Remotely sensed mid-channel bar dynamics in downstream of the Three Gorges Dam, China

The downstream reach of the Three Gorges Dam (TGD) along the Yangtze River (1560 km) hosts numerous mid-channel bars (MCBs). MCBs dynamics are crucial to the river’s hydrological processes and local ecological function. However, a systematic understanding of such dynamics and their linkage to TGD remains largely unknown. Using Landsat-image-extracted MCBs and several spatial-temporal analysis methods, this study presents a comprehensive understanding of MCB dynamics in terms of number, area, and shape, over downstream of TGD during the period 1985−2018. On average, a total of 140 MCBs were detected and grouped into four types representing small ( 2 km2), middle (2 km2 − 7 km2), large (7 km2 − 33 km2) and extra-large size (>33 km2) MCBs, respectively. MCBs number decreased after TGD closure but most of these happened in the lower reach. The area of total MCBs experienced an increasing trend (2.77 km2/yr, p-value 0.01) over the last three decades. The extra-large MCBs gained the largest area increasing rate than the other sizes of MCBs. Small MCBs tended to become relatively round, whereas the others became elongate in shape after TGD operation. Impacts of TGD operation generally diminished in the longitudinal direction from TGD to Hankou and from TGD to Jiujiang for shape and area dynamics, respectively. The quantified longitudinal and temporal dynamics of MCBs across the entire Yangtze River downstream of TGD provides a crucial monitoring basis for continuous investigation of the changing mechanisms affecting the morphology of the Yangtze River system

HMM-Based Dynamic Mapping with Gaussian Random Fields

This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments

General framework of quantum complementarity from a measurement-based perspective

One of the most remarkable features of quantum physics is that attributes of quantum objects, such as the wave-like and particle-like behaviors of single photons, can be complementary in the sense that they are equally real but cannot be observed simultaneously. Quantum measurements, serving as windows providing views into the abstract edifice of quantum theory, are basic tools for manifesting the intrinsic behaviors of quantum objects. However, quantitative formulation of complementarity that highlights its manifestations in sophisticated measurements remains elusive. Here we develop a general framework for demonstrating quantum complementarity in the form of information exclusion relations (IERs), which incorporates the wave-particle duality relations as particular examples. Moreover, we explore the applications of our theory in entanglement witnessing and elucidate that our IERs lead to an extended form of entropic uncertainty relations, providing intriguing insights into the connection between quantum complementarity and the preparation uncertainty.Comment: 13 pages (including 7 pages in the main text), 6 figure

Ginsenoside Rh2 inhibiting HCT116 colon cancer cell proliferation through blocking PDZ-binding kinase/T-LAK cell-originated protein kinase

AbstractBackgroundGinsenoside Rh2 (GRh2) is the main bioactive component in American ginseng, a commonly used herb, and its antitumor activity had been studied in previous studies. PDZ-binding kinase/T-LAK cell-originated protein kinase (PBK/TOPK), a serine/threonine protein kinase, is highly expressed in HCT116 colorectal cancer cells.MethodsWe examined the effect of GRh2 on HCT116 cells ex vivo. Next, we performed in vitro binding assay and in vitro kinase assay to search for the target of GRh2. Furthermore, we elucidated the underlying molecular mechanisms for the antitumor effect of GRh2 ex vivo and in vivo.ResultsThe results of our in vitro studies indicated that GRh2 can directly bind with PBK/TOPK and GRh2 also can directly inhibit PBK/TOPK activity. Ex vivo studies showed that GRh2 significantly induced cell death in HCT116 colorectal cancer cells. Further mechanistic study demonstrated that these compounds inhibited the phosphorylation levels of the extracellular regulated protein kinases 1/2 (ERK1/2) and (H3) in HCT116 colorectal cancer cells. In vivo studies showed GRh2 inhibited the growth of xenograft tumors of HCT116 cells and inhibited the phosphorylation levels of the extracellular regulated protein kinases 1/2 and histone H3.ConclusionThe results indicate that GRh2 exerts promising antitumor effect that is specific to human HCT116 colorectal cancer cells through inhibiting the activity of PBK/TOPK

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

Spanning the Yangtze River of China, the Three Gorges Dam (TGD) has received considerable concern worldwide with its potential impacts on the downstream side of the dam. This work investigated the spatio-temporal variations of suspended sediment concentration (SSC) at the downstream section of Yichang-to-Chenglingji from 2002 to 2015. A random forest model was developed to estimate SSC using MODIS ground reflectance products, and the spatio-temporal distributions of SSC were retrieved with this model to investigate the characteristics of water-silt variation. Our results revealed that, relatively, SSC before 2003 was evenly distributed in the downstream Yangtze River, while this spatial distribution pattern changed ce 2003 when the dam started storing water. Temporally, the SSC demonstrated a W-shaped curve of seasonal variation as one peak occurred in September and two troughs in March and November, and showed a significantly decreasing trend after three-stage impoundment. After official operation of the TGD in 2009, the SSC was reduced by over 40% than before 2003. Spatially, the most significant changes occurred in the upper Jingjiang section, where the SSC dropped by 45%. During all stages of impoundment, the water impoundment to 135 m in 2003 had the most significant impact on suspended sediment. The decreased SSC has led to emerging risks of bank failure, aggravated erosion of water front and aggressive down-cutting erosion along the downstream of the dam, as well as other ecological and environmental issues that require urgent attention by the government

Ensembles of multiple spectral water indices for improving surface water classification

Mapping surface water distribution and its dynamics over various environments with robust methods is essential for managing water resources and supporting water-related policy design. Thresholding Single Water Index image (TSWI) with a fixed threshold is a common way of using water index (WI) for mapping water for it is easy to use and could obtain acceptable accuracies in many applications. As more and more WIs are available and each has its distinct merits, the real-world application of TSWI, however, often face two practical concerns: (1) selection of an appropriate WI, and (2) determination of an optimal threshold for a given WI. These two issues are problematic for many users who rely either on trial-and-error procedures that are time-consuming or on their personal preferences that are somewhat subjective. To better deal with these two practical concerns, an alternative way of using WIs is suggested here by transforming the current paradigm into a simple but robust ensemble approach called Collaborative Decision-making with Water Indices (CDWI). A total of 145 subsite images (900 900 m) from 22 Landsat-8 OLI scenes that covering various water-land environments around the world were used to assess the performance of TSWI and the CDWI. Five benchmark WIs were adopted in five TSWI methods and CDWI method: Normalized Difference Water Index (NDWI), the Modified NDWI (MNDWI), the Automated Water Extraction Indices without considering (AWEI0) and with considering (AWEI1) shadows, and the state-of-the-art 2015 water index (WI2015). Two aspects of performance were analyzed: comparing their accuracies (indicated by both F1-scores and Youden’s Index) over various environments and comparing their accuracy sensitivities to threshold. The results demonstrate that CDWI produced higher accuracies than the other five TSWI methods for most application cases. Particularly, more samples (indicated by percentage) produced higher F1-scores by CDWI than the other five TSWI methods, i.e. 67% (CDWI) vs. 15% (TSWINDWI), 54% (CDWI) vs. 22% (TSWIMNDWI), 42% (CDWI) vs. 12% (TSWIAWEI0), 57% (CDWI) vs. 17% (TSWIAWEI1), and 34% (CDWI) vs. 12% (TSWIWI2015). Moreover, the F1-score of the CDWI is much less sensitive to the change of thresholds compared with that of the other five TSWI methods. These important benefits of CDWI make it a robust approach for mapping water. The uncertainty of CDWI method was thoroughly discussed and a general guidance (or look-up-table) for selecting WIs was also suggested. The underlying framework of CDWI could be readily generalizable and applicable to other satellite sensor images, such as Landsat TM/ETM+, MODIS, and Sentinel-2 images

Estimating seasonal aboveground biomass of a riparian pioneer plant community:An exploratory analysis by canopy structural data

The aboveground biomass (AGB) of vegetation is of central importance for ecosystem services by providing a measure of productivity. Models have been developed for estimating AGB via canopy structural variables in both fundamental and applied ecological studies. However, the potential of canopy structural variables for describing AGB dynamics throughout a growing season are still unclear. This study focuses on the AGB seasonal dynamics of a pioneer community, Cynodon dactylon (L.) Pers. (Bermuda grass), in a newly-formed riparian habitat at China’s Three Gorges Reservoir. The objectives are (1) to determine the most important structural variable for estimating AGB at different growing stages during the season, and (2) to develop a model that can estimate AGB at the different growing stages and using multiple structural variables. We sampled the C. dactylon community six times during the growing season from May to September 2016. Six variables were engaged in the analysis, including five canopy structural variables, i.e., canopy height (H), canopy cover (CC), leaf area index (LAI), the volume related variables VLAI (H × LAI) and VCC (H × CC), and one seasonal growth effect variable (SV). We conducted univariate linear regression analysis to determine the most important estimator of AGB and the best subset regression analysis were used to develop the AGB estimation model. The detected most important AGB estimator changed with different growing stages throughout a season. Canopy structural characteristics of the community are key factors for determining such changes. Cover was the most important variable for AGB estimation during the early growing season and VLAI was the most important variable in the mid and end of the growing season. The developed best multivariate models explained an additional 11% in AGB variance on average for the different growing stages compared with the univariate models using the most important estimators. SV was found to be useful in developing an acceptance general AGB estimation model appropriate for the entire growing season. The findings of this study are expected to provide knowledge for guiding sampling work and to assist with modeling AGB and understanding the AGB seasonal dynamics in the future

Genome-wide identification, expression analysis, and potential roles under low-temperature stress of bHLH gene family in Prunus sibirica

The basic helix-loop-helix (bHLH) family is one of the most well-known transcription factor families in plants, and it regulates growth, development, and abiotic stress responses. However, systematic analyses of the bHLH gene family in Prunus sibirica have not been reported to date. In this study, 104 PsbHLHs were identified and classified into 23 subfamilies that were unevenly distributed on eight chromosomes. Nineteen pairs of segmental replication genes and ten pairs of tandem replication genes were identified, and all duplicated gene pairs were under purifying selection. PsbHLHs of the same subfamily usually share similar motif compositions and exon-intron structures. PsbHLHs contain multiple stress-responsive elements. PsbHLHs exhibit functional diversity by interacting and coordinating with other members. Twenty PsbHLHs showed varying degrees of expression. Eleven genes up-regulated and nine genes down-regulated in −4°C. The majority of PsbHLHs were highly expressed in the roots and pistils. Transient transfection experiments demonstrated that transgenic plants with overexpressed PsbHLH42 have better cold tolerance. In conclusion, the results of this study have significant implications for future research on the involvement of bHLH genes in the development and stress responses of Prunus sibirica

Identification and characterization of class 1 integrons among Pseudomonas aeruginosa isolates from patients in Zhenjiang, China

SummaryObjectivesThe role of integrons in the spread of antibiotic resistance has been well established. The aim of this study was to investigate the resistance profiles of Pseudomonas aeruginosa isolated from patients in Zhenjiang to 13 antibiotics, and to identify the structure and dissemination of class 1 integrons.MethodsThe Kirby–Bauer disk diffusion assay was used to determine the rate of P. aeruginosa resistance. Class 1 integrons from multidrug-resistant isolates were amplified by PCR, and their PCR products were sequenced. We also analyzed the integron structures containing the same gene cassettes by restriction fragment length polymorphism (RFLP). Isolates were genotyped by pulsed-field gel electrophoresis (PFGE).ResultsThe resistance rates were between 29.6% and 90.1%. The prevalence of class 1 integrons was 38.0%. These integrons included five gene cassettes (aadB, aac6-II, blaPSE-1, dfrA17, and aadA5). The dfrA17 and aadA5 gene cassettes were found most often.ConclusionsClass 1 integrons were found to be widespread in P. aeruginosa isolated from clinical samples in the Zhenjiang area of China. The antibiotic resistance rates in class 1 integron-positive strains of P. aeruginosa were noticeably higher than those in class 1 integron-negative strains. PFGE showed that particular clones were circulating among patients