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In an effort to enhance the precision of wind power prediction, this study proposes a wind power prediction model with a secondary-weighted attention mechanism, which is based on feature selection. During the pre-processing stage, wavelet denoising is employed on the original dataset to eliminate noise and enhance the convergence rate of the model. As for model improvement, a secondary-weighted time attention mechanism-LSTM (STAM-LSTM) model is proposed. Additionally, the random forest algorithm is employed to analyse the feature correlation, leading to the best feature combination for the construction of the final input vector. In the comparative experiments, the STAM-LSTM model shows good performance and stability compared to the other nine models and three reference methods. In addition, to validate the effectiveness of feature selection, different combinations of features are entered for prediction. The results show that the model metrics reach a better level after feature selection. Finally, the effects of the STAM mechanism and the Random Forest feature screening assistance strategy, on the model are analysed through ablation experiments. [ABSTRACT FROM AUTHOR]

Forests play an important role in global ecosystems, but natural disasters such as drought and disease and pest-induced damages often affect growth of trees and even lead to tree death. There have been various vegetation indices (VIs) developed to detect the damages of trees using remote sensing technologies. However, developing an effective and accurate VI for detecting forest damages at an earlier stage is still challenging. In this study, a total of 32 target trees and 17 reference trees were selected from one of Chinese fir plantations that account for 24% of the afforested area in China. The barks of the target trees at 20 cm and 70 cm above the ground were respectively peeled off in August of 2016 to let the trees die. The hyperspectral data were collected from the selected branches with leaves using a high-performance spectrometer HR-1024i (Spectra Vista Corporation) with 1024 bands covering the region of wavelengths from 350 nm to 2500 nm. The spectral data collection was conducted from both the target and reference trees by non-imaging monthly from August of 2016 to February of 2017 until the target trees died. Based on statistical analysis, a Green to Red Region Spectral anGle Index (GRRSGI) was proposed and compared with other ten widely used VIs to detect the early dying process of the damaged trees. The results showed that compared with the reference trees, the damaged trees reduced the reflectance peaks of green bands and the absorption valleys of red bands and the frequency distributions of spectral reflectance from the damaged trees turned from statistically normal to non-normal in the region of 550–640 nm. This indicated that the spectral bands in the region of green to red bands were sensitive to the dying process of the target trees. Moreover, the performance of the VIs for detecting the dying process of the damaged trees varied depending on the time period after the trees damaged. The proposed GRRSGI based on the spectral bands of 550–640 nm led to the performance similar to all other VIs after three months of the damages but significantly better than them in the first two months after the damages. Except for GRRSGI, the Green-Red Spectral Area Index (GRSAI) also performed better than other VIs. Compared with GRSAI, however, the proposed GRRSGI performed slight better when the spectral data of 550–640 nm were re-sampled using the spectral intervals of 5–40 nm with an increase of 5 nm and significantly better when the re-sampling of the hyperspectral data was conducted in the region of 550–670 nm. This implied the proposed GRRSGI provided the greater potential of detecting the early dying process of the damage trees. [ABSTRACT FROM AUTHOR]

• A new DDES-Tr, based on the three-equation k-ω-γ transition/turbulence integrated model, was proposed through the similar way with the typical DDES. • A modification of the length scale was introduced to ensure the models worked in their own interest regions. • DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. The hypersonic flow at high angle of attack past the Orion capsule was predicted by a delayed detached eddy simulation (DDES), based on a three-equation k-ω-γ transition/turbulence integrated model (Tr). The computational results of the mean and instantaneous heat rates by DDES-Tr are compared with the available measurements and DDES-FT (full turbulence). As expected, the massive separation on the leeward side can be well predicted by both DDES models, but DDES-FT cannot model the transition and over-predicts the heat flux on the windward side. Fortunately, the DDES-Tr model can not only resolve the massive separation on the leeward side, but also model the transition on the windward side. Furthermore, the contributions of the first, second, and crossflow modes on the transition are quantitatively explored. Although the freestream is hypersonic, the second Mack mode is very weak owing to the low supersonic speed after the strong bow shock wave. The transition is mainly dominated by the first and rarely by the crossflow mode. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Novel phthalonitrile copoly-oligomers were synthesized by an improved method. • Oligomers exhibited good processability for prepreg and hot-press manufacture. • Glass transition temperatures of cured polymers were higher than 450 °C. • Bending strength reached 942 MPa, capable as structural composite matrices. Abstract A novel series of phthalonitrile terminated fluorene based copoly-oligomers (BPNs) were designed and synthesized by an improved method and could be cured in the presence of bis[4-(4-aminophenoxy)phenyl]sulfone. The structure of oligomers was characterized, while their curing behavior was systematically studied. The oligomers exhibited good solubility, which facilitated the preparation of prepregs. Later quartz fiber reinforced composites were prepared by hot-pressing process, and their thermal and mechanical properties were characterized. The glass transition temperatures of the composites were higher than 450 °C, and the bending strength reached 942 MPa. The combination of excellent thermal and mechanical performance indicated BPNs could be potentially applied in high performance composites or as universal modifiers for heat resistant resins. [ABSTRACT FROM AUTHOR]

Abstract Urban redevelopment practices have received substantial attention in urban planning. Remotely sensed thermal infrared monitoring of urban heat island (UHI) is a well-documented topic, however, there is a lack of understanding of the influence on UHI change caused by urban redevelopment process. The objectives of this study was to investigate the urban redevelopment-induced efforts incorporating remote sensing of UHI and land use change. Multi-temporal ASTER thermal infrared images were employed to characterize UHI change; and high resolution Worldview images were employed to perform land use classification. UHI dynamic was quantified with urban heat island ratio index. Analysis of urban redevelopment-induced land use change with response to UHI were carried out. Landscape ecology methods were employed to quantitatively identify land use change with landscape metrics. Result demonstrated that UHI effect had a trend of mitigation during urban redevelopment process in the study area. Urban heat island intensity could be significantly eliminated or weakened by changes of land use composition and spatial configuration. These phenomena were closely related to redevelopment practices such as industrial relocation, buildings demolition, and brownfield transformation. This article presented a case study for characterizing urban redevelopment process with remote sensing monitoring, quantifying the change with urban heat island ratio index and landscape ecology analysis, research findings could be utilized as indicators for urban planning. [ABSTRACT FROM AUTHOR]

Abstract The global navigation satellite system (GNSS) is presently a powerful tool for sensing the Earth's ionosphere. For this purpose, the ionospheric measurements (IMs), which are by definition slant total electron content biased by satellite and receiver differential code biases (DCBs), need to be first extracted from GNSS data and then used as inputs for further ionospheric representations such as tomography. By using the customary phase-to-code leveling procedure, this research comparatively evaluates the calibration errors on experimental IMs obtained from three GNSS, namely the US Global Positioning System (GPS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo. On the basis of ten days of dual-frequency, triple-GNSS observations collected from eight co-located ground receivers that independently form short-baselines and zero-baselines, the IMs are determined for each receiver for all tracked satellites and then for each satellite differenced for each baseline to evaluate their calibration errors. As first derived from the short-baseline analysis, the effects of calibration errors on IMs range, in total electron content units, from 1.58 to 2.16, 0.70 to 1.87, and 1.13 to 1.56 for GPS, Galileo, and BDS, respectively. Additionally, for short-baseline experiment, it is shown that the code multipath effect accounts for their main budget. Sidereal periodicity is found in single-differenced (SD) IMs for GPS and BDS geostationary satellites, and the correlation of SD IMs over two consecutive days achieves the maximum value when the time tag is around 4 min. Moreover, as byproducts of zero-baseline analysis, daily between-receiver DCBs for GPS are subject to more significant intra-day variations than those for BDS and Galileo. [ABSTRACT FROM AUTHOR]

Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%. [ABSTRACT FROM AUTHOR]

After considering the local effects of nose bluntness on the transition, a modification for the timescale of the second mode in the three-equation k-ω-γ transition model has been proposed to improve the hypersonic transition past cones. The ratio of local unit Reynolds number over freestream unit Reynolds number ( Re local / Re ∞ ) is introduced to delay and reduce the development of second mode instability, according to the results of linear stability theory. The hypersonic transitions past a cone with three nose radii at the angle of attack of 0° were simulated to calibrate the new model and its constants. Then, the improved model is extended to predict the hypersonic transitions past the cones with larger radius and at non-zero angles of attack. Despite the original model can reflect the transition trend, the improved model performs much better than the original one when comparing with the available measurements. [ABSTRACT FROM AUTHOR]

Abstract: A great challenge the US military land managers are often faced with is how to optimize road networks in order to maintain roads (including all roads, trails, and paths) for the purpose of military training and reducing negative impacts on environment. In this study, a methodology was developed to identify superfluous roads for being closed in terms of both sustainable military land carrying capacity and environment for Fort Riley. In this method, Geographic Information Systems (GIS), remote sensing, and landscape analysis technologies were combined to derive various spatial data layers of factors that had significant impacts on both military training and environment. The factors included maintenance cost of roads, road access area, military training intensity, soil erosion, water quality, landscape fragmentation, and noise production. The factors were quantified and normalized. A spatial multicriteria decision was then developed to obtain the weights of the factors, combine the data layers, and derive a priority map of all the roads for being closed. This map summarized the negative and positive impacts of the factors on environment and military land carrying capacity and can provide the US military land managers with useful guidelines and tools for determining superfluous roads in terms of both sustainable military training and environment. It is expected this effort can provide a method to quickly ascertain which roads are most cost-effective for being closed without hindering the mission and at the same time with benefits for environmental protection and thus provide the land managers with a comprehensive analysis and assessment of alternatives at their disposal. [Copyright &y& Elsevier]

Abstract: We assessed short-term impacts of changes in military training load on vegetative cover at Fort Hood, TX. From 1989 to 1995, permanent field transects were monitored for vegetative cover and land use disturbance using standard army monitoring methods [Land Condition Trend Analysis (LCTA)]. Land use intensity (training load) was quantified and used to develop a model to predict future vegetation cover values. We found that standard Army monitoring methods detected changes in installation resources associated with changes in training load. Increased training loads were associated with increased measures of disturbance, decreased ground cover, and decreased aerial vegetative cover. We found that the spatial pattern of disturbance and vegetation cover remained relatively constant over the study period despite large variations in overall training load. Our model used the consistency in spatial cover patterns over time and the strong relationship between training load and vegetation cover to predict the impact of future training loads on vegetation. [Copyright &y& Elsevier]

Soil erodibility (susceptibility of soil to be lost to erosion) is one of the components of the universal soil loss equation (USLE). In the USLE, erodibility is known as the K factor, which in turn is a function of these soil properties: particle size distribution, organic matter content, structure, and permeability. The traditional approach for estimating soil erodibility does not account for spatial variability of individual soil properties or spatial correlation among those properties. Our objectives in this study were to evaluate the use of joint sequential simulation for mapping soil erodibility, as well as to partition the individual and joint variance contribution of soil properties for predicting soil erodibility. We collected 192 usable soil samples across Fort Hood, Texas in the summer of 1999. For each of those samples, we obtained an estimate of particle size distribution, organic matter content, structure, permeability, and calculated soil erodibility. We carried out both independent and joint sequential simulation to generate spatially explicit predictions and variance of all soil properties as well as covariance between pairs of soil properties for each cell within our simulation area. We used the program GCOSIM3D to conduct those simulations. On average, joint sequential simulation resulted in a K factor variance of less than half the variance obtained from independent simulation. Using the results from joint sequential simulation, we partitioned the contribution of each soil property and pair of properties using first-order Taylor series expansion of the soil erodibility function. Individually, Very-Fine-Sand-and-Silt contributed the most (46.19%), whereas Structure contributed the least (6.53%) to the K factor variance. Jointly, Permeability/Structure contributed the most (9.32%), whereas Sand/Very-Fine-Sand-and-Silt caused the largest reduction (−19.19%) in the K factor variance. We conclude that joint sequential simulation provided approximately twice as much precision as independent simulation for the spatially explicit prediction of soil erodibility. Likewise, first-order Taylor series expansion offered an accurate approach for partitioning the individual and joint contribution of soil properties to soil erodibility variance. This partitioning allowed us to identify large sources of uncertainty and suggest efficient approaches for further improving the precision of K value predictions. [Copyright &y& Elsevier]

In the management of natural resources, multiple variables correlated with each other usually need to be mapped jointly. However, joint mapping and spatial uncertainty analyses are very difficult mainly because of interactions among variables and imperfection of existing methods. There is abundant evidence that considering interactions among variables and spatial information from neighbors can result in improved maps. This study presents a remote sensing-aided method for that purpose. The method is based on the integration of joint sequential co-simulation with Landsat TM image for mapping and polynomial regression for spatial uncertainty analysis. The method was applied to a case study in which ground cover (GC), canopy cover (CC), and vegetation height (VH) were jointly mapped to derive a map of the vegetation cover factor for predicting soil loss. The variance contributions from the variables, their interactions, and the spatial information from neighbors leading to uncertainty of predicted vegetation cover factor were assessed. The results showed that in addition to unbiased maps, this method reproduced the spatial variability of the variables and the spatial correlation among them, and successfully quantified the effect of variation from all the components on the prediction of the vegetation cover factor. [Copyright &y& Elsevier]

Soil loss is commonly predicted using the revised universal soil loss equation consisting of rainfall–runoff erosivity, soil erodibility, slope steepness and length, cover management, and support practice factors. Because of the multiple factors, their interactions, and spatial and temporal variability, soil erosion varies considerably over space and time. For these reasons, modeling soil loss is very complicated. Decision-makers need local and regional estimates of soil loss as well as their corresponding uncertainties. Neglecting the local and detailed information may lead to improper decision-making. This paper demonstrates a strategy based on a sample data set and a geostatistical method called sequential Gaussian simulation to derive local estimates and their uncertainties for the input factors of a soil erosion system. This strategy models the spatial and temporal variability of the factors and derives their estimates and variances at any unknown location and time. This strategy was applied to a case study at which the rainfall–runoff erosivity R factor was spatially and temporally estimated using a data set of rainfall. The results showed that the correlation between the observations and estimates by the strategy ranged from 0.89 to 0.97, and most of the mean estimates fell into their confidence intervals at a probability of 95%. Comparing the estimates of the R factor using a traditional isoerodent map to the observed values suggested that the R factor might have increased and a new map may be needed. The method developed in this study may also be useful for modeling other complex ecological systems. [Copyright &y& Elsevier]

To predict complex flow, including transition on the windward side and massive separation on the leeward side, a Reynolds averaged Navier Stokes-Large Eddy Simulation (RANS-LES) hybrid model based on the three-equation k - ω - γ transition model (RANS-LES-Tr) was developed. Then, it was applied to simulate the following three cases, such as the low-speed flow past a cylinder only with the first transition mode, the low-speed prolate spheroid flow at a high angle of attack (AoA) with both the first and crossflow modes; and hypersonic flow past the "Orion" capsule with the first, second and crossflow modes. RANS-LES-Tr shows much better agreements with available measurements, such as skin friction and heat flux on the windward side, than RANS-LES based on the full turbulence model. [ABSTRACT FROM AUTHOR]

Soluble hyperbranched poly(arylene ether nitrile)s end-capped by either hydroxyl (HBPAEN-OHs) or phthalonitrile groups (HBPAEN-PNs) were synthesized via nucleophilic substitution reactions, where activated carbon supported calcium oxide was used as the catalyst to avoid crosslinking at high conversion. In order to evaluate their potential application in phthalonitrile resins, these hyperbranched polymers were blended with 1,3-bis(3,4-dicyanophenoxy)benzene and 4-(4-aminophenoxy)phthalonitrile, then cured into polymers. Curing mechanisms of different prepolymers were proposed and compared, and processability, thermal and mechanical properties were characterized. The introduction of HBPAEN-OHs was found to postpone the curing, while thermal and mechanical properties were compromised. On the contrary, the usage of HBPAEN-PNs was beneficial to both processability and thermal properties. More importantly, the impact strength for polymers containing HBPAEN-PN could be as high as 12.92 kJ m−2, indicating such resin could be potentially applied as high temperature structural composite matrices. In addition, the toughening mechanism was proposed and discussed. Image 1 • Novel hyperbranched poly(arylene ether nitrile) modifiers were synthesized. • Phthalonitrile prepolymers were obtained by blending with these modifiers. • Prepolymers exhibited processability capable for RTM manufacture. • Toughening was realized at no cost of thermal properties. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Low melting dual-functional phthalonitrile monomers were synthesized. • Prepolymers exhibited processability capable for RTM manufacture. • Glass transition temperatures of cured polymers were higher than 470 °C. • Impact strength reached 13.63 kJ m−2, capable as structural composite matrices. Abstract Low melting phthalonitrile monomers containing allyl and/or methoxyl groups were designed, synthesized and characterized. In order to evaluate their potential application in phthalonitrile resins, these monomers were blended with 1,3-bis(3,4-dicyanophenoxy)benzene and 4-(4-aminophenoxy)phthalonitrile, then cured into polymers. Curing mechanisms of different prepolymers were proposed and compared, and processability, thermal and mechanical properties were characterized. The introduction of methoxyl units was found to postpone the curing, and thermal properties were slightly compromised. On the contrary, the usage of allyl was beneficial to both processability and thermal properties. More importantly, the impact strength for allyl-containing polymers could be as high as 13.63 kJ m−2, indicating such resin could be applied as high temperature structural composite matrices. [ABSTRACT FROM AUTHOR]

Charcoal rot caused by the fungal pathogen Macrophomina phaseolina is an important disease of soybean and the use of resistant cultivars is recommended to manage the disease. Since symptoms, including leaf wilt, typically occur as soybeans reach maturity, screening varieties for tolerance to charcoal rot can be time-consuming, requiring nearly an entire growing season. In this study, soybean seedlings (V1) were exposed to a culture filtrate of M. phaseolina containing toxin(s) produced by the fungal pathogen. The effect on the seedlings was measured with hyperspectral spectroscopy on leaves. Two spectrometers integrated with a fiber optic light source and a 6.35 mm diameter probe yielding 480–850 nm and 900–2400 nm ranges after preprocessing were used. The spectra of the untreated plants measured at 0 h, 4 h, 8 h, 12 h, and 24 h post-exposure to the fungal extract were nearly indistinguishable. In contrast, the toxin-treated plants had significantly different spectra from the untreated plants at each of the 4 h, 8 h, 12 h, and 24 h measurements. Reflectance increased in the NIR (900–2400 nm) region with extended exposure to the fungal extract. This change was most noticeable in the 1450 nm and 1940 nm wavebands. Across the spectra, the 24 h reflectance was significantly higher than that of 12 h, which was significantly higher than those of 8 h, 4 h, and 0 h. Jeffries-Matusita (JM) distance, quantifying class separability, was used as a feature selection method and the 24 h measurement had the highest JM distance values, which indicated good separability. Based on JM Distance the most sensitive wavebands were in the regions of 1370–2400 nm. A ratio of the reflectance at 0 h to reflectance at the other times for each of the wavebands was calculated. The ratio curves had two noticeable troughs centered on 1450 nm and 1940 nm, with respective ratios of 0.47 and 0.32 for the 24 h measurement. The 1940 nm ratio at 24 h was proposed as a relative measure of charcoal rot susceptibility of soybean varieties. A ratio of 1.0 indicated no susceptibility with lower ratios indicating greater susceptibility to charcoal rot toxin(s). This study has implications in terms of developing tools to screen for soybean varieties tolerant to charcoal rot and potentially for other biotic or abiotic factors that induce foliar wilting. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is an important soil property relating to soil formation, structure, and water-holding capacity. Remote sensing has been used for predicting SOC but remains a challenge due to the complex and indirect relationship between SOC and remote sensing variables. In this research we explored the approaches of predicting SOC distribution in a hickory plantation region using random forest (RF) and geographically weighted regression (GWR) based on Landsat and ancillary data, analyzed SOC spatial distribution and dynamic change between 2008 and 2013 through a thresholding approach, and examined major factors that resulted in SOC degradation in the young and mature hickory plantations using a logistic regression. The results showed that RF outperformed GWR in the prediction of SOC and provided stable and reliable SOC predictions with root mean squared errors of 4.6 g kg −1 in 2008 and 4.4 g kg −1 in 2013. A large area of hickory plantation was experiencing SOC decrease. The analysis of major factors causing SOC degradation indicated that steep slope and high proportion of silt component in soil resulted in SOC decrease in the young hickory plantations, and high elevation, high proportion of silt component in the soil, and the increase of soil fraction in the ground cover led to SOC decrease in the mature hickory plantations. This research provides valuable approaches to spatially predict SOC and identify major factors driving SOC degradation, which will be useful for adopting better measures to improve management of hickory plantations. [ABSTRACT FROM AUTHOR]

Considering the complexity of the urban rail transit system (URTS), it is necessary to systematically identify the critical factors and their relationships to avoid or prevent urban rail transit accidents. In response to the problems few previous studies have modeled networks for the whole system components of URTS and have focused too much on the identification of specific component states or behaviors without quantitative analysis of inter-component relationships. This paper proposed a risk network construction and analysis method based on complex network theory to identify the critical accident causative factors and their relationships of the URTS system. Firstly, the accident is defined as the combination of a sequence of risk incidents occurring in a certain order and their resulting consequences, and the definitions of risk point and risk incident were given. Secondly, a risk network construction and analysis method based on the combination of accident reports and complex network theory was proposed to obtain the critical factors and their relationships in the process of the accident. At last, this paper constructed the urban rail transit risk point set containing 39 risk points, built the risk network based on 201 accident reports of URTS in China and analyzed the critical risk points and risk relationships. The results show that this study can provide a new perspective for identifying the critical causative factors of URTS accidents and their relationships for practical application in risk analysis and accident prevention. • The new definitions of risk points and incidents are put forward. • A critical risk analysis framework are proposed by complex network theory. • Critical risk points and relationships are identified in the urban rail transit system by the risk network. [ABSTRACT FROM AUTHOR]

In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. [ABSTRACT FROM AUTHOR]

• CS IM-CT (collagen score of the tumor margin minus tumor center) was proposed. • Collagen score was analyzed using multiphoton imaging. • CS IM-CT was an effective prognostic predictor in advanced rectal cancer. • Nomogram with the CS IM-CT can individually predict prognosis in rectal cancer. Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients. A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The CS IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675–0.854) and an overall survival (OS) with AUROC of 0.822 (0.734–0.909). Additionally, the CS IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748–0.905) and an OS with AUROC of 0.882 (0.803–0.960). The CS IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS IM-CT can be used to accurately predict the individual prognosis of these patients. [ABSTRACT FROM AUTHOR]

Elevated expression and genetic aberration of IRTKS, also named as BAIAP2L1, have been observed in many tumors, especially in tumor progression. however, the molecular and cellular mechanisms involved in the IRTKS-enhanced tumor progression are obscure. Here we show that higher IRTKS level specifically increases histone H3 lysine 9 trimethylation (H3K9me3) by promoting accumulation of the histone methyltransferase SETDB1. Furthermore, we reveal that IRTKS recruits the deubiquitinase OTUD4 to remove Lys48-linked polyubiquitination at K182/K1050 sites of SETDB1, thus blocking SETDB1 degradation via the ubiquitin-proteasome pathway. Interestingly, the enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis leads to a general decrease in chromatin accessibility, which inhibits transcription of CDH1 encoding E-cadherin, a key molecule essential for maintaining epithelial cell phenotype, and therefore results in epithelial-mesenchymal transition (EMT) and malignant cell metastasis. Clinically, the elevated IRTKS levels in tumor specimens correlate with SETDB1 levels, but negatively associate with survival time. Our data reveal a novel mechanism for the IRTKS-enhanced tumor progression, where IRTKS cooperates with OTUD4 to enhance SETDB1-mediated H3K9 trimethylation that promotes tumor metastasis via suppressing E-cadherin expression. This study also provides a potential approach to reduce the activity and stability of the known therapeutic target SETDB1 possibly through regulating IRTKS or deubiquitinase OTUD4. • Higher levels of IRTKS increase H3K9me3 by promoting SETDB1 accuumulation. • IRTKS recruits the deubiquitinase OTUD4 to blocking SETDB1 degradation. • The enhanced IRTKS–OTUD4–SETDB1–H3K9me3 axis decreases chromatin accessibility, and inhibits E-cadherin transcription, resulting in EMT and tumor metastasis. • Clinically, elevated IRTKS levels in tumor correlate with SETDB1 levels, and negatively associate with survival time. [ABSTRACT FROM AUTHOR]

Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m -dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. [ABSTRACT FROM AUTHOR]

Abstract: Zoigê alpine wetland located at the Qinghai–Tibet Plateau, China, is the largest alpine wetland in the world and very sensitive to global climate changes. However, little is known of the dynamics of soil water soluble organic carbon (WSOC), light fraction organic carbon (LFOC), soil organic carbon (SOC) and its indicators in this alpine wetland. In this study, four wetland sites and one degraded wetland site in the Zoigê alpine wetland were selected to investigate the relationships of stable isotope and labile organic carbon dynamics with groundwater elevation or water table level. We also examine if the δ13C abundance, WSOC, and LFOC can be used as potential indicators of SOC dynamics in these wetland sites. The results showed that the plant dominant species in the degraded wetland site were different from those in the non-degraded wetland sites. Moreover, the declining water table did not significantly lower foliar δ13C, C and N concentrations and C/N ratio of dominant plant species, but led to a significant decrease of soil SOC, WSOC and LFOC concentrations and an increase of soil δ13C. In the wetland sites, SOC was significantly correlated with soil δ13C, WSOC, and LFOC. Our regression analysis showed that LFOC was the best potential indicator of SOC dynamics with WSOC and soil δ13C being less useful as indicators, respectively. In addition, the absolute slope coefficients of the regression models increased as the soil depth increased, which indicated that the SOC turnover increased with soil depth. [Copyright &y& Elsevier]

The degradation of permafrost in the Earth's cold regions due to climate warming and human activities is of increasing concern because of its impact on ecosystems and land management, thus, monitoring the degradation becomes critical. This study aimed to demonstrate a method in which Sentinel-1 DInSAR and MODIS data were combined to estimate and map seasonal and long-term land surface deformation (subsidence/uplifting) and permafrost active layer thickness (ALT) in Donnelly Training Area (DTA) in Alaska. The coherence (similarity) between the paired SAR images was analyzed for their applicability. A sensitivity analysis and accuracy assessment of the estimates were conducted to account for their quality. Results showed that the estimated seasonal subsidence mainly occurring in June and July was in the range of 0 to −0.43 m, while the estimated uplifting mainly happening from September to May of next year was as great as +0.34 m. Moreover, the estimated long-term (2015 to 2018) subsidence and uplifting were mostly distributed in the southern and northern parts of DTA, respectively. The spatially variable time delays led to the ALT estimates greater along the east river and in the west and south parts, and smaller in the north parts. At the significance level of 0.05, the coherence estimates of the paired images were significantly different from zero and compared with the referenced predictions from a widely used annual prediction model, the average residual of the ALT estimates did not significantly differ from zero at the significance level of 0.05. The spatial distributions of the uncertainties for the seasonal surface deformation estimates were similar to those of the input uncertainties from the estimates of model coefficients, the phase change, the modelling error, and the image pairs. Thus, the DInSAR image based method coupled with MODIS data offered the potential of mapping and monitoring the dynamics of permafrost environment for the cold regions such as DTA in which collecting field observations is difficult and costly. This study also enhanced understanding spatiotemporal variability of permafrost deformation in DTA and provided guidelines for developing a near real-time monitoring system of the permafrost environment. • Permafrost degradation due to climate warming and human activity is of great concern. • Land surface deformation of an installation in Alaska is mapped by SAR&MODIS images. • A quality assessment of the land surface deformation estimates is conducted. • The accuracy of the estimates is compatible with that from a widely used model. • This study provides guidelines for developing a near real-time monitoring system. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the geography of ecosystem service benefits, the journey from ecosystem source to human beneficiary, using a comparative case study of the 2555 ha Cache River wetlands in rural southern Illinois and the 362 ha Des Plaines River wetlands in urban northeastern Illinois. Applying log-linear and exponential distance decay functions from source ecosystems to residents of zip code units yields annual economic values for the Cache River of $28,258–$2,548,793,956 and for the Des Plaines of $531,926–$216,284,749, with the Cache having greater value at shallow rates of spatial discounting and the Des Plaines at steeper rates. These extraordinarily wide ranges in estimated value highlight the essential importance of spatial discounting in ecosystem service valuation and, to a lesser extent, the importance of the spatial unit to which they are applied. They also highlight the dearth of empirical knowledge on the geographic context of ecosystem service values and the need to address critical research questions. [ABSTRACT FROM AUTHOR]