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The backfill mining method is adopted in many mines around the world because it can reliably handle underground mine tailings and eliminate dangers in goafs. It is necessary to improve backfilling resource allocation and efficiency, thereby eliminating safety hazards and providing reliable support for the next stage of mining as quickly as possible. In this paper, we propose a backfill-scheduling optimization model that considers multiple processes, resource constraints, and operating capabilities. The purpose of this model is to minimize the exposure time of goafs. This NP-hard (Nondeterministic Polynomial-time hard) problem has a non-inferior implemented solution through multiple iterations of genetic, crossover, and mutation operations of the genetic algorithm. The results show that the model significantly reduces the backfilling-delay time and backfilling-operation time. [ABSTRACT FROM AUTHOR]

Due to the limitation of optical images that their waves cannot penetrate clouds, such images always suffer from cloud contamination, which causes missing information and limitations for subsequent agricultural applications, among others. Synthetic aperture radar (SAR) is able to provide surface information for all times and all weather. Therefore, translating SAR or fusing SAR and optical images to obtain cloud-free optical-like images are ideal ways to solve the cloud contamination issue. In this paper, we investigate the existing literature and provides two kinds of taxonomies, one based on the type of input and the other on the method used. Meanwhile, in this paper, we analyze the advantages and disadvantages while using different data as input. In the last section, we discuss the limitations of these current methods and propose several possible directions for future studies in this field. [ABSTRACT FROM AUTHOR]

To solve these problems of poor supporting effect and serious deformation and failure of surrounding rock of mining roadway under deep mining stress, a FLAC-3D numerical calculation model is established with −800 m level no. 2424 upper roadway in the Suncun Coal Mine as the background to compare the stress, deformation, and failure law of surrounding rock of mining roadway under once support and multi-level support with the same support strength. It is found that the multi-level support technology has obvious advantages in the surrounding rock of the horizontal roadway on the 2424 working face. From this, the key parameters of multi-level support are determined, and the field industrial test is carried out. The results show that the overall deformation of the surrounding rock is obviously reduced after multi-level support. The displacement of the two sides is reduced by about 40%, the displacement of the roof and floor is reduced by about 30%, and the plastic zone of the roadway is reduced by about 75%. The peak value of concentrated stress decreases from 98.7 MPa to 95.8 MPa, which decreases slightly. The integrity and stability of the surrounding rock are excellent, and the support effect is satisfactory. The research can provide reference and technical support for surrounding rock control of deep high-stress mining roadways. [ABSTRACT FROM AUTHOR]

Fungal laccases play important roles in the degradation of lignocellulose. In this study, the laccase producing cotton straw medium for Pleurotus ostreatus was optimized by single-factor and orthogonal experiments, and to investigate the role of Lacc1 gene, one of the laccase-encoding genes, in the degradation of cotton straw lignin, an overexpression strain of Lacc1 gene was constructed, which was analyzed for the characteristics of lignin degradation. The results demonstrated that the culture conditions with the highest lignin degradation efficiency of the P. ostreatus were the cotton straw particle size of 0.75 mm, a solid–liquid ratio of 1:3 and containing 0.25 g/L of Tween in the medium, as well as an incubation temperature of 26 °C. Two overexpression strains (OE L1-1 and OE L1-4) of Lacc1 gene were obtained, and the gene expression increased 12.08- and 33.04-fold, respectively. The results of 1H-NMR and FTIR analyses of significant changes in lignin structure revealed that Lacc1 gene accelerated the degradation of lignin G-units and involved in the cleavage of β-O-4 linkages and the demethylation of lignin units. These findings will help to improve the efficiency of biodelignification and expand our understanding of its mechanism. [ABSTRACT FROM AUTHOR]

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster. [ABSTRACT FROM AUTHOR]

Simple Summary: In Coleoptera, successful female reproduction partially relies on effective vitellogenesis, characterized by vitellogenin (Vg) synthesis in the fat body, secretion into hemolymphs, transport through intercellular channels in follicular epithelium, endocytosis mediated by Vg receptor (VgR), and absorption and storage by mature oocytes. In two representative Coleoptera species, Leptinotarsa decemlineata (Chrysomelidae) and Henosepilachna vigintioctopunctata (Coccinellidae), we performed RNA interference targeting ecdysone receptor (EcR) or ultraspiracle (usp) genes. Depletion of the expression level of EcR or usp inhibited oocyte development and dramatically repressed the transcription of Vg in fat bodies. Our findings indicate that 20E signaling plays an indispensable role in the stimulation of Vg synthesis and uptake in the two beetle species. Coleoptera is the largest taxa of animals by far. The robust reproductive capacity is one of the main reasons for such domination. Successful female reproduction partially relies on effective vitellogenesis. However, the hormone regulation of vitellogenesis remains to be explored. In the present paper, in vitro culture of Leptinotarsa decemlineata 1-day-old adult fat bodies in the 20E-contained median did not activate juvenile hormone production and insulin-like peptide pathways, but significantly stimulated the expression of two LdVg genes, in a cycloheximide-dependent pattern. In vivo RNA interference (RNAi) of either ecdysone receptor (LdEcR) or ultraspiracle (Ldusp) by injection of corresponding dsRNA into 1-day-old female adults inhibited oocyte development, dramatically repressed the transcription of LdVg genes in fat bodies and of LdVgR in ovaries; application of JH into the LdEcR or Ldusp RNAi L. decemlineata females did not restore the oocyte development, partially rescued the decreased LdVg mRNA levels but over-compensated LdVgR expression levels. The same RNAi experiments were performed in another Coleoptera species, Henosepilachna vigintioctopunctata. Little yolk substances were seen in the misshapen oocytes in the HvEcR or Hvusp RNAi ovaries, in contrast to larger amounts of yolk granules in the normal oocytes. Correspondingly, the transcript levels of HvVg in the fat bodies and ovaries decreased significantly in the HvEcR and Hvusp RNAi samples. Our results here show that 20E signaling is indispensable in the activation of vitellogenesis in the developing oocytes of the two beetle species. [ABSTRACT FROM AUTHOR]

Trichoderma spp. are widely used in plant disease control and growth promotion due to their high efficacy and multiple biocontrol mechanisms. Trichoderma koningiopsis T-51 is an effective biocontrol agent against gray mold disease by direct contact. However, the indirect physical contact biocontrol potential of Trichoderma spp. is not clear. In this study, the volatile organic compounds (VOCs) produced by T-51 showed high inhibitory activity against plant pathogenic fungi Botrytis cinerea and Fusarium oxysporum. The percentage of B. cinerea and F. oxysporum mycelial growth inhibition by T-51 VOCs was 73.78% and 43.68%, respectively. In both B. cinerea and F. oxysporum, conidial germination was delayed, and germ tube elongation was suppressed when exposed to T-51 VOCs, and the final conidial germination rate of B. cinerea decreased significantly after T-51 treatment. The VOCs from T-51 reduced the Botrytis fruit rot of tomato compared with that noted when using the control. Moreover, the T-51 VOCs significantly increased the size and weight of Arabidopsis thaliana seedlings. Twenty-four possible compounds, which were identified as alkenes, alkanes, and esters, were detected in VOCs of T-51. These results indicate that T. koningiopsis T-51 can exert biological control by integrating actions to suppress plant disease and promote plant growth. [ABSTRACT FROM AUTHOR]

Crop-type mapping is the foundation of grain security and digital agricultural management. Accuracy, efficiency and large-scale scene consistency are required to perform crop classification from remote sensing images. Many current remote-sensing crop extraction methods based on deep learning cannot account for adaptation effects in large-scale, complex scenes. Therefore, this study proposes a novel adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images. The selective patch module implemented in the network can adaptively integrate the features of different patch sizes to assess complex scenes better. TabNet was used simultaneously to extract spectral information from the center pixels of the patches. Multitask learning was used to supervise the extraction process to improve the weight of the spectral characteristics while mitigating the negative impact of a small sample size. In the network, superpixel optimization was applied to post-process the classification results to improve the crop edges. By conducting the crop classification of peanut, rice, and corn based on Sentinel-2 images in 2022 in Henan Province, China, the novel method proposed in this paper was more accurate, indicated by an F1 score of 96.53%, than other mainstream methods. This indicates our model's potential for application in crop classification in large scenes. [ABSTRACT FROM AUTHOR]

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model's accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model's robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth. [ABSTRACT FROM AUTHOR]

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology. [ABSTRACT FROM AUTHOR]

In the task of interactive image segmentation, the Inside-Outside Guidance (IOG) algorithm has demonstrated superior segmentation performance leveraging Inside-Outside Guidance information. Nevertheless, we observe that the inconsistent input between training and testing when selecting the inside point will result in significant performance degradation. In this paper, a deep reinforcement learning framework, named Inside Point Localization Network (IPL-Net), is proposed to infer the suitable position for the inside point to help the IOG algorithm. Concretely, when a user first clicks two outside points at the symmetrical corner locations of the target object, our proposed system automatically generates the sequence of movement to localize the inside point. We then perform the IOG interactive segmentation method for precisely segmenting the target object of interest. The inside point localization problem is difficult to define as a supervised learning framework because it is expensive to collect image and their corresponding inside points. Therefore, we formulate this problem as Markov Decision Process (MDP) and then optimize it with Dueling Double Deep Q-Network (D3QN). We train our network on the PASCAL dataset and demonstrate that the network achieves excellent performance. [ABSTRACT FROM AUTHOR]

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management. [ABSTRACT FROM AUTHOR]

Climate is one of the key drivers of the plant community's structure and trends. However, the regional vegetation-climate features in the ecotone have not yet been sufficiently studied. The aim of the research is to study features of Pinus sibirica Du Tour germination, survival, and growth in the mountain tundra of the Northern Urals against the background of a changing climate. The following research objectives were set: To determine the abundance and age structure of P. sibirica undergrowth on the mountain tundra plateau, identify the features of P. sibirica growth in the mountain tundra, and examine the correlation between the multi-year air temperature pattern, precipitation, and P. sibirica seedling emergence. A detailed study of the Pinus sibirica natural regeneration in the mountain stony shrub-moss-lichen tundra area at an altitude of 1010–1040 m above sea level on the Tri Bugra mountain massif plateau (59°30′ N, 59°15′ E) in the Northern Urals (Russia) has been conducted. The research involved the period between 1965 and 2017. Woody plant undergrowth was considered in 30 plots, 5 × 5 m in size. The first generations were recorded from 1967–1969. The regeneration has become regular since 1978 and its intensity has been increasing since then. Climate warming is driving these processes. Correlation analysis revealed significant relationships between the number of Pinus sibirica seedlings and the minimum temperature in August and September of the current year, the minimum temperatures in May, June, and November of the previous year, the maximum temperatures in May and August of the current year, and precipitation in March of both the current and previous years. However, the young tree growth rate remains low to date (the height at an age of 45–50 years is approximately 114 ± 8.8 cm). At the same time, its open crowns are rare single lateral shoots. The length of the side shoots exceeds its height by 4–5 times, and the length of the lateral roots exceeds its height by 1.2–1.5 times. This is an indicator of the extreme conditions for this tree species. With the current rates of climate warming and the Pinus sibirica tree growth trends, the revealed relationships allow for the prediction that in 20–25 years, the mountain tundra in the studied Northern Urals plateau could develop underground-closed forest communities with a certain forest relationship. The research results are of theoretical importance for clarifying the forest-tundra ecotone concept. From a practical point of view, the revealed relationship can be used to predict the trend in forest ecosystem formation in the mountain forest-tundra ecotone. [ABSTRACT FROM AUTHOR]

With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images. [ABSTRACT FROM AUTHOR]

The internal heat transfer performance and flow structures of a sweeping jet and film composite cooling on a flat plate were numerically studied. Sweeping jet and film composite cooling consists of a fluidic oscillator and 20 cylindrical film holes; the direct jet is formed by removing the feedback from the fluidic oscillator, which is different from the traditional cylindrical nozzle. Four different mass flow rates of coolant were considered, and the inclination angle of the film hole was 30°. The Conjugate Heat Transfer method (CHT) and Unsteady Reynolds Averaged Navier Stokes equation (URANS) were employed. The results indicated that the flow resistance coefficients of the sweeping jet were larger than those of the direct jet, and the Nusselt number monotonously increased with the increase in the mass flow rate. Compared to the direct jet, the sweeping jet had a more spatially uniform heat removal rate, and the area-averaged Nusselt number was slightly lower. Therefore, the sweeping jet and film composite cooling caused the distribution of the flat plate heat transfer to be more uniform. It is worth noting that the novel direct jet nozzle in the present work had considerable area-averaged impingement cooling effectiveness. [ABSTRACT FROM AUTHOR]

Due to the special properties of the ammonium salts, ammonium acetate and ammonium citrate were used to explore the best leaching conditions of rare earth with compound ammonium carboxylate. This paper explored the influence of the molar ratio, ammonium concentration, experimental temperature, and pH of the compound leaching agents on the leaching efficiency of rare earth and aluminum, and it analyzed the leaching process based on the leaching kinetics, which provides a new method for leaching rare earth from the weathered crust elution-deposited rare earth ore. The results showed that under the conditions where the molar ratio of ammonium acetate and ammonium citrate was 7:3 and the ammonium concentration was 0.15 mol/L, the leaching efficiency of rare earth was the highest when the pH of leaching agent was 4.0 and the experimental temperature was 313 K. Meanwhile, when CH3COONH4 and (NH4)3Cit were used to leach rare earth ore, the leaching reaction kinetics equation of rare earth and aluminum were obtained. In the temperature range of 283–323 K, the apparent activation energy of rare earth was 14.89 kJ/mol and that of aluminum was 19.17 kJ/mol. The reaction order of rare earth was 0.98 and that of aluminum was 0.79. The results were in accordance with the shrinking core model and indicate that the concentration of the leaching agent had a greater influence on rare earth than aluminum. This process can reduce the use of ammonium salt, and it is of great significance to extract rare earth elements from weathered crust elution-deposited rare earth ore and improve the utilization rate of resources. [ABSTRACT FROM AUTHOR]

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment. [ABSTRACT FROM AUTHOR]

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data. [ABSTRACT FROM AUTHOR]

Four kinds of Ca-montmorillonite with different layer charge density were used to study the effect of charge density on their hydration properties by molecular dynamics simulation and experiments. The research results of Z-density distribution of water molecules, Hw (hydrogen in water molecules), and Ca in the interlayer of montmorillonite show that the hydration properties of montmorillonite are closely related to its layer charge density. If the charge density is low, the water molecules in the interlayers are mainly concentrated on the sides of the central axis about –1.3 Å and 1.5 Å. As the charge density increases from 0.38semi-cell to 0.69semi-cell, the water molecules are distributed −2.5 Å and 2.4 Å away from the siloxane surface (Si-O), the concentration of water molecules near the central axis decreases, and at the same time, Ca2+ appears to gradually shift from the vicinity of the central axis to the Si-O surface on both sides in the montmorillonite layer. The simulation results of the radial distribution function (RDF) of the Ca-Hw, Ca-Ow (oxygen in water molecules), and Ca-Ot (the oxygen in the tetrahedron) show that the Ca2+ and Ow are more tightly packed together than that of Hw; with the increase of the charge density, due to the fact that the negative charge sites on the Si-O surface increase, under the action of electrostatic attraction, some of the Ca2+ are pulled towards the Si-O surface, which is more obvious when the layer charge density of the montmorillonite is higher. The results of the RDF of the Ot-Hw show that with the increase of charge density, the number of hydrogen bonds formed by Ot and Hw in the interlayers increase, and under the action of hydrogen bonding force, the water molecules near the central axis are pulled towards the two sides of Si-O surface. As a result, the arrangement of water molecules is more compact, and the structure is obvious. Correspondingly, the self-diffusion coefficient shows that the higher the layer charge density, the lower the self-diffusion coefficient of water molecules in interlayers is and the worse the hydration performance of montmorillonite. The experimental results of the experiments fit well with the above simulation results. [ABSTRACT FROM AUTHOR]

One of the major functions of the accessory protein Vif of human immunodeficiency virus type 1 (HIV-1) is to induce the degradation of APOBEC3 (A3) family proteins by recruiting a Cullin5-ElonginB/C-CBFβ E3 ubiquitin ligase complex to facilitate viral replication. Therefore, the interactions between Vif and the E3 complex proteins are promising targets for the development of novel anti-HIV-1 drugs. Here, peptides are designed for the Vif-CBFβ interaction based on the sequences of Vif mutants with higher affinity for CBFβ screened by a yeast surface display platform. We identified two peptides, VMP-63 and VMP-108, that could reduce the infectivity of HIV-1 produced from A3G-positive cells with IC50 values of 49.4 μM and 55.1 μM, respectively. They protected intracellular A3G from Vif-mediated degradation in HEK293T cells, consequently increasing A3G encapsulation into the progeny virions. The peptides could rapidly enter cells after addition to HEK293T cells and competitively inhibit the binding of Vif to CBFβ. Homology modeling analysis demonstrated the binding advantages of VMP-63 and VMP-108 with CBFβ over their corresponding wild-type peptides. However, only VMP-108 effectively restricted long-term HIV-1 replication and protected A3 functions in non-permissive T lymphocytes. Our findings suggest that competitive Vif-derived peptides targeting the Vif-CBFβ interaction are promising for the development of novel therapeutic strategies for acquired immune deficiency syndrome. [ABSTRACT FROM AUTHOR]

The charge characteristics of montmorillonite have significant effects on its hydration and application performances. In this study, a molecular dynamics simulation method was used to study the influence of the charge position and charge density of montmorillonite on the distribution of H2O and Ca2+ in layers. The results showed that when the layer charge is mainly derived from the substitution among ions in the tetrahedron, a large number of Hw and Ot are combined into a hydrogen bond in the interlayer, thus the water molecules are more compactly arranged and the diffusion of water molecules among the layers is reduced. In addition, the Ca2+ are diffused to the sides by a concentrated distribution in the central axis of the layer. As the charge density of the montmorillonite increases, the polarity of the Si–O surface increases, which lesds to the deterioration of the diffusibility of the water molecules and the structure of the water molecules in the interlayers is more stable. The increase in the layer charge density lesds to the expansion of the isomorphic substitution range of the crystal structure, which results in a more dispersed distribution of Ca2+ among the layers under the action of electrostatic attraction between the substituted negative sites and the Ca2+. [ABSTRACT FROM AUTHOR]

Eight fungal strains (P1 to P8) were isolated from rapeseed stems (Brassica napus) infected with the blackleg pathogen Leptosphaeria biglobosa (Lb). They formed pycnidia with similar morphology to those of Lb, and thus were considered as Lb relatives (LbRs). The species-level identification of these strains was performed. Their virulence on rapeseed and efficacy in the suppression of Lb infection were determined, and the biocontrol potential and biocontrol mechanisms of strain P2 were investigated. The results showed that the LbRs belong to two teleomorphic genera in the family Didymellaceae, Didymella for P1 to P7 and Boeremia for P8. Pathogenicity tests on rapeseed cotyledons and stems indicated the LbRs were weakly virulent compared to L. biglobosa. Co-inoculation assays on rapeseed cotyledons demonstrated that P1 to P7 (especially P1 to P4) had a suppressive effect on Lb infection, whereas P8 had a marginal effect on infection by L. biglobosa. Moreover, D. macrostoma P2 displayed a more aggressive behavior than L. biglobosa in the endophytic colonization of healthy rapeseed cotyledons. Cultures of P2 in potato dextrose broth (PDB) and pycnidiospore mucilages exuded from P2 pycnidia showed antifungal activity to L. biglobosa. Further leaf assays revealed that antifungal metabolites (AM) of strain P2 from PDB cultures effectively suppressed infection by L. biglobosa, Botrytis cinerea (gray mold), and Sclerotinia sclerotiorum (white mold). An antifungal metabolite, namely penicillither, was purified and identified from PDB cultures and detected in pycnidiospore mucilages of strain P2. This study suggests that the LbRs are a repertoire for screening biocontrol agents (BCAs) against rapeseed diseases, and D. macrostoma P2 is a multi-functional BCA, a penicillither producer, and an endophyte. [ABSTRACT FROM AUTHOR]

Phoma stem canker (blackleg), caused by the fungi Leptosphaeria maculans (anamorph Phoma lingam) and L. biglobosa, is one of the most devastating diseases in oilseed rape (Brassica napus L.) production worldwide. However, the population structure and genetic variation of L. biglobosa populations in China have rarely been investigated. Here, a collection of 214 fungal strains of blackleg from China (including Shaanxi, Jiangxi, Hubei, Jiangsu, Chongqing, Sichuan, Guangxi, Guizhou, Hunan, and Henan) and Europe (France and Ukraine) was identified as L. biglobosa. Three simple sequence repeat (SSR) markers were developed to characterize their population structure. The results showed that the Nei's average gene diversity ranged from 0.6771 for the population from Jiangsu to 0.3009 for the population from Hunan. In addition, most of the genetic variability (96%) occurred within groups and there were only relatively small amounts among groups (4.0%) (FST = 0.043, p = 0.042 < 0.05). Pairwise population differentiation (FST) suggested that significant genetic differentiation was observed between different L. biglobosa populations. Bayesian and unweighted average method analysis revealed that these L. biglobosa strains were clustered into three branches, and three European strains were similar to those from eastern China. The pathogenicity assay showed that those in Group III were significantly more virulent than those in Group I (t = 2.69, p = 0.016). The study also showed that Group III was dominant in Chinese L. biglobosa populations, which provides new insights for the further study of population evolution and the management of this pathogen. [ABSTRACT FROM AUTHOR]

Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems. [ABSTRACT FROM AUTHOR]

Fungal laccases play important roles in the degradation of lignocellulose. Although some PoLacs have been reported in several studies, still no comprehensive bioinformatics study of the LAC family in Pleurotus ostreatus has been reported. In this study, we identified 12 laccase genes in the whole genome sequence of P. ostreatus and their physical characteristics, gene distribution, phylogenic relationships, gene structure, conserved motifs, and cis-elements were also analyzed. The expression patterns of 12 PoLac genes at different developmental stages and under different culture substrates were also analyzed. The results revealed that PoLac2 and PoLac12 may be involved in the degradation of lignin and the formation of the fruiting body, respectively. Subsequently, we overexpressed PoLac2 in P. ostreatus by the Agrobacterium tumefaciens-mediated transformation (ATMT) method. The transformants’ laccase activity increased in varying degrees, and the gene expression level of PoLac2 in transformants was 2–8 times higher than that of the wild-type strain. Furthermore, the lignin degradation rate by transgenic fungus over 30 days was 2.36–6.3% higher than that of wild-type. Our data show that overexpression of PoLac2 significantly enhanced the lignin degradation of cotton-straw. To our knowledge, this study is the first report to demonstrate the functions of PoLac2 in P. ostreatus. [ABSTRACT FROM AUTHOR]

Coastal areas have become the primary location for nuclear power plant sites due to the convenience of seawater cooling. In China, the diffusion range of thermal discharge water has always been a hot research topic as the basis for sea use areas. The main physical process of heat loss in the numerical simulation of thermal discharge from power plants is heat exchange between the water surface and air. Currently, the methods for calculating this heat loss include the heat flux method and the water surface heat exchange coefficient method. Taking a power plant in Zhejiang as the research object, the MIKE 3 temperature–salinity module (heat flux method) and MIKE ECO Model (water surface heat exchange coefficient method) were used for modeling, and the diffusion of thermal discharge water under different modules was compared and analyzed. The results show that the calculated area of the water temperature rise envelope between the two modules differed significantly under the same conditions. The surface 4 °C temperature rise area, as calculated using ECO Model, was 45.8% smaller than that of the temperature–salinity module. The MIKE 3 temperature–salinity module can simulate the heat accumulation of water under thermal buoyancy, producing a significant thermal stratification phenomenon; the ECO module does not accurately represent the heat exchange process between the water surface and the air, and it does not adequately capture the significant vertical stratification that occurs in real-world scenarios. On this basis, this study not only lays a foundation for further exploring the impact of thermal discharge water from this powerhouse on the structure of surrounding marine biological communities and dissolved oxygen content in water bodies but also provides scientific evidence for the selection of modules when simulating thermal discharge water with the MIKE model. [ABSTRACT FROM AUTHOR]

Sclerotinia sclerotiorum is an important plant pathogenic fungus of many crops. Our previous study identified the S. sclerotiorum agglutinin (SSA) that can be partially degraded by the serine protease CmSp1 from the mycoparasite Coniothyrium minitans. However, the biological functions of SSA in the pathogenicity of S. sclerotiorum and in its response to infection by C. minitans, as well as to environmental stresses, remain unknown. In this study, SSA disruption and complementary mutants were generated for characterization of its biological functions. Both the wild-type (WT) of S. sclerotiorum and the mutants were compared for growth and sclerotial formation on potato dextrose agar (PDA) and autoclaved carrot slices (ACS), for pathogenicity on oilseed rape, as well as for susceptibility to chemical stresses (NaCl, KCl, CaCl2, sorbitol, mannitol, sucrose, sodium dodecyl sulfate, H2O2) and to the mycoparasitism of C. minitans. The disruption mutants (ΔSSA-175, ΔSSA-178, ΔSSA-225) did not differ from the WT and the complementary mutant ΔSSA-178C in mycelial growth. However, compared to the WT and ΔSSA-178C, the disruption mutants formed immature sclerotia on PDA, and produced less but larger sclerotia on ACS; they became less sensitive to the eight investigated chemical stresses, but more aggressive in infecting leaves of oilseed rape, and more susceptible to mycoparasitism by C. minitans. These results suggest that SSA positively regulates sclerotial development and resistance to C. minitans mycoparasitism, but negatively regulates pathogenicity and resistance to chemical stresses. [ABSTRACT FROM AUTHOR]

Simple Summary: In Drosophila melanogaster and Tribolium castaneum, dusky-like (Dyl) is related to linking the insect cuticle to the plasma membrane of epidermal cells, regulating the cytoskeleton and deposition of the cuticle. A deficiency in Dyl impairs the formation of embryonic denticles and adult sensory bristles and wing hairs. In a polyphagous beetle, Henosepilachna vigintioctopunctata, we characterized Hvdyl using RNAi. The results showed that the knockdown of Hvdyl inhibited the growth of cellular protuberances, inhibited foliage consumption, and affected the survival of the beetles. Our findings indicated that two functions of Dyl in Drosophila are conserved in H. vigintioctopunctata. Dusky-like (Dyl) is a transmembrane protein containing a zona pellucida domain. Its physiological roles during metamorphosis have been well explored in Drosophila melanogaster and have also been documented in Tribolium castaneum. However, Dyl has undergone a functional shift between Diptera and Coleoptera insects. Further investigation of Dyl in other insects will be helpful to further clarify its function in insect growth and development. Henosepilachna vigintioctopunctata is an important Coleoptera that causes enormous economic losses in agriculture in China. In this study, we found that the expression of Hvdyl was detectable in embryos, larvae, prepupae, pupae, and adults. We knocked down Hvdyl in third- and fourth-instar larvae and pupae with RNA interference (RNAi). RNAi of Hvdyl mainly caused two phenotypic defects. Firstly, the growth of epidermal cellular protuberances was suppressed. Injection of dsdyl (double-stranded dusky-like RNA) at the third-instar larval stage truncated the scoli throughout the thorax and abdomen and shortened the setae on the head capsules and mouthparts of the fourth-instar larvae. Introduction of dsdyl at the third- and fourth-instar stages led to misshapen pupal setae. The setae were shortened or became black nodules. Treatment with dsdyl at the larval and pupal stages resulted in deformed adults with completely suppressed wing hairs. Moreover, the knockdown of Hvdyl at the third-instar stage caused deformed larval mouthparts at the fourth-instar period. As a result, foliage consumption was inhibited, and larval growth was slowed. The results indicate that Dyl is associated with the growth of cellular protuberances throughout development and with the formation of the cuticle in H. vigintioctopunctata. [ABSTRACT FROM AUTHOR]

The non-coplanar lasers on both sides of the rail during full-section rail profile measurement based on line-structured light vision will cause the measured profile to be distorted, resulting in measurement errors. Currently, in the field of rail profile measurement, there are no effective methods for evaluating laser plane attitude, and it is impossible to determine the degree of laser coplanarity quantitatively and accurately. This study proposes an evaluation method based on fitting planes in response to this problem. Real-time fitting of laser planes with three planar targets of different heights provides information about the laser plane attitude on both sides of the rails. On this basis, laser coplanarity evaluation criteria were developed to determine whether the laser planes on both sides of the rails are coplanar. Using the method in this study, the laser plane attitude can be quantified and accurately assessed on both sides, effectively resolving the problem with traditional methods that can only assess the laser plane attitude qualitatively and roughly, thereby providing a solid foundation for calibration and error correction of the measurement system. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) is an effective means to achieve the goals of carbon peaking and carbon neutrality. To improve the operating economics and low-carbon emission of an integrated energy system, the strong exothermic property of power-to-gas is utilized for heat recovery and injection into the heat network. This expands the adjustable range of electric output of combined heat and power (CHP) units which will improve wind power accommodation. The CO2 produced by the coal-fired unit is captured using post-combustion carbon capture technology, and then stored and used to manufacture methane, in order to realize the electric–gas–heat integrated energy system coupled with power-to-gas. Based on the ladder-type carbon trading mechanism, a low-carbon economic dispatch model of integrated energy system is proposed, which considers the incorporation of power-to-gas heat recovery and carbon capture and storage. The objective function is to minimize the total operation cost of the system. The model is simulated in the revised IEEE 39-bus power network, Belgium 20-node gas network and 6-node heat network by CPLEX solver and simulation results verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

A fungal strain (JC-1) of Simplicillium was isolated from a pod of oilseed rape (Brassica napus) infested with the blackleg pathogen Leptosphaeria biglobosa. This study was done to clarify its taxonomic identity using morphological and molecular approaches, to characterize its antifungal activity through bioassays and genome-based identification of antifungal metabolites, and to determine its efficacy in inducing systemic resistance (ISR) in oilseed rape. The results showed that JC-1 belongs to Simplicillium lamellicola. It displayed a strong antagonistic relationship with L. biglobosa, Botrytis cinerea (gray mold) and Sclerotinia sclerotiorum (stem rot). The cultural filtrates of JC-1 showed a high efficacy in suppressing infection by S. sclerotiorum on detached leaves of oilseed rape. Genome analysis indicated that JC-1 has the capability of producing multiple antifungal metabolites, including aureobasidin A1, squalestatin S1 and verlamelin. Inoculation of JC-1 on seeds of oilseed rape caused a suppressive effect on infection by L. biglobosa on the cotyledons of the resulting seedlings, suggesting that JC-1 can trigger ISR. Endophytic growth, accumulation of anthocyanins, up-regulated expression of CHI (for chalcone isomerase) and PR1 (for pathogenesis-related protein 1), and down-regulated expression of NECD3 (for 9-cis-epoxycarotenoid dioxygenase) were detected to be associated with the ISR. This study provided new insights into the biocontrol potential and modes of action of S. lamellicola. [ABSTRACT FROM AUTHOR]

Coniothyrium minitans (Cm) is a mycoparasitic fungus of Sclerotinia sclerotiorum (Ss), the causal agent of Sclerotinia stem rot of oilseed rape. Ss can produce oxalic acid (OA) as a phytotoxin, whereas Cm can degrade OA, thereby nullifying the toxic effect of OA. Two oxalate decarboxylase (OxDC)-coding genes, CmOxdc1 and CmOxdc2, were cloned, and only CmOxdc1 was found to be partially responsible for OA degradation, implying that other OA-degrading genes may exist in Cm. This study cloned a novel OxDC gene (CmOxdc3) in Cm and its OA-degrading function was characterized by disruption and complementation of CmOxdc3. Sequence analysis indicated that, unlike CmOxdc1, CmOxdc3 does not have the signal peptide sequence, implying that CmOxDC3 may have no secretory capability. Quantitative RT-PCR showed that CmOxdc3 was up-regulated in the presence of OA, malonic acid and hydrochloric acid. Deletion of CmOxdc3 resulted in reduced capability to parasitize sclerotia of Ss. The polypeptide (CmOxDC3) encoded by CmOxdc3 was localized in cytoplasm and gathered in vacuoles in response to the extracellular OA. Taken together, our results demonstrated that CmOxdc3 is a novel gene responsible for OA degradation, which may work in a synergistic manner with CmOxdc1. [ABSTRACT FROM AUTHOR]

In view of the problem that anchored bedding rock material is prone to instability and failure under impact loading in the process of deep coal mining, and taking the lower roadway of a deep 2424 coal working face in the Suncun coal mine as the engineering background, a mechanical model of anchored bedding rock material was established, and the instability criterion of compression and shear failure of anchored bedding rock material was obtained. Then, the separated Hopkinson pressure bar was used to carry out an impact-loading test on the anchored bedding rock material, and the dynamic mechanical properties of the rock with different anchoring modes and bolt bedding angles were studied; the evolution law of the strain field of the anchored bedding rock material was also obtained. The results show the following: (1) The bolt support could effectively improve the dynamic load strength and dynamic elastic modulus of the rock material with anchorage bedding, the degree of improvement increased with the increase in the angle of the bolt bedding, and the full anchorage effect was much higher than the end anchorage effect was. (2) The bolt bedding angle and anchorage mode greatly influenced crack development and displacement characteristics. After an impact, the bedding rock material had obvious shear displacement along the bedding direction, and obvious macroscopic cracks were produced in the bedding plane. The research results offer theoretical guidance to and have reference significance for deep roadway anchorage support engineering. [ABSTRACT FROM AUTHOR]

The ecological environment is the basis of human survival and development. Effective methods to monitor the ecological environment are essential for the healthy development of human settlements. At present, methods based on remote sensing images and other basic data have played key roles in ecological environment monitoring, providing support for decision-making on local ecological environment protection. However, these data and methods have obvious limitations. On the one hand, they cannot reflect the feelings of human beings about the ecological environment in which they live. On the other hand, it is difficult to capture more detailed information about the ecological environment. Non-professional observation data represented by social media describe the ecological environment from the perspective of the public, which can be a powerful supplement to traditional data. However, these different data sources have their own characteristics and forms, and it is difficult to achieve efficient integration. Therefore, in this paper, we proposed a framework that comprehensively considers social media, remote sensing, and other data to monitor the ecological environment of a study area. First, the framework extracted the ecological environment-related information contained in social media data, including public sentiment information and topic keyword information, by integrating algorithms such as natural language processing and machine learning. Then, we constructed a social semantic network related to the ecological environment based on the extracted information. We used a remote sensing image and other basic data to analyze the ecological sensitivity in the study area. Finally, based on the keyword with spatial location attribute contained in the social semantic network, we established the link between the constructed network and the results of ecological sensitivity analysis to comprehensively analyze the ecological environment in the study area. The comprehensive analysis results not only reflect the distribution of ecological vulnerability in the study area, but also help identify specific areas worthy of attention and the ecological problems faced by these areas. We used the city of Sanya in China as a case study to verify the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper. [ABSTRACT FROM AUTHOR]

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models. [ABSTRACT FROM AUTHOR]

The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people's daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper. [ABSTRACT FROM AUTHOR]

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya'an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

Here, we characterized a negative single-stranded (−ss)RNA mycovirus, Botrytis cinerea mymonavirus 1 (BcMyV1), isolated from the phytopathogenic fungus Botrytis cinerea. The genome of BcMyV1 is 7863 nt in length, possessing three open reading frames (ORF1–3). The ORF1 encodes a large polypeptide containing a conserved mononegaviral RNA-dependent RNA polymerase (RdRp) domain showing homology to the protein L of mymonaviruses, whereas the possible functions of the remaining two ORFs are still unknown. The internal cDNA sequence (10-7829) of BcMyV1 was 97.9% identical to the full-length cDNA sequence of Sclerotinia sclerotiorum negative stranded RNA virus 7 (SsNSRV7), a virus-like contig obtained from Sclerotinia sclerotiorum metatranscriptomes, indicating BcMyV1 should be a strain of SsNSRV7. Phylogenetic analysis based on RdRp domains showed that BcMyV1 was clustered with the viruses in the family Mymonaviridae, suggesting it is a member of Mymonaviridae. BcMyV1 may be widely distributed in regions where B. cinerea occurs in China and even over the world, although it infected only 0.8% of tested B. cinerea strains. [ABSTRACT FROM AUTHOR]

Background: Liver fibrosis, as a common and refractory disease, is challenging to treat due to the lack of effective agents worldwide. Recently, we have developed a novel compound, N-(3,4,5-trichlorophenyl)-2(3-nitrobenzenesulfonamide) benzamide (IMB16-4), which is expected to have good potential effects against liver fibrosis. However, IMB16-4 is water-insoluble and has very low bioavailability. Methods: Mesoporous silica nanoparticles (MSNs) were selected as drug carriers for the purpose of increasing the dissolution of IMB16-4, as well as improving its oral bioavailability and inhibiting liver fibrosis. The physical states of IMB16-4 and IMB16-4-MSNs were investigated using nitrogen adsorption, thermogravimetric analysis (TGA), HPLC, UV-Vis, X-ray diffraction (XRD) and differential scanning calorimetry (DSC). Results: The results show that MSNs enhanced the dissolution rate of IMB16-4 significantly. IMB16-4-MSNs reduced cytotoxicity at high concentrations of IMB16-4 on human hepatic stellate cells LX-2 cells and improved oral bioavailability up to 530% compared with raw IMB16-4 on Sprague–Dawley (SD) rats. In addition, IMB16-4-MSNs repressed hepatic fibrogenesis by decreasing the expression of hepatic fibrogenic markers, including α-smooth muscle actin (α-SMA), transforming growth factor-beta (TGF-β1) and matrix metalloproteinase-2 (MMP2) in LX-2 cells. Conclusions: These results provided powerful information on the use of IMB16-4-MSNs for the treatment of liver fibrosis in the future. [ABSTRACT FROM AUTHOR]

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance among single models, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable. [ABSTRACT FROM AUTHOR]

Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain. [ABSTRACT FROM AUTHOR]

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages. [ABSTRACT FROM AUTHOR]

Broadband metamaterial absorbers (MAs) are critical for applications of photonic and optoelectronic devices. Despite long-standing efforts on broadband MAs, it has been challenging to achieve ultrabroadband absorption with high absorptivity and omnidirectional characteristics within a comparatively simple and low-cost architecture. Here we design, fabricate, and characterize a novel compact Cr-based MA to achieve ultrabroadband absorption in the visible to near-infrared wavelength region. The Cr-based MA consists of Cr nanorods and Cr substrate sandwiched by three pairs of SiO2/Cr stacks. Both simulated and experimental results show that an average absorption over 93.7% can be achieved in the range of 400–1000 nm. Specifically, the ultrabroadband features result from the co-excitations of localized surface plasmon (LSP) and propagating surface plasmon (PSP) and their synergistic absorption effects, where absorption in the shorter and longer wavelengths are mainly contributed bythe LSP and PSP modes, respectively. The Cr-based MA is very robust to variations of the geometrical parameters, and angle-and polarization-insensitive absorption can be operated well over a large range of anglesunder both transverse magnetic(TM)- and transverse electric (TE)-polarized light illumination. [ABSTRACT FROM AUTHOR]

In this work, the effect of layer charge density of Na-montmorillonite (Na-MT) and carbon chain length of alkyl ammonium on the structure and gel property of organo-montmorillonite (organo-MT) was studied by using X-ray diffraction (XRD), Fourier transform infrared (FTIR), thermogravimetric (TG) analysis, contact angle test, molecular dynamics (MD) simulation, and gel apparent viscosity determination experiment. The results of XRD show that Na-MT with lower layer charge density is easier to swell after intercalation of alkyl ammonium, and the basal spacing of organo-MT increases with the increase of carbon chain length. The results of FTIR show that the absorption bands at 2924 cm−1 and 2853 cm−1 shift towards low frequency region with the increase of carbon chain length, and the absorption bands at 515 cm−1 and 463 cm−1 move towards high frequency region when the layer charge density increases. The mass loss of organo-MT evidently increases with the increase of layer charge density of Na-MT or carbon chain length of alkyl ammonium. The contact angle test results are well in line with the TG data and reveal that alkyl ammonium with longer carbon chain can significantly improve the hydrophobicity of organo-MT. MD simulation indicates that, when the layer charge density is low, the distribution of alkyl ammonium gradually changes from parallel double layers to partially inclined distribution with the increase of carbon chain length, but when the layer charge density is high, the distribution of alkyl ammonium gradually changes from three layers into four layers. The test results of the apparent viscosity of the gel formed by organo-MT in xylene show that the apparent viscosity of organo-MT gel is negatively correlated with the layer charge density of Na-MT and positively correlated with the carbon chain length of alkyl ammonium. [ABSTRACT FROM AUTHOR]

At monolayer hydration state, the spatial distribution of H2O and Na+ in the interlayer of Na-montmorillonite (Na-MT) with different crystal chemistry properties was investigated by the molecular dynamics simulation method. The simulation results show that when layer charge density increases, H2O will move and form hydrogen bonds with O in tetrahedral surfaces (Ot) at a distance of 1.676 ± 0.043 Å. The impact of isomorphic substitution on the relative concentration of H2O depends largely on the layer charge density of Na-MT, when layer charge density is high, H2O move obviously to both sides of Na-MT sheets with the increase of octahedral substitution ratio. Nevertheless, Na+ coordinate with Ot at a distance of 2.38 Å, and the effect of isomorphic substitution ratio on the diffusion of Na+ is opposite to that of H2O. The mobility of both H2O and Na+ decreases with the increase of layer charge density or tetrahedral substitution ratio. The radial distribution function of Na-Ow (O in H2O) shows that the coordination strength between Na+ and Ow decreases with the increase of layer charge density or tetrahedral substitution ratio, and Na+ are hydrated by four H2O at a Van der Waals radius of 2.386 ± 0.004 Å. The research results can provide a theoretical basis for the efficient application of Na-MT at the molecular and atomic levels. [ABSTRACT FROM AUTHOR]

A pink isolate (QT5-19) of Botrytis cinerea was compared with three gray isolates of B. cinerea for growth and morphogenesis on potato dextrose agar (PDA), and for pathogenicity on tobacco. A double-stranded (ds) RNA mycovirus infecting QT5-19 was identified based on its genome feature and morphology of the virus particles. The results showed that QT5-19 grew rapidly and established flourishing colonies as the gray isolates did. However, it is different from the gray isolates, as it failed to produce conidia and sclerotia asthe gray isolates did. QT5-19 hardly infected tobacco, whereas the gray isolates aggressively infected tobacco. Two dsRNAs were detected in QT5-19, dsRNA 1 and dsRNA 2, were deduced to encode two polypepetides with homology to viral RNA-dependent RNA polymerase (RdRp) and coat protein (CP), respectively. Phylogenetic analysis of the amino acid sequences of RdRp and CP indicated that the two dsRNAs represent the genome of a novel partitivirus in the genus Alphapartitivirus, designated here as Botrytis cinerea partitivirus 2 (BcPV2). BcPV2 in QT5-19 was successfully transmitted to the three gray isolates through hyphal contact. The resulting BcPV2-infected derivatives showed rapid growth on PDA with defects in conidiogenesis and sclerogenesis, and hypovirulence on tobacco. This study suggests that BcPV2 is closely associated with hypovirulence of B. cinerea. [ABSTRACT FROM AUTHOR]

In sharp contrast to conventional photosensitization methods in which the organic pigments were often adsorbed, herein we present a study on natural vegetable pigment inserted TiO2 aerogel nanocomposites and we directly use red cabbage anthocyanin (RCP) as a structure-directing agent. It was found that pure TiO2 aerogel nanocomposite did not exhibit any meaningful activity for photocatalytic reduction of Cr(VI). However, the photocatalytic reduction activity was greatly improved by the RCP inserted TiO2 aerogel nanocomposites under visible-light irradiation, which was approximately 2- and 12.3-fold higher than that of TiO2 aerogel conventionally photosensitized by RCP and pure TiO2 aerogel nanocomposites, respectively. It also exhibited good stability and could be reused at least three times without losing a significant amount of its activity. [ABSTRACT FROM AUTHOR]

Sclerotinia minor is a phytopathogenic fungus causing sclerotinia blight on many economically important crops. Here, we have characterized the biological and molecular properties of a novel endornavirus, Sclerotinia minor endornavirus 1 (SmEV1), isolated from the hypovirulent strain LC22 of S. minor. The genome of SmEV1 is 12,626 bp long with a single, large open reading frame (ORF), coding for a putative protein of 4020 amino acids. The putative protein contains cysteine-rich region (CRR), viral methyltransferase (MTR), putative DEXDc, viral helicase (Hel), and RNA-dependent RNA polymerase (RdRp) domains. The putative protein and the conserved domains are phylogenetically related to endornaviruses. SmEV1 does not contain a site-specific nick characteristic of most previously described endornaviruses. Hypovirulence and associated traits of strain LC22 and SmEV1 were readily cotransmitted horizontally via hyphal contact to isolates of different vegetative compatibility groups of S. minor. Additionally, SmEV1 in strain LC22 was found capable of being transmitted vertically through sclerotia. Furthermore, mycelium fragments of hypovirulent strain LC22 have a protective activity against attack by S. minor. Taken together, we concluded that SmEV1 is a novel hypovirulence-associated mycovirus with a wide spectrum of transmissibility, and has potential for biological control (virocontrol) of diseases caused by S. minor. [ABSTRACT FROM AUTHOR]

Micro phasor measurement units (μPMU) installed in active distribution networks are very useful for improving observability by acquiring system real-time data. However, three-phase imbalance and harmonic power flows adversely impact the accuracy of synchronous measurements, which implies the importance of phasor estimation errors. This paper proposes a new phasor estimation algorithm for μPMU in active distribution networks that uses a conditional maximum likelihood (CML) estimation method. Firstly, the signal model of three-phase, three-wire and four-wire imbalance systems is established. Then, the probability distributions of the magnitude and phase angles are derived from the geometric characteristics of the CML method by solving the geometric equation. Simulation results show that the proposed CML based method is effective for estimating phasor and impedance models of active distribution networks by using μPMU measurement data. [ABSTRACT FROM AUTHOR]