle and biomass and affects other applications. In northeastern China, the Daxinganling region has abundant forest resources, where the forest coverage is about 30%. The Global LAnd Surface Satellite (GLASS) leaf area index (LAI) time series data provide important information to monitor the possible change of forests. In this study, we developed a new method to detect forest disturbances using GLASS LAI data over the Daxinganling region of Northeast China. As a dynamic model, the season-trend model has a higher sensitivity toward a seasonal change in LAI. Based on the accumulation of multi-year GLASS LAI products from 1997 to 2002, the dynamic model of LAI time series for each pixel is established first. The time-stepping modeling (TSM) process was designed by using the season-trend method, and sequential tests for detecting disturbances from a time series of pixels. Significant changes in the model parameters were captured as disturbance signals. Then, the near-infrared and shortwave-infrared bands of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance are used as auxiliary information to distinguish the types of forest disturbances. Here, the algorithm led to the detection of two different types of disturbances: fire and other (e.g., insect, drought, deforestation). In this study, we took the forest region as the study area, used the 8-day composite GLASS LAI data at 1000-m spatial resolution to identify each pixel as a fire disturbance, other disturbance, or non-disturbance. Validation was performed using reference burned area data derived from Landsat 30 m imagery. Results were also compared with the MCD64 product. The validation results were based on confusion matrices showing the overall accuracy (OA) exceeded 92% for our method and the MCD64 product. Statistical tests identified that TSM’s product accuracy is higher than that of MCD64. This study demonstrated that the TSM algorithm using a season-trend model provides a simple and automated approach to identify and map forest disturbance.

Oligonucleotide probes and the non-denaturing fluorescence in situ hybridization (ND-FISH) technique are widely used to analyze plant chromosomes because they are convenient tools. New oligonucleotide probes, Oligo-Ku, Oligo-3B117.1, Oligo-3B117.2, Oligo-3B117.2.1, Oligo-3B117.3, Oligo-3B117.4, Oligo-3B117.5, Oligo-3B117.6, Oligo-pTa71A-1, Oligo-pTa71A-2, Oligo-pTa71B-1, Oligo-pTa71B-2, Oligo-pTa71C-1, Oligo-pTa71C-2, Oligo-pTa71C-3 and Oligo-pTa71D were designed based on the repetitive sequences KU.D15.15, pSc119.2-like sequence 3B117 and pTa71. Oligonucleotide probe (GT)7 was also used. Oligo-Ku and (GT)7 can be together used to identify Dasypyrum villosum from wheat chromosomes and to distinguish individual D. villosum chromosomes. The oligonucleotide probes that were derived from the same repeat sequence displayed different signal intensity and hybridization sites on the same chromosomes. Both the length and the nucleotide composition of oligonucleotide probes determined their signal intensity. For example, Oligo-3B117.2 (25 bp) and Oligo-pTa71A-2 (46 bp) produced the strongest signals on chromosomes of wheat (Triticum aestivum L.), rye (Secale cereale L.), barley (Hordeum vulgare ssp. vulgare) or D. villosum, the signal of Oligo-3B117.4 (18 bp) on the short arm of 7B chromosome was weaker than that of Oligo-3B117.2.1 (15 bp) and Oligo-3B117.3 (16 bp), and Oligo-pTa71A-1 (38 bp) produced the same strong signals as Oligo-pTa71A-2 did on 1B and 6B chromosomes, but its signals on 1R and 1V chromosomes were weaker than the ones of Oligo-pTa71A-2. Oligonucleotide probes and ND-FISH analysis can reflect the distribution and structural statues of different segments of tandem repeats on chromosomes. The possible reasons why different segments derived from the same repeat sequence produced different signal patterns are discussed.

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.

Long-term high-quality global leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products are urgently needed for the study of global change, climate modeling, and many other problems. As the successor of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) will continue to provide global environmental measurements. This paper aims to generate longer time series Global LAnd Surface Satellite (GLASS) LAI and FAPAR products after the era of the MODIS sensor. To ensure spatial and temporal consistencies between GLASS LAI/FAPAR values retrieved from different satellite observations, the GLASS LAI/FAPAR retrieval algorithms were adapted in this study to retrieve LAI and FAPAR values from VIIRS surface reflectance time-series data. After reprocessing of the VIIRS surface reflectance to remove remaining effects of cloud contamination and other factors, a database generated from the GLASS LAI product and the reprocessed VIIRS surface reflectance for all Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites was used to train general regression neural networks (GRNNs). The reprocessed VIIRS surface reflectance data from an entire year were entered into the trained GRNNs to estimate the one-year LAI values, which were then used to calculate FAPAR values. A cross-comparison indicates that the LAI and FAPAR values retrieved from VIIRS surface reflectance were generally consistent with the GLASS, MODIS and Geoland2/BioPar version 1 (GEOV1) LAI/FAPAR values in their spatial patterns. The LAI/FAPAR values retrieved from VIIRS surface reflectance achieved good agreement with the GLASS LAI/FAPAR values (R2 = 0.8972 and RMSE = 0.3054; and R2 = 0.9067 and RMSE = 0.0529, respectively). However, validation of the LAI and FAPAR values derived from VIIRS reflectance data is now limited by the scarcity of LAI/FAPAR ground measurements.

The fractional vegetation cover (FCover) is an essential biophysical variable and plays a critical role in the carbon cycle studies. Existing FCover products from satellite observations are spatially incomplete and temporally discontinuous, and also inaccurate for some vegetation types to meet the requirements of various applications. In this study, an operational method is proposed to calculate high-quality, accurate FCover from the Global LAnd Surface Satellite (GLASS) leaf area index (LAI) product to ensure physical consistency between LAI and FCover retrievals. As a result, a global FCover product (denoted by TRAGL) were generated from the GLASS LAI product from 2000 to present. With no missing values, the TRAGL FCover product is spatially complete. A comparison of the TRAGL FCover product with the Geoland2/BioPar version 1 (GEOV1) FCover product indicates that these FCover products exhibit similar spatial distribution pattern. However, there were relatively large discrepancies between these FCover products over equatorial rainforests, broadleaf crops in East-central United States, and needleleaf forests in Europe and Siberia. Temporal consistency analysis indicates that TRAGL FCover product has continuous trajectories. Direct validation with ground-based FCover estimates demonstrated that TRAGL FCover values were more accurate (RMSE = 0.0865, and R2 = 0.8848) than GEOV1 (RMSE = 0.1541, and R2 = 0.7621).

Currently, multiple leaf area index (LAI) products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method was developed to simultaneously retrieve multiscale LAI data from satellite observations with different spatial resolutions based on an ensemble multiscale filter (EnMsF). The LAI average values corresponding to the date of satellite observations were calculated from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and were used as a priori knowledge for LAI in order to construct an initial ensemble multiscale tree (EnMsT). Satellite observations obtained at different spatial resolutions were then applied to update the LAI values at each node of the EnMsT using a two-sweep filtering procedure. Next, the retrieved LAI values at the finest scale were used as a priori knowledge for LAI for the new round of construction and updating of the EnMsT, until the sum of the difference of LAI values at each node of the EnMsT between two adjacent updates is less than a given threshold. The method was tested using Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and MODIS surface reflectance data from five sites that have different vegetation types. The results demonstrate that the retrieved LAI values for each spatial resolution were in good agreement with the aggregated LAI reference map values for the corresponding spatial resolution. The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE) = 0.45) compared with that obtained from the MODIS LAI values (RMSE = 1.30).

Mapping surface all-wave net radiation (Rn) is critically needed for various applications. Several existing Rn products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime Rn product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS Rn product based on high-quality in situ measurements in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm−2, and an average bias of −17.59 Wm−2. We also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS Rn product is satisfactory. The GLASS Rn product from 2000 to the present is operational and freely available to the public.

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.

The equation of state (EoS) of neutron star matter plays a key role in both the structure and evolution of a neutron star. However, as lattice QCD faces significant difficulties in simulating dense matter, only effective models can be relied on to unveil their properties, such as the microscopic many body theories and density functional theories. Due to the uncertainties reside in model assumptions and parameters, the matter state and composition of dense matter are still unclear and exhibit large ambiguities, leading to large uncertainties in the corresponding EoSs. The measurements of neutron star properties in multi-messenger era, on the other hand, have placed a stringent constraint on its EoS. For example, the measurements of2M⊙ pulsars have ruled out various soft EOSs for dense stellar matter. Based on the multi-messenger observations of the binary neutron star merger event GW170817, the tidal deformability of 1.4M⊙ neutron star is believed to lie within 70≤Lambda1.4≤580 with the corresponding radii R=（11.9±1.4）km, excluding the EoSs which are too stiff at small densities. The masses and radii of the two millisecond pulsars PSR J0030+0451 and PSR J0740+6620 were measured accurately via pulse-profile modeling with NICER and XMM-Newton data, where similar radii (about 12.4 km) were obtained despite their large differences in masses. This reduces the likelihood of a strong first-order phase transition inside neutron stars with masses M≤2M⊙. Adopting various EoS parameterizations which are either phenomenological or nuclear physics-motivated, extensive investigations were carried out by applying those observational data to constraining the EoSs and neutron star properties with Bayesian parameter estimation. On the other hand, as proposed by Biswas, one can take a complementary approach to estimate statistically the most preferred EoS according to the current observations. In light of the recently obtained 10 unified neutron star EoSs using relativistic mean field models with nonlinear self couplings (NL3, PK1, TM1, GM1, and MTVTC) and density-dependent couplings (DD-LZ1, DDME-X, PKDD, DD-ME2, and TW99), combined with the other 6 unified EoSs (FSU2, DD2, NL3wr-L55, FSU2R, FSU2H, and TM1e) which were obtained in a similar manner, those EoS models then can be studied for Bayesian model selection, which is not included in Biswas’s original study. In this work, a Bayesian model selection on the 16 unified neutron star EoSs predicted by relativistic mean field models is thus performed, which are available on CompOSE. In particular, the tidal deformability measurements were used from the binary neutron star merger event GW170817 and the simultaneous mass-radius measurements of PSR J0030+0451 and PSR J0740+6620 by the NICER collaboration. The most preferred EoS model is DD2, which predicts the radius and tidal deformability of a 1.4M⊙ neutron star to be 13.19 km and 687, respectively. The selected EoS models that are not ruled out by observation can then be ordered with respect to the relative odds ratio: DD2, TW99, DD-LZ1, DD-ME2, TM1e, FSU2H, DDME-X, PKDD, FSU2R, and MTVTC. These EoSs predict that the radii and tidal deform abilities of 1.4M⊙ neutron stars should lie within 12.3-13.6 km and 400-784, while the maximum masses range from 2.02M⊙ to 2.56M⊙.

Cassava (Manihot esculenta) is known as the third most important food crop in the tropics and also used for industrial feedstock for biofuels. Two new bacterial artificial chromosome (BAC) libraries were constructed for W14 (M. Esculenta ssp. flabellifolia), a wild ancestor of domesticated cassava. The libraries were constructed with EcoRI and HindIII insertion vectors, respectively. The EcoRI library has 29952 clones with an average insert size of 115 kb, while the HindIII library consists of 29952 clones with an average insert of 129 kb. The combined libraries contain a total of 59904 clones with an average insert size of 125 kb, representing approximately 10 × haploid genome equivalents. A total of 29952 clones were fingerprinted and resulted in a cassava physical map composed of 2485 contigs with an average physical length of 336 kb and 2909 singletons, representing approximately 762 Mb of the cassava genome. 5000 clones located at the ends of BAC contigs were selected and sequenced. A total of 6077 SNPs and 231 indels were identified, that covered 459 gene sequences, of which 6 genes were associated with starch and sucrose metabolism. This BAC-based physical map provides valuable tools to understand the genetics and evolution of cassava.

A full-length cDNA library from leaf and root tissues of cassava (Manihot esculenta) Arg7 and one accession of its wild ancestor W14 (M. esculenta ssp. flabellifolia) has been constructed. The library is comprised of four sub-libraries, containing 32640 recombinant clones, 6028 cDNA clones from their 5′ ends, and 128 clones from the 3′ ends were sequenced. In total, 5013 high-quality expressed sequence tags (ESTs) and 1259 unigenes were obtained. Of these, 746 unigenes were identified by their sequence homologies to ESTs from model plants, and 323 unigenes were mapped onto 114 different KEGG pathways. From these, 24 differentially expressed genes involved in starch metabolism and photosynthesis were identified and five of them were selected to compare their expression level between Arg7 and W14. Notably, Arg7 has a higher net photosynthesis rate in leaves, higher ribulose-1,5-bisphosphate carboxy-lase oxygenase activities in leaves, and higher AGPase activity in roots. This resource is the first EST collection from wild cassava and should be of value for gene discovery, genome annotation and studies of Manihot evolution.

Viral infections still threaten human health all over the world, and many people die from viral diseases every year. However, there are no effective vaccines or drugs for preventing or managing most viral diseases. Thus, the discovery and development of broad-spectrum antiviral agents remain urgent. Here, we expressed and purified a venom peptide, Ev37, from the scorpion Euscorpiops validus in a prokaryotic system. We found that rEv37 can inhibit dengue virus type 2 (DENV-2), hepatitis C virus (HCV), Zika virus (ZIKV), and herpes simplex virus type 1 (HSV-1) infections in a dose-dependent manner at noncytotoxic concentrations, but that it has no effect on Sendai virus (SeV) and adenovirus (AdV) infections in vitro. Furthermore, rEv37 alkalized acidic organelles to prevent low pH--dependent fusion of the viral membrane--endosomal membrane, which mainly blocks the release of the viral genome from the endosome to the cytoplasm and then restricts viral late entry. Taken together, our results indicate that the scorpion venom peptide Ev37 is a broad-spectrum antiviral agent with a specific molecular mechanism against viruses undergoing low pH--dependent fusion activation during entry into host cells. We conclude that Ev37 is a potential candidate for development as an antiviral drug. [ABSTRACT FROM AUTHOR]

Cassava, a tropical food, feed and biofuel crop, has great capacity for biomass accumulation and an extraordinary efficiency in water use and mineral nutrition, which makes it highly suitable as a model plant for tropical crops. However, the understanding of the metabolism and genomics of this important crop is limited. The recent breakthroughs in the genomics of cassava, including whole-genome sequencing and transcriptome analysis, as well as advances in the biology of photosynthesis, starch biosynthesis, adaptation to drought and high temperature, and resistance to virus and bacterial diseases, are reviewed here. Many of the new developments have come from comparative analyses between a wild ancestor and existing cultivars. Finally, the current challenges and future potential of cassava as a model plant are discussed.