Search

1-Alkylpyridinium bromide [C

Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using one-meter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments.

Peste des petits ruminants (PPR) is an acute, highly infectious, and highly pathogenic disease, which mainly damages small ruminants such as goats and sheep. Hemagglutinin protein (H), the main antigenic protein of peste des petits ruminants virus (PPRV), has been a hot spot in the research of genetic engineering vaccine for PPRV. In this study, the silkworm baculovirus surface display technology is combined with the transmembrane structure of the silkworm baculovirus envelope protein GP64 and different characteristics of the promoters to display four kinds of fusion proteins, which contain Pph-H, Pph-HJ, Pie1-H, and Pie1-HJ. The fusion proteins displayed on baculovirus surface have been detected by western blotting, cell surface immunofluorescence, and immunogold electron microscopy. In addition, the dominant form of PPR H displayed on baculovirus surface has been determined which is fusion protein mediated by Pph containing the hemagglutinin protein and full-length GP64, Pph-H. Furthermore, by comparing the fluorescence intensity of binding of hemagglutinin protein and signaling lymphocyte activation molecules (SLAM) in Vero-SLAM cells by immunocytochemistry, Pph-H can be combined with the receptor protein of PPRV, SLAM. It provides technical support for displaying the different structure of hemagglutinin and exploring the key sites of hemagglutinin and SLAM binding. Meanwhile, it is important for exploring the pathogenesis and immune mechanism of PPRV.

Peste-des-petits-ruminants virus (PPRV) haemagglutinin (H) protein mediates binding to cellular receptors and then initiates virus entry. To identify the key residues of PPRV H (Hv) protein of the Nigeria 75/1 strain involved in binding to receptors, interaction of the Hv and mutated Hv (mHv) proteins with receptors (SLAM and Nectin 4) and their mutants (mSLAM1, mSLAM2, mSLAM3 and mNectin 4) was investigated using surface plasmon resonance imaging (SPRi) and coimmunoprecipitation (co-IP) assays. The results showed that the Hv protein failed to interact with mSLAM3, but interacted at a strong or medium intensity with SLAM, mSLAM2, Nectin 4 and mNectin 4, and at a low level with mSLAM1. The mHv protein was unable to interact with SLAM and its mutants, but bound to Nectin 4 and mNectin 4 with medium and weak intensity, respectively. Further analysis showed that the Hv protein could precipitate mSLAM1, mSLAM2 and mNectin 4, but not mSLAM3. The mHv protein failed to coprecipitate with SLAM and its mutants. The binding activities of mNectin 4 and Nectin 4 to mHv were less than 30.36 and 51.94 % of the wild-type levels, respectively. Based on the results obtained, amino acids at positions R389, L464, I498, R503, R533, Y541, Y543, F552 and Y553 of H protein and I61, H62, L64, K76, K78, E123, H130, I210, A211, S226 and R227 in SLAM were identified to be essential for the specificity of H-SLAM interaction, while the critical residues of H-Nectin 4 interaction require further study. These findings would improve our understanding of the invasive mechanisms of PPRV.

While the functions of STING1 (stimulator of interferon response cGAMP interactor 1) during DNA virus infection had been well documented, the roles STING1 plays during RNA viruses infection is obscure. Infection with foot-and-mouth disease virus (FMDV), a well-known picornavirus, induces endoplasmic reticulum (ER) stress response and autophagy. Here, we found that the FMDV-induced integrated stress response originates from the cellular pattern recognition receptor DDX58/RIG-I (DExD/H-box helicase 58). DDX58 transmits signals to the ER-anchored adaptor protein STING1, which specifically activates the EIF2AK3/PERK (eukaryotic translation initiation factor 2A)-dependent integrated stress response and finally leads to reticulophagy and degradation of STING1 itself. Knockdown/knockout of STING1 or EIF2AK3 suppresses FMDV genome replication and viral protein expression. Reticulophagy induction by STING1 does not require its translocation to the Golgi or IFN response activation. However, STING1 polymerization is necessary for the FMDV-induced integrated stress response and reticulophagy. Our work illustrated the signaling cascades that mediate the cellular stress response to FMDV infection and indicated that induction of autophagy in response to both DNA and RNA virus infection may be an evolutionarily conserved function of STING1. Abbreviations: ATF6: activating transcription factor 6; CGAS: cyclic GMP-AMP synthase; DDX58/RIG-I: DExD/H-box helicase 58; EIF2A/eIF2α: eukaryotic translation initiation factor 2A; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 2; ER: endoplasmic reticulum; ERN1/IRE1: endoplasmic reticulum to nucleus signaling 1; FMD: foot-and-mouth disease; FMDV: foot-and-mouth disease virus; IFIH1/MDA5: interferon induced with helicase C domain 1; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MAVS: mitochondrial antiviral signaling protein; MOI: multiplicity of infection; RETREG1/FAM134B: reticulophagy regulator 1; STING1: stimulator of interferon response cGAMP interactor 1; TCID50: 50% tissue culture infectious dose; XBP1: X-box binding protein 1.

Background: Peste des petits ruminants virus (PPRV) is a negative-stranded RNA virus belonging to the Paramyxoviridae family and causes acute, highly contagious disease in small ruminants. Lysine acetylation plays central role in regulating gene expression. However, the extent and function of lysine acetylation in host cells during PPRV infection remains unknown. Methods: Lysine acetylation of PPRV-infected Vero cells was tested and differentially expressed lysine acetylation was found. The acetylated peptides were enriched using specific antibody and labeled with demethylation. Proteins with acetylation sites were identified. Subsequently, intensive bioinformatics analysis of succinylome of PPRV-infected Vero cells was were performed. In this study, intensive proteomic quantification analysis of the proteome and acetylome of PPRV-infected Vero cells was performed using dimethylation labeling-based quantitative proteomics. Results: We identified 4729 cellular proteins and 1068 proteins with 2641 modification sites quantifiable detected by mass spectrometry, of which 304 proteins with 410 acetylation sites were significantly acetylated in response to PPRV infection. Bioinformatics analyses revealed that the differentially acetylated proteins mainly participated in carbohydrate catabolic and DNA metabolic process, and were associated with multifarious functions, suggesting that intracellular activities were extensively changed after PPRV infection. Protein-protein interaction (PPI) network of the identified proteins further indicated that a variety of chaperone and ribosome processes were modulated by acetylation. Conclusions: To our knowledge, this is the first study on acetylome in host cell infected with PPRV. It provides an important baseline to future study the roles of acetylation in the host response to PPRV replication.

Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using one-meter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments.

Coal fire hazards not only causes loss of coal resources but also induces serious geological hazards and environmental degeneration. Wuda Coalfield (China), known for its widespread coal fires, was used as the study area for this study. Owing to an inherent noise problem, CBERS (China & Brazil Earth Resource Satellite)-04 thermal infrared images have not been explored and applied in the field of coal fire detection. An adaptive-edge-threshold (AET) method was proposed to delineate coal fire maps based on nighttime CBERS-04 data from 2015 to 2020. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was employed to cluster coal fire spots into different classes, then coal fire propagation directions were generated by connecting the clustering centers of the same class in chronological order. The results of quantitative analysis of coal fires showed that AET method achieved the highest accuracy among four coal fire recognition algorithms; the accuracy of AET in field verification reached 81.25%. The total area of coal fires increased by 137%, while the total intensity of coal fires decreased by 18% from 2015 to 2020. Overall accuracies of ISODATA clustering coal fire spots were between 90.25% and 100.00%, the Kappa coefficients were between 0.82 and 1.00. The evolution trend of coal fires obtained based on this clustering accuracy is reliable. The evolution trend of coal fires is as follows: coal fires of Class I spread to the northwest; coal fires of Class II first propagated to the northwest, then to the south and then to the west; coal fires of Class III essentially stayed in the same place; coal fires of Class IV spread to the northwest; coal fires of Class V spread to the southeast and then to the northeast.

To investigate bay hydrodynamics and its impacts on the adjacent micro-tidal wetland sustainability, hourly measurements of wave, tidal current, and benthic suspended sediment concentration in summer, winter, and spring of 2015–2016 were conducted in Fourleague Bay, Louisiana, USA. High-temporal resolution data indicate that benthic suspended sediment resuspension had a dominant periodicity of 4.8-d, which was mainly caused by wind-driven waves. Sediment flux reached 28 g·m−2·s−1 during events. Net sediment flux direction is northwestward in summer, and southeastward in winter and spring. Potential depth-integrated sediment flux to surrounding wetland varied within 0–500 g·m−1·s−1. Seasonal variations of river discharge and wind direction (particularly speed >3 m·s−1) dominated potential sediment contribution from the bay to the surrounding wetland. Three sediment transport regimes were delineated: ‘bypassing’ season, resuspension-accumulation season, and combined ‘bypassing’ and resuspension-accumulation season. This study couples bay hydrodynamics to the sediment transport processes and sustainability of adjacent wetlands in a micro tidal environment. It sheds light on the understanding of natural feedback mechanisms and how estuarine-marsh system survive high relative sea level rising scenario in micro tidal environment, which could aid in the design of future ecological engineering restoration strategies.

Peste-des-petits-ruminants virus (PPRV) haemagglutinin (H) protein mediates binding to cellular receptors and then initiates virus entry. To identify the key residues of PPRV H (Hv) protein of the Nigeria 75/1 strain involved in binding to receptors, interaction of the Hv and mutated Hv (mHv) proteins with receptors (SLAM and Nectin 4) and their mutants (mSLAM1, mSLAM2, mSLAM3 and mNectin 4) was investigated using surface plasmon resonance imaging (SPRi) and coimmunoprecipitation (co-IP) assays. The results showed that the Hv protein failed to interact with mSLAM3, but interacted at a strong or medium intensity with SLAM, mSLAM2, Nectin 4 and mNectin 4, and at a low level with mSLAM1. The mHv protein was unable to interact with SLAM and its mutants, but bound to Nectin 4 and mNectin 4 with medium and weak intensity, respectively. Further analysis showed that the Hv protein could precipitate mSLAM1, mSLAM2 and mNectin 4, but not mSLAM3. The mHv protein failed to coprecipitate with SLAM and its mutants. The binding activities of mNectin 4 and Nectin 4 to mHv were less than 30.36 and 51.94 % of the wild-type levels, respectively. Based on the results obtained, amino acids at positions R389, L464, I498, R503, R533, Y541, Y543, F552 and Y553 of H protein and I61, H62, L64, K76, K78, E123, H130, I210, A211, S226 and R227 in SLAM were identified to be essential for the specificity of H–SLAM interaction, while the critical residues of H–Nectin 4 interaction require further study. These findings would improve our understanding of the invasive mechanisms of PPRV.

Background The specific and dynamic interaction between the hemagglutinin (H) and fusion (F) proteins of morbilliviruses is a prerequisite for the conformational rearrangements and membrane fusion during infection process. The two heptad repeat regions (HRA and HRB) of F protein are both important for the triggering of F protein. Methods In this study, the direct interactions of Peste des petits ruminants virus (PPRV) H with F, HRA and HRB were quantitatively evaluated using biosensor surface plasmon resonance (SPR). Results The binding affinities of immobilized pCMV-HA-H (HA-H) interacted with proteins pCMV-HA-F (HA-F) and pCMV-HA-HRB (HA-HRB) (KD = 1.91 × 10− 8 M and 2.60 × 10− 7 M, respectively) reacted an order of magnitude more strongly than that of pCMV-HA-HRA (HA-HRA) and pCMV-HA-Tp IGFR-LD (HA) (KD = 1.08 × 10− 4 M and 1.43 × 10− 4 M, respectively). Conclusions The differences of the binding affinities suggested that HRB is involved in functionally important intermolecular interaction in the fusion process.

Terrestrial Light Detection And Ranging (LiDAR), also referred to as terrestrial laser scanning (TLS), has gained increasing popularity in terms of providing highly detailed micro-topography with millimetric measurement precision and accuracy. However, accurately depicting terrain under dense vegetation remains a challenge due to the blocking of signal and the lack of nearby ground. Without dependence on historical data, this research proposes a novel and rapid solution to map densely vegetated coastal environments by integrating terrestrial LiDAR with GPS surveys. To verify and improve the application of terrestrial LiDAR in coastal dense-vegetation areas, we set up eleven scans of terrestrial LiDAR in October 2015 along a sand berm with vegetation planted in Plaquemines Parish of Louisiana. At the same time, 2634 GPS points were collected for the accuracy assessment of terrain mapping and terrain correction. Object-oriented classification was applied to classify the whole berm into tall vegetation, low vegetation and bare ground, with an overall accuracy of 92.7% and a kappa value of 0.89. Based on the classification results, terrain correction was conducted for the tall-vegetation and low-vegetation areas, respectively. An adaptive correction factor was applied to the tall-vegetation area, and the 95th percentile error was calculated as the correction factor from the surface model instead of the terrain model for the low-vegetation area. The terrain correction method successfully reduced the mean error from 0.407 m to −0.068 m (RMSE errors from 0.425 m to 0.146 m) in low vegetation and from 0.993 m to −0.098 m (RMSE from 1.070 m to 0.144 m) in tall vegetation.

Peste des petits ruminants (PPR) is one of the highly contagious transboundary viral diseases of small ruminants. Host microRNA (miRNA) expression patterns may change in response to virus infection, and it mainly works as a post-transcriptional moderator in gene expression and affects viral pathogenesis and replication. In this study, the change of miRNA expression profile in peripheral blood lymphocyte (PBMC) from sheep inoculated with PPR vaccine virus in vivo as well as primary sheep testicular (ST) cells inoculated with PPR vaccine virus in vitro were determined via deep sequencing technology. In PBMC cells, 373 and 115 differentially expressed miRNAs (DEmiRNAs) were identified 3 days and 5 days post inoculated (dpi), respectively. While, 575 DEmiRNAs were identified when comparing miRNA profiles on 5 dpi with 3 dpi. Some of the DEmiRNAs were found to change significantly via time-course during PPR vaccine virus inoculated. Similarly, in ST cells, 136 DEmiRNAs were identified at 3 dpi in comparison with mock-inoculation. A total of 12 DEmiRNAs were validated by real-time quantitative PCR (RT-qPCR). The oar-miR-150, oar-miR-370-3p and oar-miR-411b-3p were found common differentially expressed in both PPR vaccine virus-inoculated PBMC cells and ST cells. Targets prediction and functional analysis of the DEmiRNAs uncovered mainly gathering in antigen processing and presentation pathways, protein processing in endoplasmic reticulum pathways and cell adhesion molecules pathways. Our study supplies information about the DEmiRNAs in PPR vaccine virus-inoculated PBMC cells and ST cells, and provides clues for further understanding the function of miRNAs in PPR vaccine virus replication.

Coastal Louisiana hosts 37% of the coastal wetland area in the conterminous US, including one of the deltaic coastal regions more susceptible to the synergy of human and natural impacts causing wetland loss. As a result of the construction of flood protection infrastructure, dredging of channels across wetlands for oil/gas exploration and maritime transport activities, coastal Louisiana has lost approximately 4900 km2 of wetland area since the early 1930s. Despite the economic relevance of both wetland biomass and net primary productivity (NPP) as ecosystem services, there is a lack of vegetation simulation models to forecast the trends of those functional attributes at the landscape level as hydrological restoration projects are implemented. Here, we review the availability of peer-reviewed biomass and NPP wetland data (below and aboveground) published during the period 1976&ndash, 2015 for use in the development, calibration and validation of high spatial resolution (&lt, 200 m &times, 200 m) vegetation process-based ecological models. We discuss and list the knowledge gaps for those species that represent vegetation community associations of ecological importance, including the long-term research issues associated to limited number of paired belowground biomass and productivity studies across hydrological basins currently undergoing different freshwater diversions management regimes and hydrological restoration priorities.

Taenia saginata, Taenia solium and Taenia asiatica (beef, pork and Asian tapeworms, respectively) are parasitic flatworms of major public health and food safety importance. Among them, T. asiatica is a newly recognized species that split from T. saginata via an intermediate host switch ∼1.14 Myr ago. Here we report the 169- and 168-Mb draft genomes of T. saginata and T. asiatica. Comparative analysis reveals that high rates of gene duplications and functional diversifications might have partially driven the divergence between T. asiatica and T. saginata. We observe accelerated evolutionary rates, adaptive evolutions in homeostasis regulation, tegument maintenance and lipid uptakes, and differential/specialized gene family expansions in T. asiatica that may favour its hepatotropism in the new intermediate host. We also identify potential targets for developing diagnostic or intervention tools against human tapeworms. These data provide new insights into the evolution of Taenia parasites, particularly the recent speciation of T. asiatica.

Sand cay is a special kind of islet formed by coral detritus and bioclast, which is common in Nansha Islands of China. Some sand cays play an important role in ocean strategy and economy, but surprisingly we know little about them, especially those recently formed sand cays. In this research, we monitor migration of a new sand cay in Nanxun Jiao (Gaven Reef) using a series of QuickBird and WorldView-2 satellite images between June 2006 and August 2013. We conduct a regression between migration distance and wind observational data to examine the migration patterns of the new sand cay. The migration distance is calculated based on the sand cay locations extracted based on Normalized Difference Water Index (NDWI). The wind observational data downloaded from NOAA are reformed into four wind direction vectors. Based on the results of regression, we concluded that the migration of the new sand cay on Nanxun Jiao was significantly associated with the east, west and north wind. East wind was the main influence factor of the migration; its impact strength was almost twice as the west and north wind. The south wind has little effect on the migration of the sand cay, which is partly blocked by the artificial structure in the south.

This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and water conditions. For NDVI time series starting at the beginning of the year and terminating at the end of the year, the method can separate the non-growing season cycle and growing season cycle for crops. The non-growing season cycle may provide some useful information for crop recognition, such as soil conditions. However, the shape of the growing season’s NDVI time series for crops is the key to separating cropland from other land cover types because the shape contains all of the crop growth information. The principle of the OLWDTW method is to enhance the effects of the growing season cycle on the NDVI time series by adding a local weight to the growing season when comparing the similarity of time series based on the open-boundary dynamic time warping (DTW) method. Experiments with two satellite datasets located near the Khorat Plateau in the Lower Mekong Basin validate that OLWDTW effectively improves the precision of cropland recognition compared to a non-weighted open-boundary DTW method in terms of overall accuracy. The method’s classification accuracy on cropland exceeds the non-weighted open-boundary DTW by 5–7%. In future studies, an open-boundary self-adaption locally weighted DTW and a more effective combination rule for different crop types should be explored for the method’s best performance and highest extraction accuracy for cropland.

Terrestrial laser scanning (TLS) swings a tiny-footprint laser to resolve 3D structures rapidly and precisely, affording new opportunities for ecosystem studies, but its actual utility depends largely on efficacies of lidar analysis methods. To improve characterizing forest canopies with TLS, we forged a methodological paradigm that combines physics and statistics to derive foliage profile, leaf area index (LAI), and leaf angle distribution (LAD): We modeled laser-vegetation interactions probabilistically and then developed a maximum likelihood estimator (MLE) of vegetation parameters. Unlike classical gap-based algorithms, MLE explicitly accommodates laser scanning geometries, fully leverages raw laser ranging data, and simultaneously derives foliage profile and LAD. We evaluated MLE using both synthetic lidar data and real TLS scans at sites in Everglades National Park, USA. Estimated LAI differed between algorithms by an average of 26%. Compared to classical gap analyses, MLE derived foliage density profile and LAD more accurately. Also, MLE has a rigorous statistical foundation and generated error intervals better indicative of the true uncertainties of estimated canopy parameters—an aspect often overlooked but essential for credible use of lidar vegetation products. The theoretical justification and experimental evidence converge to suggest that classical gap methods are sub-optimal for exploiting tiny-footprint lidar data and MLE offers a paradigm-shifting alternative. We envision that MLE will further boost confident use of terrestrial lidar as a versatile tool for environmental applications, such as forest survey, ecological conservation, and ecosystem management. © 2015 Elsevier B.V. All rights reserved.

Spectral mixture analysis has been frequently applied in various fields to solve the mixed pixel problem in remote sensing. So far, all the research in mixture analysis has focused on the sub-pixel analysis, i.e., selecting endmembers and conducting mixture analysis at the pixel level. Research efforts in mixture analysis at the object level are very scarce, even though the object-based image analysis (OBIA) techniques have been well developed. In this study, we examined the applicability of object-based mixture analysis in an urban environment using a Landsat Thematic Mapper image. Informative and accurate object-based fraction maps (vegetation, impervious surface, and water) were produced by combining the OBIA and multiple endmember spectral mixture analysis (MESMA) techniques. A new approach to identifying the spectral representatives of a specific class for MESMA was developed. The accuracy of the object-based fraction maps were assessed using manual interpretation results of a 1-m digital aerial phot...

As experienced by Apollo lunar astronauts, spatial orientation can be affected significantly by lunar environmental conditions such as the moon's altered gravity, lack of an atmosphere, limited spatial references, and different level of reflectivity. To help overcome these challenges, a lunar astronaut navigation system called LASOIS Lunar Astronaut Spatial Orientation and Information System has been developed. It can significantly reduce spatial disorientation and improve real-time navigation capability for astronauts exploring the lunar surface. LASOIS is capable of integrating satellite imagery and sensors mounted on the astronaut spacesuit including inertial measurement units, stereo cameras, and pressure sensors by an extended Kalman filter algorithm. The processed navigation information is presented through a wrist-mounted display system. The system has been tested at three field experiment sites, including Moses Lake, WA, Black Lava Point, AZ, and Haleakala National Park, HI. It is demonstrated that the system has achieved an error rate or relative accuracy of 2.4% for astronaut navigation over a traverse of 6.1 km in a lunarlike environment.

Photolysis of some polycyclic aromatic hydrocarbons (PAHs) on soil surfaces may play an important role in the fate of PAHs in the environment. Photolysis of PAHs on soil surfaces under UV irradiation was investigated. The effects of oxygen, irradiation intensity and soil moisture on the degradation of the three PAHs were observed. The results showed that oxygen, soil moisture and irradiation intensity enhanced the photolysis of the three PAHs on soil surfaces. The degradation of the three PAHs on soil surfaces is related to their absorption spectra and the oxidation-half-wave potential. The photolysis of PAHs on soil surfaces in the presence of oxygen followed pseudo first-order kinetics. The photolysis half-lives ranged from 37.87 days for benzo[a]pyrene to 58.73 days for phenanthrene. The results indicate that photolysis is a successful way to remediate PAHs-contaminated soils.

Relating less directly to the physical reflectance from remote sensors, land-use analysis is comparably more challenging than land-cover studies, especially for residential land-uses. This research presents an object-oriented approach to detect residential land use of buildings directly from lidar data, aerial photography, and road maps to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then uses a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on seven land-use characteristics of buildings. Experiments based on the 8.25 km2 study site located in Austin, Texas proved the possibility and efficiency of directly detecting and identifying residential buildings from remote sensing images with 81.1 percent of residential buildings correctly classified.

A lack of reliable observations for canopy science research is being partly overcome by the gradual use of lidar remote sensing. This study aims to improve lidar-based canopy characterization with airborne laser scanners through the combined use of lidar composite metrics and machine learning models. Our so-called composite metrics comprise a relatively large number of lidar predictors that tend to retain as much information as possible when reducing raw lidar point clouds into a format suitable as inputs to predictive models of canopy structural variables. The information-rich property of such composite metrics is further complemented by machine learning, which offers an array of supervised learning models capable of relating canopy characteristics to high-dimensional lidar metrics via complex, potentially nonlinear functional relationships. Using coincident lidar and field data over an Eastern Texas forest in USA, we conducted a case study to demonstrate the ubiquitous power of the lidar composite metrics in predicting multiple forest attributes and also illustrated the use of two kernel machines, namely, support vector machine and Gaussian processes (GP). Results show that the two machine learning models in conjunction with the lidar composite metrics outperformed traditional approaches such as the maximum likelihood classifier and linear regression models. For example, the five-fold cross validation for GP regression models (vs. linear/log-linear models) yielded a root mean squared error of 1.06 (2.36) m for Lorey's height, 0.95 (3.43) m for dominant height, 5.34 (8.51) m 2 /ha for basal area, 21.4 (40.5) Mg/ha for aboveground biomass, 6.54 (9.88) Mg/ha for belowground biomass, 0.75 (2.76) m for canopy base height, 2.2 (2.76) m for canopy ceiling height, 0.015 (0.02) kg/m 3 for canopy bulk density, 0.068 (0.133) kg/m 2 for available canopy fuel, and 0.33 (0.39) m 2 /m 2 for leaf area index. Moreover, uncertainty estimates from the GP regression were more indicative of the true errors in the predicted canopy variables than those from their linear counterparts. With the ever-increasing accessibility of multisource remote sensing data, we envision a concomitant expansion in the use of advanced statistical methods, such as machine learning, to explore the potentially complex relationships between canopy characteristics and remotely-sensed predictors, accompanied by a desideratum for improved error analysis.

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

From July 11-12, 2009, the tropical storm Soudeler swept the study area with a Level 8 wind and disturbed the suspended substance in this coastal area, which may have caused some fatal impact on the health condition of coral reef in Xuwen coral reef coast located in Leizhou Peninsula of South China. In order to evaluate the impact of extreme weather on coral reef, this study applied and validated a TSS model to map the TSS variation based on red and infrared spectral bands of MODIS data through one before-storm and two after-storm images after applying the atmospheric correction of in-water linear regression analysis. By mapping and comparing the changes of TSS values before- and after- tropical storm, this study found substantial increases of TSS concentrations as a mean value of 47.8 mg/L (~3.6 times of mean TSS value before rainstorm) in the area during the passage of tropical storm compared to those under no-storm condition. Besides, the TSS returned back to even lower values five days after the passage of tropical storm as a mean value of 3.6mg/L (~one quarter of mean TSS value (13.4 mg/L) before rainstorm). The conclusion was made that the TSS concentration in estuary and coastal areas under local rainstorm tends to return to a normal level faster (approximately 2.5 days) than under a hurricane [1] or tropical storm as discovered in this study (approximately 5 days). Compared to the less frequent and non-synoptic in-situ field sampling approach, the synoptic and frequent sampling facilitated by frequent remote sensing imagery of MODIS provides an improved assessment of TSS concentration and two-dimensional distribution patterns and is recommended to be used as a valuable tool for frequently monitoring coral reef water quality in coastal water bodies of China and other areas in the world if applicable.

To investigate the adjuvant potential of porcine IL-4 and IFN-γ in mice and pigs, the genes of porcine IL-4 and IFN-γ were cloned and the recombinant mammalian expression plasmids were constructed for in vivo expression of the cytokines. Adjuvant effects of recombinant expression plasmids of IL-4 and IFN-γ (pcDNA-IL-4, pcDNA-IFN-γ) co-administrated with Cysticercus cellulosae crude antigen or TSOL18 recombinant protein antigen have been carried out in mice and pigs, respectively. We have demonstrated that recombinant plasmids of the cytokines as an adjuvant could induce stronger immune response in mice and pigs. With the C. cellulosae parasite antigen, porcine pcDNA-IL-4 induced higher specific antibody of immunized mice than pcDNA-IFN-γ. But pcDNA-IFN-γ is significantly stronger than that of no adjuvant or empty plasmids with the antigen control group. For the TSOL18 recombinant protein antigen vaccine, pcDNA-IL-4 still had a stronger ability to enhance specific antibody in swine than pcDNA-IFN-γ (P < 0.01), but the immune protective rate was lower in challenged pigs (only 68.7%). Although pcDNA-IFN-γ showed lower specific antibody, the protection rate was very high (91%) than other group (P < 0.01). This study indicated that the recombinant expression plasmids of porcine IL-4 and IFN-γ display stronger adjuvant effects to C. cellulosae vaccine, further research should be carried out for understanding of the interaction mechanism.

The advent of Light Detection and Ranging (lidar) technique provides a promising resource for three-dimensional building detection. Due to the difficulty of removing vegetation, most building detection methods fuse lidar data with multispectral images for vegetation indices and relatively few approaches use only lidar data. However, the fusing process may cause errors introduced by resolution and time difference, shadow and high-rise building displacement problems, and the geo-referencing process. This research presents a morphological building detecting method to identify buildings by gradually removing non-building pixels. First, a ground-filtering algorithm separates ground pixels with buildings, trees, and other objects. Then, an analytical approach removes the remaining non-building pixels using size, shape, height, building element structure, and the height difference between the first and last returns. The experimental results show that this method provides a comparative performance with an overall accuracy of 95.46 percent as in a study site in Austin urban area.

Automatic ground filtering for Light Detection And Ranging (LIDAR) data is a critical process for Digital Terrain Model (DTM) and three-dimensional urban model generation. Although researchers have developed many methods to separate bare ground from other urban features, the problem has not been fully solved due to the similar characteristics possessed by ground and non-ground objects, especially on abrupt surfaces. Current methods can be grouped into two major categories: neighborhood-based approaches and directional filtering. In this study, following the direction of the second branch, we propose a new Multi-directional Ground Filtering (MGF) algorithm to incorporate a two-dimensional neighborhood in the directional scanning so as to prevent the errors introduced by the sensitivity to directions. Besides this, the MGF algorithm explores the utility of identifying pattern varieties in different directions across an image. The authors conducted a comprehensive test of the performance on fifteen study sites and compared our results to eight other publicized methods based on the Kappa coefficients calculated from the error matrices reported by ISPRS. Overall, the MGF filter produces a promising performance in both urban and forest areas. The size and shape of non-ground objects do not pose significant influence on the performance of the MGF algorithm. The fact that MGF algorithm is robust to two commonly required parameters, slope and elevation difference thresholds, has added practical merits to be adopted in different landscapes.

This research was to clone, express, and analyze the structure and function of major molecules of porcine interleukin family. Genes of porcine interleukin family were cloned by RT-PCR from stimulated porcine PBMC by LPS and PHA, and then expressed in E. coli, and the structure and function of these molecules were predicted by ExPASY. The results showed that genes of IL-4, IL-6, and IL-18 were successfully cloned and expressed. Furthermore, the expression products of recombinant IL-4 and IL-6 both have multiple biological activities. By analyzing these genes with the NCBI/GenBank data, the homologies of the nucleotide acid sequence are 99.25, 99.21, and 100%, respectively, and have great species differences when compared with other animal species. The results of the prediction showed that all these molecules contain several phosphorylation, glycosylation, protein kinase, and signal transduction bonding sites in secondary structure, and all are compact globularity protein in space configuration. These characteristics of structure are the basis for their multiple biological functions. The genes, structure and function of key molecular of porcine interleukin family were successfully cloned, expressed, and analyzed in this paper.