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Abstract: To prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades in the western Loess Plateau of China. In this paper, a case study was taken in Luoyu valley and Lver valley, two sub-watersheds of Xihe watershed and comparison was carried out between them. The main object of this study is to monitor land use/cover changes in the two similar small watersheds utilizing SPOT5 imageries by object-oriented human–computer interactive classification method, further develop the method of spatio-temporal analysis of land use/cover change by using pattern metrics of change trajectories and relative land use suitability index (R) in smaller watersheds, and make comparisons between the two similar small watersheds, taking water and soil conservation measures into consideration. Results show that combining GIS and RS, this method can be perfectly applied to make comparisons between different small watersheds with similar geographical backgrounds. And land use/cover spatiotemporal dynamic change characteristics can be preferably expressed by pattern metrics of change trajectories and R values based on topographical data. Different emphases have been laid according to their own geological backgrounds in the two watersheds and human activities have different effects on the landscapes of the two watersheds. The main change pattern is from slope farmland to terrace (322, the largest in Luoyu valley) or to economic fruit forest (344, the largest in Lver valley). R value of every slope grade in both of the two watersheds drops with the rising of slope degree on the whole and it shows that there is still much to do for people in the two watersheds in consideration that all the R values are still lower than 0.7. [Copyright &y& Elsevier]

Abstract: Urban management requires assessing bioterrorism response in micro-spatial environments, modeling potential attacks and carrying out efficient risk assessment. This paper presents the method of crowd evacuation simulation for bioterrorism in micro-spatial environments using the basic theory of Virtual Geographic Environments (VGE), combined with pathogen diffusion and crowd simulation modeling techniques. A framework of evacuation simulation was presented in this paper and the key technologies to implement the framework, including grid-based spatial data environment, agent-based individual model, and the method of associated individual behavior with spatial environment were also proposed to simulating crowd behaviors in bioterrorism. Taking the Olympic Park station of the Beijing Metro Line 8 as a typical study area, a crowd evacuation simulation prototype system was developed and a quasi-experiment using three different scenarios was conducted. The prototype system can be used to demonstrate some emergent individual behaviors in bioterrorism, such as competitive, queuing, and herding. Details can be obtained at each computation step (e.g., the number of people in subway station, the region of population, the geo-location of individual, the exposed population in polluted areas) with the prototype system. In the experiments, the number of people exposed is constantly increasing with the gradually increase of contaminant area as time pass. And different scenes were appeared for the different way of deal with bioterrorism. With the experiment, the number of people affected and the relevant parameters under various bioterrorism scenarios can be estimated, which can support rapid and efficient emergency decision-making for bioterrorism prevention. [Copyright &y& Elsevier]

The Loess Plateau suffers the most serious soil erosion in China. Sloping cultivated land is one of the most common land types in the region, and it leads to severe soil erosion. Analyses based on fine resolution satellite imagery can play a key role in the surveying of sloping farmland. In this article, a combination of remote-sensing (RS) and geographical information system (GIS) techniques under the hierarchical classification framework is used to investigate the sloping cultivated land in the Xi–He ecological engineering demonstration region of the Loess Plateau. This article synthetically adopts Système Pour l'Observation de la Terre 5 (SPOT-5) high-resolution images, Thematic Mapper (TM) multi-spectral images, and a terrain map at a scale of 1:10,000 to calculate the spectral, textural, and slope attributes of the sloping land type. A Bayesian classification method is employed to distinguish the crop and non-crop areas with a priori knowledge provided by local experts. The Gabor filter and the grey-level co-occurrence matrices (GLCM) method are used in combination under a man–machine interactive interpretation to extract the differences of texture trends and density distributions between sloping cultivated land and terraces, and a window-based method of texture analysis operations is adopted. A classification accuracy assessment by field survey shows that the total interpretation accuracy exceeds 80%. The sloping cultivated land in the research region covers an area of 233.06 km2, and the sub-watersheds, such as the Yangliu watershed, need key management in the future. [ABSTRACT FROM AUTHOR]

To avoid crowd evacuation simulations depending on 2D environments and real data, we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning (DRL) and 3D physical environments (3DPEs). In 3DPEs, we construct simulation scenarios from the aspects of geometry, semantics and physics, which include the environment, the agents and their interactions, and provide training samples for DRL. In DRL, we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy. With a unified configuration, we conduct evacuation simulations. In scenarios with one exit, we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics (number, mass and height) on evacuation. In scenarios with two exits and a uniform (nonuniform) distribution of agents, we explore the impact of exit characteristics (width and relative position) and agent characteristics (height, initial location and distribution) on agent exit selection and evacuation. Overall, interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation. [ABSTRACT FROM AUTHOR]

Abstract: Because of the uncertainties and complexities of the factors involved in causing landslides, it is generally difficult to analyze their influences quantitatively and to predict the probability of landslide occurrence. In this work, a hybrid method based on Bayesian network (BN) is proposed to analyze earthquake-induced landslide-causing factors and assess their effects. Our study area is Beichuan, China, where landslides have occurred in recent years, including mass landslides triggered by the 2008 Wenchuan earthquake. To provide a robust assessment of landslide probability, key techniques from landslide susceptibility assessment (LSA) modeling with BN are explored, including data acquisition and processing, BN modeling, and validation. In the study, eight landslide-causing factors were chosen as the independent variables for BN modeling. And this study shows that lithology and Arias intensity are the major factors affecting landslides in the study area. On the basis of the a posteriori probability distribution, the occurrence of a landslide is highly sensitive to relief amplitudes above 116.5m. Using a 10-fold cross-validation and a receiver operating characteristic (ROC) curve, the resulting accuracy of the BN model was determined to be 93%, which demonstrates that the model achieves a high probability of landslide detection and is a good alternative tool for landslide assessment. [Copyright &y& Elsevier]

Abstract: Human-induced land use/cover change has been considered to be one of the most important parts of global environmental changes. In loess hilly and gully regions, to prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades. The main objective of this study is to quantify the spatio-temporal variability of land use/cover change spatial patterns and make preliminary estimation of the role of human activity in the environmental change in Xihe watershed, Gansu Province, China. To achieve this objective, the methodology was developed in two different aspects, that is, (1) analysis of change patterns by binary image of change trajectories overlaid with different natural geographic factors, in which Relative Change Intensity (RCI) metric was established and used to make comparisons, and (2) analysis based on pattern metrics of main trajectories in the study area. Multi-source and multi-temporal Remote Sensing (RS) images (including Landsat ETM+ (30 June 2001), SPOT imagery (21 November 2003 and 5 May 2008) and CBERS02 CCD (5 June 2006)) were used due to the constraints of the availability of remotely sensed data. First, they were used to extract land use/cover types of each time node by object-oriented classification method. Classification results were then utilized in the trajectory analysis of land use/cover changes through the given four time nodes. Trajectories at every pixel were acquired to trace the history of land use/cover change for every location in the study area. Landscape metrics of trajectories were then analyzed to detect the change characteristics in time and space through the given time series. Analysis showed that most land use/cover changes were caused by human activities, most of which, under the direction of local government, had mainly led to virtuous change on the ecological environments. While, on the contrary, about one quarter of human-induced changes were vicious ones. Analysis through overlaying binary image of change trajectories with natural factors can efficiently show the spatio-temporal distribution characteristics of land use/cover change patterns. It is found that in the study area RCI of land use/cover changes is related to the distance to the river line. And there is a certain correlation between RCI and slope grades. However, no obvious correlation exists between RCI and aspect grades. [Copyright &y& Elsevier]

The Loess Plateau located in the upper and middle reaches of the Yellow River is the most serious soil erosion region in China. Check-dams (also known as silt trappers) have been demonstrated to be an effective soil and water conservation measure in the region and a traditional workflow for check-dam planning and engineering is time-consuming and cannot meet the requirement of efficiently locating optimal check-dam sites. Fine resolution satellite imagery and analysis can play a key role in screening and determining special river bed segments that can be candidates for check-dam sites. In this research, HongShiMao watershed of the Loess Plateau was selected as our case study area. Based on a detailed analysis of spectral characteristics of a fused SPOT-5 imagery for dominant land covers and geomorphological features of a constructed digital elevation model, the river bed of key channels within the watershed was automatically identified. Then we selected four check-dam sites on the river bed and four orientations of a check-dam site to explore locational and directional profiles. Such profile information is most useful for locating optimal check-dam sites in a cost-effective manner and reducing associated expenditures surrounding check-dam constructions. This remote sensing application demonstrates the latest spatial information technologies such as fine resolution satellite imagery and 3D geospatial visualisation hold promises for changing traditional workflows and advancing scientific decision making of environmental conservation projects. [ABSTRACT FROM AUTHOR]

Chemoresistance has been associated with increased reliance on mitochondrial functions in many cancers, including lung cancer. Atovaquone is an anti-malaria drug and mitochondrial inhibitor. In this work, we attempted to explore whether atovaquone can be repurposed for lung cancer treatment to overcome chemoresistance. We showed that atovaquone inhibited proliferation, colony formation and survival in non-small cell lung cancer cell (NSCLC) cells. Of note, the effective dose of atovaquone was clinically achievable. Combination index value indicated that atovaquone and carboplatin were synergistic in inhibiting NSCLC. The potent efficacy of atovaquone and its synergism with chemotherapeutic drug were also demonstrated in NSCLC xenograft mice model. Mechanism studies showed that the synergism between atovaquone and carboplatin was due to atovaquone's ability in disrupting mitochondrial functions via specifically inhibiting complex III induced oxygen consumption. Subsequently, atovaquone activated AMP-activated protein kinase (AMPK) and inhibited mammalian target of rapamycin (mTOR) signaling. AMPK inhibition reversed the anti-NSCLC activity of atovaquone, suggesting that the action of atovaquone is also dependent on AMPK. Our work suggests that atovaquone is an attractive candidate for NSCLC treatment. Our findings emphasize that inhibition of mitochondrial function is a promising therapeutic strategy to enhance NSCLC chemosensitivity. [ABSTRACT FROM AUTHOR]

Mucosa-associated microbiota from different regions of the gastrointestinal (GI) tract of adult broilers was studied by analysis of 16S rRNA gene sequences. The microbiota mainly comprised Gram-positive bacteria along the GI tract. Fifty-one operational taxonomic units (OTUs) (from 98 clones) were detected in the ceca, as compared with 13 OTUs (from 49 clones) in the crops, 11 OTUs (from 51 clones) in the gizzard, 14 OTUs (from 52 clones) in the duodenum, 12 OTUs (from 50 clones) in the jejunum and nine OTUs (from 50 clones) in the ileum. Ceca were dominantly occupied by clostridia-related sequences (40%) with other abundant sequences being related to Faecalibacterium prausnitzii (14%), Escherichia coli (11%), lactobacilli (7%) and Ruminococcus (6%). Lactobacilli were predominant in the upper GI tract and had the highest diversity in the crop. Both Lactobacillus aviarius and Lactobacillus salivarius were the predominant species among lactobacilli. Candidatus division Arthromitus was also abundant in the jejunum and ileum. [ABSTRACT FROM AUTHOR]

Abstract: The dynamic rheological properties and morphology in the vicinity of phase-separated region for poly(methyl methacrylate) (PMMA)/poly(styrene-co-acrylonitrile) (SAN) blends with lower critical solution temperature (LCST) behavior were investigated. When temperature was above the phase separation temperature, i.e. cloud point (Tc) for some PMMA/SAN blends, the slope of plotting versus decreased at low frequencies (terminal region), indicating the appearance of phase-separation and existence of heterogeneous structure. We employed a model dealing with complex modulus of the two phases mixture proposed by Kopnistos et al. for describing the dynamic rheological behaviors of PMMA/SAN blends, according to the assumption that the interfacial tension between the matrix and the dispersed phase was independent of local shear and variation of interfacial area, and that the dispersed spherical droplets were nearly monodispersed. It is found that the predicted results were in qualitative agreement with the experimental data of this study. The ratio of interfacial tension α to the size of dispersed phase R, α/R, was obtained for 80/20 and 60/40 PMMA/SAN blends, and the two different morphology were also observed. [Copyright &y& Elsevier]

Cell lysis efficiency and the quality of DNA extracts from complex bacterial ecosystems are two major concerns in molecular ecological studies of gut microbiota. In this study, we use PCR-denaturing gradient gel electrophoresis (DGGE) DNA profiling, random cloning and sequence analysis of 16S rRNA genes to compare the QIAamp® DNA Stool Mini Kit with the bead beating technique in the preparation of DNA extracts from gut microbiota of pigs. We also developed a washing procedure that can release more than 93% of bacterial cells attached to the gut mucosa. Both the QIAamp kit and bead beating method lysed approximately 95% of bacterial cells. PCR-DGGE DNA profiles of ileal and cecal microbiota from both digesta and mucosa that were generated from the DNA extracts using the two methods were nearly identical. Random cloning and sequence analysis also demonstrated the high quality of DNA extracts using the two methods. Two random clone sets of 16S rRNA genes generated from the DNA extracts had a similar degree of bacterial diversity. Different preparations of DNA extract from a single sample using the QIAamp kit consistently produced similar PCR-DGGE DNA profiles with similarity indexes higher than 99%. Our data suggest the appropriateness of the QIAamp® DNA Stool Mini Kit for the studies of gut microbial ecology and the effectiveness of the QIAamp kit in processing multiple samples for cell lysis and DNA extraction. [Copyright &y& Elsevier]

Bacterial populations in the ileum of broiler chickens were analyzed by molecular analysis of 16S rRNA genes and compared to those in the cecum. Bacteria found in the ileal mucosa were mainly Gram-positive with low G+C content. There were 15 molecular species among 51 cloned sequences. More than 70% of the cloned sequences were related to lactobacilli and Enterococcus cecorum. Two sequences had 95% or less homology to existing database sequences. Terminal restriction fragment length polymorphism (T-RFLP) analysis revealed differences among bacterial populations present in the mucosa and lumen of the ileum. Comparative studies by T-RFLP and sequence analyses of 16S rRNA genes indicated a less diverse bacterial population in the ileum (mucosa and lumen) than in the cecum. Lactobacilli, E. cecorum, and butyrate-producing bacteria related (including both identified and unidentified species) sequences were the three major groups detected in ilea and ceca. Although butyrate-producing bacteria may have good potential in the development of novel probiotics for poultry, verifying the presence of the bacteria in the chicken gut is required to warrant further investigation. [Copyright &y& Elsevier]

We reported the first attempt to describe mucosa-associated bacterial populations in the chicken ceca by molecular analysis of 16S rRNA genes. Bacteria in the mucosa were highly diverse but mainly Gram-positive with low G+C. Fusobacterium prausnitzii and butyrate-producing bacteria comprised the largest groups among 116 cloned sequences. Twenty five percent of the clones had less than 95% homology to database sequences. Many sequences were related to those of uncultured bacteria identified in human feces or the bovine rumen. Terminal restriction fragment length polymorphism (T-RFLP) analysis revealed some differences between bacterial populations present in the mucosa and lumen of ceca. Greater resolution of bacterial population was obtained using a culture-independent approach rather than a culture-based approach. [Copyright &y& Elsevier]

The word "symmetry" is a Greek word that originated from "symmetria". It means an agreement in dimensions, due proportion, and arrangement; however, in complex analysis, it means objects remaining invariant under some transformation. This idea has now been recently used in geometric function theory to modify the earlier classical q-derivative introduced by Ismail et al. due to its better convergence properties. Consequently, we introduce a new class of analytic functions by using the notion of q-symmetric derivative. The investigation in this paper obtains a number of the latest important results in q-theory, including coefficient inequalities and convolution characterization of q-symmetric starlike functions related to Janowski mappings. [ABSTRACT FROM AUTHOR]

Assessing short-term exposure to PM 2.5 requires the concentration distribution at a high spatiotemporal resolution. Abundant researches have derived the daily predictions of fine particles, but estimating hourly PM 2.5 is still a challenge restrained by the input data. The recent aerosol optical depth (AOD) product from Himawari-8 provides hourly satellite observations informative to modelling. In this study, we developed separate random forest models with and without AOD and combined the estimates to obtain a full-coverage hourly PM 2.5 distribution. 10-fold cross validation R2 ranged from 0.92 to 0.95 and root mean square errors from 14.1 to 16.9 μg/m3, indicating the good model performance. Spatial convolutional layers of PM 2.5 measurements and temporal accumulation effects of meteorological features were added into the model. They turned out to be of the most important predictors and improved the performance significantly. Finally, we mapped hourly PM 2.5 at a 1-km resolution in Beijing during a pollution episode in 2019 and studied the pollution pattern. The study proposed a method to obtain 24-h full-coverage hourly PM 2.5 estimates which are useful for acute exposure assessment in epidemiological researches. Unlabelled Image • Separate random forest models were built with and without AOD to obtain full-coverage hourly PM 2.5 estimates. • The use of spatial autocorrelation and temporal accumulation improved the models significantly. • Himawari-8 AOD worked on the spatial trends explanation and was important to estimates in remote regions. [ABSTRACT FROM AUTHOR]

• We extend the double exchange model of the skyrmion crystal by taking into account the next-nearest-neighbor hopping. • Techniques to solve the Hamiltonian within and without the strong coupling limit are elaborated. • Numerical results of the bulk Chern number and nanoribbon edge states are obtained. • Topological phase transitions driven by the topological number change and next-nearest-neighbor hopping are discussed. By considering the topological Hall conductivity and the nanoribbon edge states, i.e., the bulk-boundary correspondence of the skyrmion crystal (SkX), this work focused on the relation of the topological properties to the next-nearest-neighbor (nnn) hopping (t ′), the exchange coupling strength (J), and the topological number of the skyrmion (Q sk). When the nnn hopping of the electrons coexists with the nearest-neighbor hopping, the flux pattern of the emergent magnetic field traversing the SkX is reshaped giving rise to different topological Hall effect. By increasing t ′ from 0 to t , we found the Chern numbers of the electronic bands change from unity to indefinite values demonstrating a phase transition. By continually increasing Q sk from 1 to 2, we found topological phase transitions occur at Q sk = 1.3 and 1.7. We also considered the effect of finite J and found the results obtained in the adiabatic limit (J ≫ t) hold to around J = t. [ABSTRACT FROM AUTHOR]

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively. [ABSTRACT FROM AUTHOR]

Evaluates cytokine production by mycobacterium tuberculosis-stimulated peripheral blood mononuclear cells from children with tuberculosis and children who were healthy tuberculin reactors. Relationship between interferon gamma production in patients and radiographic extent of disease and nutritional status; Influence of changes in messenger RNA expression on interferon gamma production.

Describes the design and test of oligonucleotide probes covering three phylogenetically defined groups of Butyrivibrio fibrisolvens. Probe design and specificity studies; Hybridization to RNA isolated from the rumen; Phylogenetic placement; Use of probes in community-based studies.

Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground. [ABSTRACT FROM AUTHOR]

Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning. [ABSTRACT FROM AUTHOR]

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method's ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents' navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions. [ABSTRACT FROM AUTHOR]

Visual localization nowadays is a research hotspot in computer vision and photogrammetry. It can provide meter level or higher localization accuracy under the conditions without GPS signals. However, achieving efficient, robust and high-accuracy visual localization under the condition of day-night changes is still challenging. To deal with this problem, we develop an improved lightweight deep neural network with knowledge distillation to efficiently extract deep local features from imagery while maintaining strong robustness for day-night visual localization. Furthermore, to further improve the accuracy of visual localization, we use aligned dense LiDAR point clouds and imagery collected by a new portable camera-LiDAR integrated device to build a prior map, and directly utilize the 2D-3D correspondences between 2D local feature points extracted by our lightweight network and 3D laser points retrieved from the prior map for localization. Moreover, we build our own ground truth point cloud dataset at 5 cm accuracy to evaluate the accuracy of the constructed prior map as well as a day-night dataset including prior map and verification data for the evaluation of the proposed visual localization method. The experimental results prove that our visual localization method achieves a balance between the efficiency and robustness while improving localization accuracy for day-night visual localization. In a comparison with a variety of state-of-the-art local feature extraction methods based on deep neural networks, our lightweight network has the least number of parameters (0.2 million) and reaches the highest feature extraction efficiency (92 frames per second), which is on par with that of the classic real-time ORB feature extraction method. Furthermore, our network remains competitive with other advanced deep local feature extraction networks in feature matching and day-night visual localization. In addition, evaluations performed on our own dataset demonstrate that our visual localization method using images and LiDAR point clouds provides a localization error of 1.2 m under the conditions of day-night changes, which is much smaller than those achieved by a state-of-the-art, purely visual localization method. [ABSTRACT FROM AUTHOR]

In order to study the crowd evacuation process in complex subway stations, this paper proposes a social group perception evacuation model based on the social group evacuation model and the perception evacuation model, combined with the spatial index algorithm and the social group random algorithm, and takes a real subway station as an example to simulate the crowd evacuation process. Through the simulation results, the evacuation bottlenecks in the subway station are analyzed and the influence of social groups on evacuation efficiency is also verified. The results show that subway gates, fenced aisles and exits are important bottlenecks for crowd evacuation in subway stations, and the social groups will reduce the evacuation efficiency. This model can more truly restore the interaction behavior between evacuees and the environment in the evacuation process, and is suitable for the real complex subway stations. • Bottlenecks that affecting evacuation efficiency in subway stations mainly include subway gates, fenced aisles and exits. • Social groups have an important effect on evacuation efficiency. • The variation trends of panic index of individuals and social groups under different desired velocities are basically the same. [ABSTRACT FROM AUTHOR]

To describe the latest disease burden, temporal trends, and risk factors of depressive disorders among young people. Data from the Global Burden of Disease Study 2019 was utilized to analyze depressive disorders among individuals aged 10–24 years. The study focused on describing the incidence, prevalence, disability-adjusted life years (DALYs), and their attributable risk factors across 204 countries and territories from 2010 to 2019. The estimated annual percentage change (EAPC) was calculated to quantify the temporal trends. Globally, the incidence, prevalence, and DALYs rate of depressive disorders per 100 000 young people increased from 3003.01, 2445.69, and 448.61 in 2010 to 3035.26, 2470.67, and 452.58 in 2019, indicating a slight upward trend (EAPC = 0.11 for incidence and prevalence; EAPC = 0.09 for DALYs rate). Notably, the percentage of DALYs of depressive disorders among young people increased substantially from 3.24% in 2010 to 3.66% in 2019, an increase of 13.06% (EAPC = 1.26, 95%CI: 1.08–1.44), and the burden of depressive disorders among young people rose from fouth to second in females, and from tenth to fifth in males. Social demographic index (SDI) and other indicators were positively correlated with the percentage of DALYs of depressive disorder and negatively correlated with the EAPC of DALYs. The global burden of depressive disorders among young people is on the rise. The regional differences in depressive disorders among young people suggest the need for enhanced screening efforts in low-SDI areas, along with the adoption of more effective prevention and control measures. [Display omitted] • Global DALYs rate of depressive disorders in young people slightly rose, 2010–2019. • Percentage of DALYs due to depressive disorders in young people rose 13%, 2010–2019. • In 2019, depressive disorder burden climbed: 2nd in young females, 5th in young males. • Depressive disorders burden in young people was related to Universal Health Coverage. [ABSTRACT FROM AUTHOR]

Wheat is a major staple food crop in China. Accurate and cost-effective wheat mapping is exceedingly critical for food production management, food security warnings, and food trade policy-making in China. To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping, we present a novel approach that combines a random forest (RF) classifier with multi-sensor and multi-temporal image data. This study aims to (1) determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping, (2) to find out whether the proposed approach can provide improved performance over the traditional classifiers, and (3) examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data. Winter wheat mapping experiments were conducted in Boxing County. The experimental results suggest that the proposed method can achieve good performance, with an overall accuracy of 92.9% and a kappa coefficient (κ) of 0.858. The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%. The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods. We conclude that the proposed approach can provide accurate delineation of winter wheat areas. [ABSTRACT FROM AUTHOR]

Landslides are one of the main geological disasters in the riparian zone of the Three Gorges Reservoir Area (TGRA) in China, causing massive losses in terms of industrial and agricultural production and people's lives and property. Due to the difficulty of data collection and lack of facade information, the remote sensing methods currently used for landslide mapping and detection are insufficient. The current landslide information acquisition method is mainly based on visual interpretation and automatic computer classification from aerial orthophotographs, which is subjective and time-consuming and yields low accuracy. New shipborne photogrammetry has unique advantages in small and steep slope landslide detection. In recent years, the deep learning technology represented by convolutional neural networks (CNNs) exhibits more powerful feature learning ability in image recognition than traditional machine learning technology. Meanwhile, transfer learning can make the network suitable for small datasets through fine-tuning, which could transfer the parameters of the pretrained model to the new model in a certain way, thereby increasing the learning efficiency of the model. In this paper, the training of multiple deep learning models is realized through transfer learning, and automatic identification of landslides based on shipborne images is achieved through decision-level fusion. The experimental results show that through the integration of decision-level fusion and transfer learning, it performs well in the classification of landslides based on shipborne images. [ABSTRACT FROM AUTHOR]

Due to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model's spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. [ABSTRACT FROM AUTHOR]

Oblique airborne photogrammetry-based three-dimensional (3D) city model (OAP3D) provides a spatially continuous representation of urban landscapes that encompasses buildings, road networks, trees, bushes, water bodies, and topographic features. OAP3D is usually present in the form of a group of unclassified triangular meshes under a multi-resolution data structure. Modifying such a non-separable landscape constitutes a daunting task because manual mesh editing is normally required. In this paper, we present a systematic approach for easily embedding user-generated content into OAP3D. We reduce the complexity of OAP3D modification from a 3D mesh operation to a two-dimensional (2D) raster operation through the following workflow: (1) A region of interest (ROI) is selected to cover the area that is intended to be modified for accommodating user-defined content. (2) Spatial interpolation using a set of manually controlled elevation samples is employed to generate a user-defined digital surface model (DSM), which is used to reform the ROI surface. (3) User-generated objects, for example, artistically painted road textures, procedurally generated water effects, and manually created 3D building models, are overlaid onto the reformed ROI. [ABSTRACT FROM PUBLISHER]

The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people's wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic cancer is a malignant tumor with a high degree of malignancy, strong heterogeneity, and high lethality. Trop2 is a transmembrane glycoprotein associated with the occurrence, development, and poor prognosis of pancreatic cancer. This study aims to develop 64Cu/177Lu-labeled anti-Trop2 monoclonal antibody (hIMB1636) for positron emission tomography (PET) imaging and radioimmunotherapy (RIT) application in pancreatic cancer tumor models. Methods: The binding kinetics of hIMB1636 to Trop2 antigen was measured by Biolayer interferometry (BLI). Western blotting was used to screen the Trop2 expression of pancreatic cancer cell lines. Flow cytometry and cell immunofluorescence were used to evaluate the binding ability of hIMB1636 and Trop2 on the cell surface. hIMB1636 were conjugated with p-SCN-Bn-NOTA (NOTA) and DOTA-NHS-ester (DOTA) for 64Cu and 177Lu radiolabeling respectively. ImmunoPET imaging and RIT studies were performed using 64Cu-NOTA-hIMB1636 and 177Lu-DOTA-hIMB1636 in subcutaneous pancreatic cancer tumor models. Results: hIMB1636 had a strong binding affinity to Trop2 according to the results of BLI. The T3M-4 cell line showed the strongest expression of Trop2 and specific binding ability of hIMB1636 according to the results of Western blotting, flow cytometry, and cell immunofluorescence. The radiochemical purity of 64Cu-NOTA-hIMB1636 and 177Lu-DOTA-hIMB1636 exceeded 95%. PET imaging showed gradually an accumulation of 64Cu-NOTA-hIMB1636 in T3M-4 tumor models. The maximum tumor uptake was 8.95 ± 1.07%ID/g (n = 4) at 48 h post injection (p.i.), which had significant differences with T3M-4-blocked and PaTu8988-negative groups (P < 0.001). The high-177Lu-hIMB1636 group demonstrated the strongest tumor suppression with standardized tumor volume about 94.24 ± 14.62% (n = 5) at 14 days p.i., significantly smaller than other groups (P < 0.05). Ex vivo biodistribution and histological staining verified the in vivo PET imaging and RIT results. Conclusions: This study demonstrated that 64Cu/177Lu-labeled hIMB1636 could noninvasively evaluate the expression level of Trop2 and inhibit the Trop2-overexpressed tumor growth in pancreatic cancer tumor models. Further clinical evaluation and translation of Trop2-targeted drug may be of great help in the stratification and management of pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

In the current work, by using the familiar q-calculus, first, we study certain generalized conic-type regions. We then introduce and study a subclass of the multivalent q-starlike functions that map the open unit disk into the generalized conic domain. Next, we study potentially effective outcomes such as sufficient restrictions and the Fekete–Szegö type inequalities. We attain lower bounds for the ratio of a good few functions related to this lately established class and sequences of the partial sums. Furthermore, we acquire a number of attributes of the corresponding class of q-starlike functions having negative Taylor–Maclaurin coefficients, including distortion theorems. Moreover, various important corollaries are carried out. The new explorations appear to be in line with a good few prior commissions and the current area of our recent investigation. [ABSTRACT FROM AUTHOR]

Many diverse subclasses of analytic functions, q-starlike functions, and symmetric q-starlike functions have been studied and analyzed by using q-analogous values of integral and derivative operators. In this paper, we define a q-analogous value of differential operators for harmonic functions with the help of basic concepts of quantum (q-) calculus operator theory; and introduce a new subclass of harmonic functions associated with the Janowski and q-Mittag–Leffler functions. We obtain several useful properties of the new class, such as necessary and sufficient conditions, criteria for analyticity, compactness, convexity, extreme points, radii of starlikeness, radii of convexity, distortion bounds, and integral mean inequality. Furthermore, we discuss some applications of this study in the form of some results and examples and highlight some known corollaries for verifying our main results presented in this investigation. Finally, the conclusion section summarizes the fact about the (p , q) -variations. [ABSTRACT FROM AUTHOR]

As an important ecosystem, wetlands play a crucial role in both regional and global environments. Accurate land-cover classification can facilitate the management and understanding of wetlands. Considering the timely and cost-effective characteristics of remote sensing, this technique was used to obtain land-cover information for the Yellow River Delta (YRD) wetland in this investigation. Landsat-8 Operational Land Imager (OLI) sensor data were selected for the data set in this study. A combined approach of multiple end-member spectral mixture analysis (MESMA) and Random Forest (RF) was developed for land-cover classification mapping of the YRD wetland. This study aimed (1) to determine whether the MESMA technique in combination with RF significantly improves the accuracy of classification in complex landscapes such as the YRD wetland, (2) to determine whether the RF classifier shows good performance in land-cover classification of the YRD wetland, and (3) to compare the proposed method with the traditional Maximum Likelihood Classifier (MLC). The proposed hybrid method showed good performance, with an overall accuracy of 89.5% and a kappa coefficient (κ) of 0.88. The inclusion of fractional information derived from MESMA can improve the classification accuracy by 2–3%. In addition, through a comparison with traditional maximum likelihood (ML) methodology, the effectiveness of the proposed approach was evaluated. Overall, the proposed approach in this study can relatively accurately delineate a land-cover classification map of the YRD wetland with Landsat-8 OLI remotely sensed data. [ABSTRACT FROM PUBLISHER]

Aberrant activation of oncoproteins such as members of the Ras family is common in human lung cancers. The proper function of Ras largely depends on a post-translational modification termed prenylation. Bisphosphonates have been shown to inhibit prenylation in cancer cells. In this study, we show that zoledronic acid, a third generation bisphosphonate, is effective in targeting lung cancer cells. This is achieved by the induction of apoptosis and inhibition of proliferation, through suppressing the activation of downstream Ras and EGFR signalling by zoledronic acid. The combination of zoledronic acid and paclitaxel or cisplatin (commonly used chemotherapeutic drugs for lung cancer) augmented the activity of either drug alone in in vitro lung cancer cellular system and in vivo lung xenograft mouse model. Importantly, zoledronic acid inhibits protein prenylation as shown by the increased levels of unprenylated Ras and Rap1A. In addition, the effects of zoledronic acid were reversed in the presence of geranylgeraniol and farnesol, further confirming that mechanism of zoledroinc acid's action in lung cancer cells is through prenylation inhibition. Since zoledronic acid is already available for clinic use, these results suggest that it may be an effective addition to the armamentarium of drugs for the treatment of lung cancer. [ABSTRACT FROM AUTHOR]

A series of novel carbazole sulfonamide derivatives were synthesized and evaluated for antiproliferative activity. Among them, compounds 7 and 15 showed strong potency (IC 50 values of 0.81–31.19 nM) against five different cancer cells including multidrug-resistant MCF7/ADR cells. Compound 15 displayed a high cancer cell selectivity (IC 50 (L02)/average IC 50 : SI = 7.7). The l -valine prodrug 7a and the phosphate prodrug 15a exerted rohust in vivo antitumor efficacies and accepted safety prolifes. Further mechanism studies revealed that 7 and 15 directly bind to the colchicine site in tubulin to block tubulin polymerization, promote microtubule fragmentation at the cellular level, and induce apoptosis with G2/M cell cycle arrest. These compounds also inhibit HEMC-1 cells migration and vascular tube formation. Additionally, compound 7 displayed a selective inhibition of Topo I. Collectively, these studies suggest that 7 and 15 represents a promising new generation of tubulin inhibitors for cancer treatment. [Display omitted] • Compound 7 is a novel dual-mechanism antitumor agent with targeting tubulin and Topo I. • Compounds 7 and 15 showed an average IC 50 value of 17.99 and 6.94 nM against five cancer cells, respectively. • Compound 15 showed a high selective between cancer cells and L02 cells with SI of 7.7. • Compounds 7 and 15 possessed antitumor effect in MCF-7 xenograft model with IR 0f 66 % and 73 %, respectively. [ABSTRACT FROM AUTHOR]

The processing parameters for making a Maillard reaction product (SPPMP) from soy protein isolate (SPI) and soy soluble polysaccharide (SSPS) were studied against the yield of the product and its emulsification capacity in an oil-in-water emulsion. The optimized SPPMP was produced by dry-heating the SPI-SSPS mixture (SPP) at a ratio of 3:5, temperature of 60 °C and 75% relative humidity for 3 days. The formation of SPI-SSPS conjugates was confirmed by gel electrophoresis, FTIR spectroscopy and high performance size exclusion chromatography. The citral (10 wt%) oil-in-water emulsions stabilized by SPPMP exhibited superior physical stability than those stabilized by SPI or SPP during prolonged storage, after thermal treatment or under simulated gastrointestinal conditions. At pH 7.0, all the emulsions studied exhibited monomodal particle size distribution initially, however, only those stabilized by SPPMP remained monomodal distribution for up to 70 days during storage at 25 °C. The SPPMP-stabilized emulsion maintained its physical stability to the thermal treatment at 95 °C for 30 min or under simulated gastric conditions for 2 h; while the emulsions stabilized by SPI or SPP exhibited various degrees of instability. The release rate of citral from the emulsion droplets was found inversely related to the stability of emulsion. The emulsion droplets retained approximately 70% of citral after 2 h incubation in simulated gastric fluid, whereas, complete release of citral from the droplets occurred in 4 h in simulated intestinal fluid. These results indicate that SPPMP-stabilized emulsions have a good potential as a carrier system for intestinal delivery of hydrophobic compounds such as citral. [ABSTRACT FROM AUTHOR]

Citral is an important essential oil with antibacterial activities, but its use as an antibiotic alternative is limited due to its physical and chemical instability during processing and in biological systems such as the gastrointestinal tract of animals. This study aimed to investigate the capacity of a soy protein–polysaccharide Maillard reaction product (SPPMP) to stabilize citral in an oil-in-water emulsion system. The retention rates of citral in the emulsions during long time storage, upon heating and under simulated gastrointestinal conditions were determined. The results showed that SPPMP-stabilized emulsions demonstrated outstanding ability to stabilize citral under all challenge conditions as compared to emulsions stabilized by soy protein only, or by physical mixtures of soy protein and polysaccharide. Therefore, SPPMP-stabilized emulsions could potentially be used as protectors and carriers for targeted delivery of citral or other hydrophobic compounds to animal/human intestines. [ABSTRACT FROM AUTHOR]

IMB5036 is a novel pyridazinone compound with potent cytotoxicity. In this study, we reported its antitumour activity against pancreatic cancer and the underlying mechanism. We found that IMB5036 induced rapid cell swelling and increased membrane permeability in pancreatic cancer cells. IMB5036 increased the ratio of PI+ cells, which could be rescued by necroptosis inhibitor. Furthermore, MLKL inhibitor NSA attenuated the killing effect of IMB5036 on pancreatic cancer cells. IMB5036 stimulated translocation of MLKL and p‐MLKL from cytoplasm to cell membrane. IMB5036 upregulated the level of p‐RIPK1, p‐RIPK3, and p‐MLKL. At the same time, IMB5036 also partially activated apoptosis and pyroptosis. IMB5036 inhibited tumour growth in pancreatic xenograft. IMB5036 induced larger necrosis area, increased p‐MLKL level, and inhibited Ki67 expression in tumour mass. The study indicates that IMB5036 inhibits human pancreatic cancer growth primarily activating necroptosis. [ABSTRACT FROM AUTHOR]

Background: Paris polyphylla is a herb widely used in traditional Chinese medicine to treat various diseases. Stem rot diseases seriously affected the yield of P. polyphylla in subtropical areas of China. Therefore, cost-effective, chemical-free, eco-friendly strategies to control stem rot on P. polyphylla are valuable and urgently needed. Results: In this paper, we reported the biocontrol efficiency of Paenibacillus peoriae HJ-2 and its complete genome sequence. Strain HJ-2 could serve as a potential biocontrol agent against stem rot on P. polyphylla in the greenhouse and field. The genome of HJ-2 consists of a single 6,001,192 bp chromosome with an average GC content of 45% and 5,237 predicted protein coding genes, 39 rRNAs and 108 tRNAs. The phylogenetic tree indicated that HJ-2 is most closely related to P. peoriae IBSD35. Functional analysis of genome revealed numerous genes/gene clusters involved in plant colonization, biofilm formation, plant growth promotion, antibiotic and resistance inducers synthesis. Moreover, metabolic pathways that potentially contribute to biocontrol mechanisms were identified. Conclusions: This study revealed that P. peoriae HJ-2 could serve as a potential BCA against stem rot on P. polyphylla. Based on genome analysis, the genome of HJ-2 contains more than 70 genes and 12 putative gene clusters related to secondary metabolites, which have previously been described as being involved in chemotaxis motility, biofilm formation, growth promotion, antifungal activity and resistance inducers biosynthesis. Compared with other strains, variation in the genes/gene clusters may lead to different antimicrobial spectra and biocontrol efficacies. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has been widely used in emergency response, self-driving and city-scale 3D mapping and navigation. Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges (e.g. extreme lighting) while the traditional extractors are struggling. In this paper, we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework. In this thread, our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device. The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively. The scale estimation of the monocular visual SLAM is also significantly improved (0.01 versus 0.98). In addition, a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping. This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors. [ABSTRACT FROM AUTHOR]

Abstract: Air pollution, which is a global environmental problem, has been the hot research area among the scientists in the geoscience community. Air pollution simulation is of low-efficiency caused by the computation-intensive models, such as MM5 or WRF, and the complicated and unfriendly user interface. These issues are addressed in this paper by integrating computational grid and virtual geographic environment (VGE). The computational grid is employed to improve the computation efficiency of air pollution models. The VGE is used as a straightforward and easy to use interface to navigate the air pollution modeling workflow and improve the operational efficiencies of the models with respect to initiation, computation, and output visualization. On the aspect of technique implementation, this paper designs a framework and addresses the methodologies of the integration of computational grid and VGE. The prototype system, which integrates the computation grid of the Chinese University of Hong Kong (CUGrid) and a VGE to facilitate air pollution simulation based on the Mesoscale Model Version 5 (MM5), was developed. Based on the prototype system, a case was tested and the results indicate that the efficiencies of air pollution simulation on the model computation and workflow operation based on MM5 are increased significantly. This success also proves the reasonability of our general contribution of integrating computational grid and VGE to facilitate air pollution simulation. [Copyright &y& Elsevier]

Background: Contamination of grains with trichothecene mycotoxins, especially deoxynivalenol (DON), has been an ongoing problem for Canada and many other countries. Mycotoxin contamination creates food safety risks, reduces grain market values, threatens livestock industries, and limits agricultural produce exports. DON is a secondary metabolite produced by some Fusarium species of fungi. To date, there is a lack of effective and economical methods to significantly reduce the levels of trichothecene mycotoxins in food and feed, including the efforts to breed Fusarium pathogen-resistant crops and chemical/physical treatments to remove the mycotoxins. Biological approaches, such as the use of microorganisms to convert the toxins to non- or less toxic compounds, have become a preferred choice recently due to their high specificity, efficacy, and environmental soundness. However, such approaches are often limited by the availability of microbial agents with the ability to detoxify the mycotoxins. In the present study, an approach with PCR-DGGE guided microbial selection was developed and used to isolate DON -transforming bacteria from chicken intestines, which resulted in the successful isolation of several bacterial isolates that demonstrated the function to transform DON to its de-epoxy form, deepoxy-4-deoxynivalenol (DOM-1), a product much less toxic than DON. Results: The use of conventional microbiological selection strategies guided by PCR-DGGE (denaturing gradient gel electrophoresis) bacterial profiles for isolating DON-transforming bacteria has significantly increased the efficiency of the bacterial selection. Ten isolates were identified and isolated from chicken intestines. They were all able to transform DON to DOM-1. Most isolates were potent in transforming DON and the activity was stable during subculturing. Sequence data of partial 16S rRNA genes indicate that the ten isolates belong to four different bacterial groups, Clostridiales, Anaerofilum, Collinsella, and Bacillus. Conclusions: The approach with PCR-DGGE guided microbial selection was effective in isolating DON-transforming bacteria and the obtained bacterial isolates were able to transform DON. [ABSTRACT FROM AUTHOR]

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. [ABSTRACT FROM AUTHOR]

Bovine mastitis is of major concern to the dairy industry worldwide. The bovine teat canal is the primary route through which pathogens enter the mammary gland. The microflora of the teat canals of dairy and beef cattle was investigated by analysis of 16S rRNA gene sequences. The goal was to examine the global difference between dairy cattle, which are sanitized on a regular basis, and beef cattle, which receive little sanitary management. A diverse population of 16S rRNA gene sequences was recovered from both the dairy and the beef herd samples, with diversity higher in the beef sample. Analysis revealed the presence of 90 operational taxonomic units (OTUs) among 156 sequences, with 45 OTUs in the dairy sample and 53 OTUs in the beef sample. Only eight OTUs were common to both samples. Members of the classes Clostridia and Bacilli dominated both samples, followed by Actinobacteria and Proteobacteria. The dairy sample contained a cluster (20/80 clones) of Staphylococcaceae members, seven of which were identifiable as coagulase-negative Staphylococcus species. The beef sample was dominated by members of the genus Clostridia, many of which have not been previously cultured. The results suggest that the microorganisms present in the bovine teat canal are more diverse than previously described. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD. [ABSTRACT FROM AUTHOR]