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Abstract Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

The rapid and accurate detection of peanuts and soybeans allergen is important to the food safety. In this study, Cu-TCPP nanosheet, a kind of ultra-thin metal-organic framework (MOF) was synthesized and applied in loop-mediated isothermal amplification (named Cu-TCPP@LAMP), which can inhibit the non-specific amplification by absorbing and precise temperature releasing of single primer. As thus, Cu-TCPP@LAMP can achieve high sensitivity and specific amplification of the target gene. As a result, peanut and soybean allergens genes contained in food were successfully detected with a favorable detection sensitivity (5 ng/μL for peanuts and 10 ng/μL for soybeans) and reliable repeatability (The coefficient of variation was 3.38% for peanuts and 3.33% for soybeans). Moreover, the established method was utilized for detection of several commercial products, and had a high consistency with the standard method. Apart from food allergens, this novel assay can be widely used in other areas, such as pathogen detection, tumor nucleic acid detection and so on.

Ischemic stroke (IS), primarily caused by cerebrovascular obstruction, results in severe neurological deficits and has emerged as a leading cause of death and disability worldwide. Recently, there has been increasing exploration of the neuroprotective properties of the inert gas argon. Argon has exhibited impressive neuroprotection in many in vivo and ex vivo experiments without signs of adverse effects, coupled with the advantages of being inexpensive and easily available. However, the efficient administration strategy and underlying mechanisms of neuroprotection by argon in IS are still unclear. This review summarizes current research on the neuroprotective effects of argon in IS with the goal to provide effective guidance for argon application and to elucidate the potential mechanisms of argon neuroprotection. Early and appropriate argon administration at as high a concentration as possible offers favorable neuroprotection in IS. Argon inhalation has been shown to provide some long-term protection benefits. Argon provides the anti-oxidative stress, anti-inflammatory and anti-apoptotic cytoprotective effects mainly around Toll-like receptor 2/4 (TLR2/4), mediated by extracellular signal-regulated kinase 1/2 (ERK1/2), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), nuclear factor kappa-B (NF-ĸB) and B-cell leukemia/lymphoma 2 (Bcl-2). Therefore, argon holds significant promise as a novel clinical neuroprotective gas agent for ischemic stroke after further researches to identify the optimal application strategy and elucidate the underlying mechanism.

Objective Hypertension is one of the leading causes of human death and disability. MTHFR and MTRR regulate folate metabolism and are closely linked to hypertension, although the relationship is inconsistent among different ethnic groups. The present study aims to investigate the effects of MTHFR C677T (rs1801133), MTHFR A1298C (rs1801131), and MTRR A66G (rs1801394) polymorphisms on hypertension susceptibility in the Bai nationality of the Yunnan Province, China. Methods This case–control study included 373 hypertensive patients and 240 healthy controls from the Chinese Bai population. The genotyping of MTHFR and MTRR gene polymorphisms was carried out by using the KASP method. The effects of genetic variations of MTHFR and MTRR genes on hypertension risk were evaluated with odds ratios (OR) and 95% confidence intervals (95% CI). Results The present study revealed that the CT and TT genotypes and T allele of MTHFR C677T locus were considerably associated with an increased risk of hypertension. In addition, MTHFR A1298C locus CC genotype could significantly increase the hypertension risk. The T-A and C–C haplotypes of MTHFR C677T and MTHFR A1298C could increase the risk of hypertension. Further stratified analysis by risk rank of folate metabolism indicated that people with poor utilization of folic acid were more prone to develop hypertension. In the hypertension group, the MTHFR C677T polymorphism was significantly associated with fasting blood glucose, fructosamine, apolipoprotein A1, homocysteine, superoxide dismutase, and malondialdehyde levels. Conclusion Our study suggested that genetic variations of MTHFR C677T and MTHFR A1298C were significantly associated with susceptibility to hypertension in the Bai population from Yunnan, China.

Saline–alkaline soil is a severe threat to Sustainable Development Goals (SDGs), but it can also be a precious land resource if properly utilized according to its properties. This research takes the Songnen Plain as the study area. The aim is to figure out the saline–alkaline status and mechanisms for its scientific utilization. Sentinel-2 multispectral imagery is used, and a 3D spectral space optimization method is proposed according to the restrictive relationships among the surface soil salinity index (SSSI), vegetation index (VI), and surface soil wetness index (SSWI) to construct a surface soil salinization–alkalization index (SSSAI) for estimation of the surface soil salinity (SSS). It is testified that SSS can be precisely estimated using the SSSAI (R2 = 0.74) with field verification of 50 surface salinized soil samples. Surface water and groundwater investigations, as well as deep soil exploration, indicate that the salt ions come from groundwater, and alkalinization is a primary problem in the deep soils. Fine-textured clay soils act as interrupted aquifers to prevent salt ions from penetrating and diluting downward with water, which is the cause of the salinization–alkalization problem in the study area. Finally, a sustainable solution for the saline–alkaline land resource is proposed according to the deep soil properties.

FOXG1, a transcriptional factor belonging to the Forkhead Box (Fox) superfamily, is highly expressed in the brain tissue during brain development and plays an important role in cellular proliferation. Recently, FOXG1 was reported to play important roles in oncogenesis, wherein its abnormal expression regulates tumor cell proliferation. However, the expression and role of FOXG1 in lung cancer remain largely unknown. This study investigated the clinical significance, expression, and role of FOXG1 in lung cancer. We found that FOXG1 was highly expressed in lung cancer tissues. MTT, CCK-8 and colony formation assays showed that FOXG1 overexpression could enhance the proliferation of A549 lung cancer cells. Flow cytometry analysis revealed that FOXG1 promoted the cell cycle and suppressed cell apoptosis. Additionally, the expression levels of PTEN, phosphorylated AKT, mTOR, p53, and Bax were significantly altered in response to changes in FOXG1 expression, indicating that FOXG1 regulated the PI3K pathway. Furthermore, in the xenograft mouse model, the upregulated FOXG1 expression strongly promoted tumor growth. In conclusion, these results suggested that FOXG1 was a critical regulator of the proliferation of lung cancer cells and enhanced tumor growth in vivo.

Macadamia decline poses a serious economic threat to the macadamia industry. It exhibits either a slow decline due to infection by Kretzschmaria clavus or Ganoderma lucidum, or a quick decline caused by pathogens like Phytophthora spp., Lasiodiplodia spp., Neofusiccocum spp., Nectria rugulosa, Xylaria arbuscula, Phellinus gilvus, Acremonium recifei, and Rosellinia spp. Chemical strategies, resistant cultivars, and agronomic measures have been widely adopted to control macadamia decline, but effective biological control measures have rarely been applied. This paper proposes two key steps for implementing biological control strategies, i.e., the isolation and selection of biological control agents from healthy plants, or from the disease-suppressive soil for the construction of synthetic microbial communities, and the integration of synthetic microbial communities with various strategies, including seed coating, root dipping, seedling substrate, soil drenching, foliar spraying, and application as a bio-organic fertilizer. By adopting these strategies, we aim to provide proactive and efficient approaches for combating macadamia decline and safeguarding the health of macadamia orchards.

ABSTRACTThe purpose of this paper is to present mechanisms and algorithms implemented for improving Bitcoin cryptocurrency efficiency and security and to examine the block propagation times from a selected period before and after SegWit was introduced. In this paper, Segregated Witness Implementation issues were verified based both on the simulation and real data from the Bitcoin network. Based on the block propagation times calculated in the simulator, as well as bitcoin network real data, the efficiency and safety of Bitcoin have been analysed and validated.

Abstract The aim of this paper was to perform an analysis of the state-of-the-art solutions of the permissioned blockchain compliance with the General Data Protection Regulation (GDPR), including the implementation of one of the analyzed methods and the own solution. This paper covers the subject of GDPR and its impact on already existing blockchain databases to determine the domain of the problem, including the necessity to introduce mutability in the data structure to comply with the ”right to be forgotten”. The performed analysis made it possible to discuss current research in technical terms as well as in the regulation itself. In the experimental part, attempts were made to research and implement the Reference-based Tree Structure (RBTS), including the performance tests. The proposed solution is efficient and easily reproducible. The deletion of unwanted content is quick and requires consent only from the owner of personal data; therefore, eliminating the dependency on the other blockchain network participants.

Growing potatoes (Solanum tuberosum) using the idle rice fields in Southern China and the Indo-Gangetic Plains of India in the winter season through the rice–potato rotation (RC) system could support future food security. However, the modulation capacity of the RC system on soilborne fungal pathogens is still unclear. In the current study, a pot experiment was designed and conducted to monitor the dynamics of soil fungal community composition between the potato monoculture (CC) system and the RC system, where the two systems were set with the same soil conditions: autoclaving with fertilization; autoclaving without fertilization; autoclave-free with fertilization; and autoclave-free without fertilization. Then, the uncultivated soil (CK) and root-zone soil samples of conditions under the two systems were collected, and then soil physiochemical properties and enzymatic activities were determined. Next, the high-variable region (V5–V7) of fungal 18S rRNA genes of the samples were amplified and sequenced through the PCR technique and the Illumina Miseq platform, respectively. Finally, the fungal species diversity and composition, as well as the relative abundance of fungal pathogens annotated against the Fungiuld database in soil samples, were also investigated. The results showed that the RC could significantly (p < 0.05) increase soil fungal species diversity and decrease the relative abundance of soil fungal pathogens, where the RC could suppress 23 soil fungal pathogens through cultivating the rice during the summer season and 93.75% of the remaining pathogens through winter-season cultivation. Seven-eighths of the conditions under RC have lower pathogenic MGIDI indices (6.38 to 7.82) than those of the CC (7.62 to 9.63). Notably, both rice cultivation and winter planting reduced the abundance of the pathogenic strain ASV24 under the Colletotrichum genus. The bipartite fungal network between the pathogens and the non-pathogens showed that the pathogenic members could be restricted through co-occurring with the non-pathogenic species and planting crops in the winter season. Finally, the redundancy analysis (RDA) indicated that soil pH, electronic conductivity, available phosphorus content, and various enzyme activities (cellulase, urease, sucrase, acid phosphatase, catalase, polyphenol oxidase) could be the indicators of soil fungal pathogens. This experiment demonstrated that the rice–potato rotation system outperformed the potato monoculture on suppressing soilborne fungal pathogenic community.

Habitat quality is a key factor in regional ecological restoration and green development. However, limited information is available to broadly understand the role of natural and human factors in influencing habitat quality and the extent of their impact. Based on remote sensing monitoring data of land use over five time points (2000, 2005, 2010, 2015, and 2020), natural factors, and socioeconomic data, we applied the InVEST model to assess habitat quality in Guangdong Province. Using a multiscale geographically weighted regression (MGWR) model, we explored the spatial scale differences in the role of natural and human factors affecting habitat quality and the degree of their influence. The highlights of the results are as follows: ① From 2000 to 2020, land-use changes in the Pearl River Delta (PRD) region were particularly obvious, with the dynamic degree of construction land being higher than that of other land-use types. Construction land has gradually occupied agricultural and ecological land, causing damage to habitats. ② The overall habitat quality in Guangdong Province is decreasing; the areas with low habitat quality values are concentrated in the PRD region and the coastal areas of Chaoshan, Maoming, and Zhanjiang, while the areas with higher habitat quality values are mainly located in the non-coastal areas in the east and west of Guangdong and the north of Guangdong. ③ The MGWR regression results showed that the normalized vegetation index had the strongest effect on habitat quality, followed by road density, gross domestic product (GDP) per unit area, slope, and average elevation, and the weakest effect on average annual precipitation. ④ The effects of average elevation, GDP per unit area, and normalized vegetation index on habitat quality were significantly positively correlated, while road density was significantly negatively correlated. These results provide a scientific basis for adjusting spatial land-use planning and maintaining regional ecological security.

Dodders (Cuscuta chinensis) are rootless and holoparasitic herbs that can infect a variety of host plants, including the vitally important economic and bioenergy crop soybean (Glycine max). Although dodder parasitism severely affects the physiology of host plants, little is known about its effects on fungal communities and root secondary metabolites in hosts. In this study, variations in root-associated fungal communities and root metabolites of soybean under different parasitism conditions were investigated using ITS rRNA gene sequencing and UPLC–MS/MS metabolome detection technologies. The results showed that dodder parasitism significantly altered the composition and diversity of the fungal communities in the rhizosphere and endosphere of soybean. The relative abundance of the potential pathogenic fungus Alternaria significantly increased in the root endosphere of dodder-parasitized soybean. Furthermore, correlation analysis indicated that the fungal community in the root endosphere was susceptible to soil factors under dodder parasitism. Meanwhile, the content of soil total nitrogen was significantly and positively correlated with the relative abundance of Alternaria in the rhizosphere and endosphere of soybean. Metabolomic analysis indicated that dodder parasitism altered the accumulation of flavonoids in soybean roots, with significant upregulation of the contents of kaempferol and its downstream derivatives under different parasitism conditions. Taken together, this study highlighted the important role of dodder parasitism in shaping the fungal communities and secondary metabolites associated with soybean roots, providing new insights into the mechanisms of multiple interactions among dodder, soybean, microbial communities and the soil environment.

Abstract Background Microfluidic chip detection technology is considered a potent tool for many bioanalytic applications. Rapid detection of foodborne pathogens in the early stages is imperative to prevent the outbreak of foodborne diseases, known as a severe threat to human health. Conventional bacterial culture methods for detecting foodborne pathogens are time-consuming, laborious, and lacking in pathogen diagnosis. To overcome this problem, we have created an embedded paper-based microchip based on isothermal loop amplification (LAMP), which can rapidly and sensitively detect foodborne pathogens. Results We embed paper impregnated with LAMP reagent and specific primers in multiple reaction chambers of the microchip. The solution containing the target pathogen was injected into the center chamber and uniformly distributed into the reaction chamber by centrifugal force. The purified DNA of Escherichia coli O157:H7, Salmonella spp., Staphylococcus aureus, and Vibrio parahaemolyticus has been successfully amplified and directly detected on the microchip. The E. coli O157:H7 DNA was identified as low as 0.0134 ng μL− 1. Besides, the potential of this microchip in point-of-care testing was further tested by combining the on-chip sample purification module and using milk spiked with Salmonella spp.. The pyrolyzed milk sample was filtered through a polydopamine-coated paper embedded in the inside of the sample chamber. It was transported to the reaction chamber by centrifugal force for LAMP amplification. Then direct chip detection was performed in the reaction chamber embedded with calcein-soaked paper. The detection limit of Salmonella spp. in the sample measured by the microchip was approximately 12 CFU mL− 1. Conclusion The paper embedded LAMP microchip offers inexpensive, user-friendly, and highly selective pathogen detection capabilities. It is expected to have great potential as a quick, efficient, and cost-effective solution for future foodborne pathogen detection.

The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.

Pig production not only consumes large water and land, but also emits substantial greenhouse gases. Scholars have used environmental footprint, including water, land, and carbon footprints, to assess the environmental impacts of pig production. However, there is a lack of simultaneous analysis of water, land, and carbon footprints for pig production, particularly in China, the leading pig breeding and consumption country in the world. In this study, we used environmental footprint analysis to develop a water-land-carbon footprint model for pig production system from the life cycle perspective. We also examined virtual water, land and carbon flows embedded in trade. The life cycle of pig production mainly involves feed crop cultivation, feed production, and pig breeding. Then we analyzed the water, land, and carbon footprints of the pig production in China from 1990 to 2018. The findings indicated that both the total water footprint and the total land footprint increased, whereas the total carbon footprints fluctuated over the years. All the unit footprints generally decreased. Feed crop cultivation had a significant impact on the footprints. Regarding the spatial characteristics in China, all the three total footprints were generally higher in the southeast compared to the northwest. While the unit footprints in different provinces exhibited spatial heterogeneity, primarily attributed to the different feed consumptions. Overall, the trade of feed crop shifted virtual water, land and carbon flows from the north to the south, reducing the three total footprints on the whole. To achieve a resource-saving and eco-friendly pig production in China, we proposed some potential recommendations, including improving feed crop cultivation techniques, reducing feed conversion ratio, implementing precision feeding, and managing manure. We hope not only mitigate the environmental impacts of China’s pig production, but also provide references for achieving agricultural sustainability in other regions of the world.

Orobanche cumama wallr. is the sunflower root parasitic weed with special life stage in which seed germination and parasitism take place in the soil. In practice, applying microbial agents and trapping crop rotation are utilized separately, or just one of them is selected to control O. cumana. The development of the sunflower industry is severely constrained on the farmland, where there is high density of O. cumana’s seed banks. In this study, two biological control methods were combined to solve the problem of O. cumana parasitism. The bioassay experiment showed that the high concentration fermentation filtrates of Streptomyces rochei D74 could effectively inhibit the germination and growth of the germ tube of O. cumana seeds. As the concentration was increased to 3.1 mg/mL, O. cumana was almost unable to sprout. A two-year pot experiment revealed that the use of D74 agents and sunflower–maize–sunflower rotation together promoted sunflower growth, as shown by the biomass accumulation, plant height, and denser root systems. The combined method resulted in a significant decrease in the number of O. cumana parasitism, compared to one method alone. Additionally, it affected the bacterial community composition of sunflower rhizosphere, mostly leading to an increase in Streptomyces and Brevibacterium and a decrease in Arthrobacter. This experiment, combined with multiple biological control, means significantly reducing the parasitism of O. cumana, which provides an effective foundation for practical application.

The interferometric imaging radar altimeter (InIRA) is a new generation radar altimeter, which can provide two-dimensional images of the sea surface topography at high resolution along a wide swath. This article proposes a method for the simulation of sea surface images measured by InIRA. First, the Pierson-Moskowitz wave spectrum and two-scale model are used to simulate the sea surface from which elevation data is to be acquired. This simulated sea surface is then divided into small triangular facets using Delaunay triangulation. Second, the backscattering cross sections of these small facets are calculated via application of quasi-mirror scattering theory, and the backscattering coefficient of the simulated region derived via coherent superposition. Third, system parameters are set, consistent with the basic principle of InIRA. Assuming that the signal transmitted is a linear frequency modulation pulse signal, the simulation images are then derived using the range Doppler and back projection algorithms. By inverting the interferometric phase diagram, elevation estimates can be derived, and compared with original simulated sea levels. This demonstrated accuracy within the centimeter range, verifying the correctness and feasibility of the proposed method.

Nucleic acid amplification is a sensitive and powerful tool for allergen detection. However, it is limited due to the relatively cumbersome methods required to extract nucleic acids from single plant seed allergen (e.g. peanut and soybean). In view of this, an approach of extracting nucleic acid with untreated glass-fiber paper (paper-based microfluidics) was applied for nucleic acid capture and purification from plant seed allergen and commercial products. After cut by hollow cylindrical cutter, a certain size the paper chip it used to absorb DNA. And this paper-based microfluidics with DNA was directly applied for amplification by loop-mediated isothermal amplification (LAMP). To evaluate the adsorption performance of paper chip to DNA, CTAB and SDS method were used as comparisons. From amplification results, the established technique has good specificity, high repeatability (C.V. values are 4.41% and 6.17% for peanut and soybean) and favorable sensitivity (7.39 ng/μL or peanut and 6.6 ng/μL for soybean), and successfully used for commercial products (2 kinds of candy and 2 kinds of cakes containing peanut, and 2 kinds of drinks, candy and 2 kinds of biscuits containing soybean). This speed and flexible detection method makes it suit for applications in point-of-care (POC) detection at different scenario, such as custom house and import port.

Rapid antidepressant effects of S-ketamine have repeatedly been confirmed in patients with depression, as well as in chronic unpredictable mild stress (CUMS) animal models. However, the pharmacological study of S-ketamine for anti-postpartum depression has not been considered. In this study, the classical method of reproductive hormone withdrawal was used to construct a rat model of postpartum depression (PPD). Subsequently, the study evaluated the effects of low-dose S-ketamine on behavior and synaptic plasticity, which is related to depression, in the hippocampus of PPD rats. Multiple behavioral tests were used to evaluate depression-like behaviors in PPD models. Synaptic plasticity of the hippocampus can be demonstrated by Western blot, Golgi staining, transmission electron microscopy, and electrophysiological recording. Our study provides insight into the role of low-dose S-ketamine in antidepressant as well as antianxiety and indicates that maintaining synaptic plasticity is a key target for S-ketamine therapy for postpartum depression induced by reproductive hormone withdrawal.

Chronic pain is an enormous modern public health problem, with significant numbers of people debilitated by chronic pain from a variety of etiologies. Translocator protein 18 kDa (TSPO) was discovered in 1977 as a peripheral benzodiazepine receptor. It is a five transmembrane domain protein, mainly localized in the outer mitochondrial membrane. Recent and increasing studies have found changes in TSPO and its ligands in various chronic pain models. Reversing their expressions has been shown to alleviate chronic pain in these models, illustrating the effects of TSPO and its ligands. Herein, we review recent evidence and the mechanisms of TSPO in the development of chronic pain associated with peripheral nerve injury, spinal cord injury, cancer, and inflammatory responses. The cumulative evidence indicates that TSPO-based therapy may become an alternative strategy for treating chronic pain.

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.

Suspended sediment concentration (SSC) is an important indicator of water quality that affects the biological processes of river ecosystems and the evolution of floodplains and river channels. The in situ SSC measurements are costly, laborious and spatially discontinuous, while the spaceborne SSC overcome these drawbacks and becomes an effective supplement for in situ observation. However, the spaceborne SSC observations of rivers are more challenging than those of lakes and reservoirs due to their narrow widths and the broad range of SSCs, among other factors. We developed a novel SSC retrieval method that is suitable for the rivers. Water was classified as clear or turbid based on the Forel–Ule index, and optimal SSC models were constructed based on the spectral responses to SSCs in cases of different turbidity. The estimated SSC had a strong correspondence with in situ measurements, with a root mean squared error (RMSE) of 24.87 mg/L and a mean relative error (MRE) of 51.91%. Satellite-derived SSC showed good consistency with SSCs obtained from gauging stations (r2 > 0.79). We studied the spatiotemporal variation in SSC in the Yangtze main stream from 2017 to 2021. It increased considerably from May to October each year, with the peak generally occurring in July or August (ca. 200–300 mg/L in a normal year and 800–1000 mg/L in a flood year), while it remained stable and decreased to around 50 mg/L from November to April of the following year. It was high in the east and low in the west, with local maxima in Chongqing (ca. 80–150 mg/L) and in the lower Dongting Lake reaches (ca. 80–100 mg/L) and a local minima in the downstream of the Three Gorges Dam (ca. 1–20 mg/L). Case studies in the Yibin reach and Three Gorges Reservoir determined that local variation in SSCs is due to special hydrodynamic conditions and anthropogenic activities. The procedure applied to process Sentinel-2 imagery and the novel SSC retrieval method we developed supplement the deficiencies in river SSC retrieval.

The high penetration of photovoltaic (PV) systems in low-voltage distribution networks relieves the pressure of energy shortages. However, the severe intermittency and variability in the output of PVs also bring a series of voltage quality problems, such as over-voltage, under-voltage, and voltage fluctuations. In this paper, a novel voltage control strategy based on air conditioners (ACs) and an on-load-tap-changer (OLTC) is proposed. By regulating the operating power of the ACs, voltage regulation powers are provided for the distribution network during the regulation time delays of the OLTC, which can effectively avoid the occurrence of regulation lag by the OLTC. The thermal model and electrical model are established to evaluate the operating performance of voltage regulation potential of the ACs. Furthermore, the voltage regulation strategy is formulated based on the regulation potential of the ACs, which it can make full use of. The effectiveness of the proposed voltage control strategy is illustrated in numerical studies.

In the north of China, during winter, a large number of activities (such as leisure, work, sightseeing and sports) are engaged in. This paper mainly focuses on human thermal perceptions of outdoor activities in a winter climate, especially the change in thermal perceptions when humans enter a temporary rest space with a shelter effect. A climate chamber was applied in our experiments, and temperature up-steps of 4, 8 and 12 °C were set, respectively. Twenty four college students were invited to engage in activities of different intensities, such as standing, walking (slowly) and biking. Through questionnaire survey and field measurement, the subjects’ thermal sensation, thermal comfort and skin temperature were obtained. Hypothesis testing and non-linear regression methods were introduced to analyze experiment data. Major results were as follows. After temperature up-step changes, thermal sensation and skin temperature reach steady state within 30 min. However, the change in skin temperature caused by a short-term thermal experience does not disappear completely within 40 min. In addition to the influence of ambient temperature changes, activity intensity also influenced the variation in thermal sensation (subjective) and skin temperature (objective). These study results provide a scientific reference for future research and design of a temporary rest space in low ambient temperatures. Experimental studies including broader age groups and outdoor field tests are valuable for future research.

Heating systems are essential municipal infrastructure in winter, especially in severe cold cities of China. The safety and efficiency of heating systems greatly affect building energy efficiency and indoor thermal comfort. Heating networks (HNs), playing the role of transportation, are the key parts of heating systems. In HNs, hydraulic models could be affected by the accuracy of resistance characteristic parameters, which are expressed by pipe friction parameters (PFPs) in this paper. As the uniqueness of the estimation results of PFPs has not been discussed in previous studies, this paper builds an estimation method of PFPs by dividing two types of pipes, substituting variables and establishing a split-step linearization method. Combining with the theory of matrix equations, the decision conditions and solution methods for obtaining the unique estimation results of PFPs are determined. Theoretical analysis and case study results show that estimation values of PFPs can be obtained by utilizing measured data under multiple hydraulic conditions. In the example of DN and the simple actual HN, the average estimation deviation of PFPs is 1.42% and 1.86%, which are accurate enough for actual engineering. Estimation results of PFPs obtained by this method guarantee the accuracy of analysis and regulation in heating systems and improve social energy utilization efficiency.

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues.

Perioperative neurocognitive disorders (PND) is a common postoperative neurological complication. Neuroinflammation is a major cause that leads to PND. Autophagy, an intracellular process of lysosomal degradation, plays an important role in the development and maintenance of nervous system. PK11195 is a classic translocator protein (TSPO) ligand, which can improve the cognitive function of rats. In this study, we evaluate the protective effect of PK11195 on the learning and memory of rats. A rat model of lipopolysaccharide (LPS)-induced cognitive dysfunction was established by intraperitoneal injection of LPS. Morris Water Maze (MWM), Western blot, qRT-PCR, confocal microscopy and transmission electron microscopy (TEM) were used to study the role of TSPO-specific ligand PK11195 in LPS-activated mitochondrial autophagy in rat hippocampus. We found that PK11195 ameliorated LPS-induced learning and memory impairment, as indicated by decreased escape latencies, swimming distances and increased target quadrant platform crossing times and swimming times during MWM tests. TSPO, ATG7, ATG5, LC3B and p62 protein and mRNA expression increased in the hippocampus of PND model rats. The hippocampal microglia of PND model rats also have severe mitochondrial damage, and a large number of autophagosomes and phagocytic vesicles can be seen. PK11195 pretreatment significantly decreased the expression of TSPO, ATG7, ATG5, LC3B and p62 protein and mRNA, as well as mitochondrial damage. These findings suggested that PK11195 may alleviate the damage of LPS-induced cognitive dysfunction of rats by inhibiting microglia activation and autophagy.

The composite slab with steel trusses is composed of precast bottom plate and cast-in-place concrete. In engineering applications, cracks often appear in the bottom plate before casting the upper concrete, which even leads to the failure of the composite slab. To improve the crack resistance of the slab, a composite slab with additional steel trusses is proposed; that is, on the basis of the original longitudinal steel trusses, the transverse steel trusses are added. Static test and numerical analysis were carried out on the bottom plate of the new type of composite slab with the additional transverse steel trusses. The experimental and analytical results show that the load level of the plate with additional steel trusses can be increased by 33% under the normal service limit state; the deflection of the plate is significantly reduced and the crack development is effectively controlled, which illustrates that the new type of composite slab can improve the bearing capacity, increase the bending stiffness, and enhance the crack resistance effectively.

Neuroinflammation related to microglial activation plays an important role in neurodegenerative diseases. Translocator protein 18 kDa (TSPO), a biomarker of reactive gliosis, its ligands can reduce neuroinflammation and can be used to treat neurodegenerative diseases. Therefore, we explored whether TSPO ligands exert an anti-inflammatory effect by affecting the nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome, thereby inhibiting the release of inflammatory cytokines in microglial cells. In the present study, BV-2 cells were exposed to lipopolysaccharide (LPS) for 6 h to induce an inflammatory response. We found that the levels of reactive oxygen species (ROS), NLRP3 inflammasome, interleukin-1β (IL-1β), and interleukin-18 (IL-18) were significantly increased. However, pretreatment with TSPO ligands inhibited BV-2 microglial and NLRP3 inflammasome activation and significantly reduced the levels of ROS, IL-1β, and IL-18. Furthermore, a combination of LPS and ATP was used to activate the NLRP3 inflammasome. Both pretreatment and post-treatment with TSPO ligand can downregulate the activation of NLRP3 inflammasome and IL-1β expression. Finally, we found that TSPO was involved in the regulation of NLRP3 inflammasome with TSPO ligands treatment in TSPO knockdown BV2 cells. Collectively, these results indicate that TSPO ligands are promising targets to control microglial reactivity and neuroinflammatory diseases.

By simulating the actual working conditions of a cable, the temperature variation rule of different measuring points under different load currents was analyzed. On this basis, a three-dimensional finite element model (FEM) was established, and the difference and influence factors between the simulation temperature and the experimental measured value were discussed, then the influence of thermal conductivity on the operating temperature of the conductor layer was studied. Finally, combined with the steady-state thermal conductivity model and the experimental measured data, the relation between thermal conductivity and load current was obtained.

In this study, based on the shipborne high-frequency (HF) surface wave radar target detection application, the first-order ocean surface cross-section for the shipborne HF surface wave radar is simulated, and the relationship between the sea clutter widening characteristics and platform speed is analysed. Then, the relationship between the target echo characteristics of shipborne HF surface wave radar with the platform speed and sails direction is analysed. Finally, combined with the characteristics of the target and sea clutter, a new scheme of target detection for the shipborne radar is proposed. That is, by changing the course of the shipborne platform motion, the target is separated from the widened sea clutter, thereby improving the target-detection performance of the shipborne HF surface wave radar.

Osteosarcoma (OS) is a common malignant primary bone tumor. Its mechanism of development and progression is poorly understood. Currently, there is no effective therapeutic regimens available for the treatment of OS. DEAD-box helicase 5 (DDX5) is involved in oncogenic processes. This study aimed to explore the role of DDX5 in the development and progression of OS and its relationship with transcription factor 12 (TCF12), which is as an important molecule of Wnt signaling pathway. We found that the expressions of DDX5 and TCF12 protein were significantly higher in OS patients tissues and in the MG63 cells than in the corresponding normal tissues and human osteoblast cell hFOB 1.19. Overexpressions of both DDX5 and TCF12 were associated with clinicopathological features and poor prognosis of OS patients. siRNA based knockdown of DDX5 inhibited the proliferation of MG63 cells as demonstrated by an in vitro MTS assay and 5-ethynyl-2-deoxyuridine DNA proliferation detection, and promoted apoptosis of MG63 cells measured by flow cytometry. In addition, DDX5 knockdown inhibited the MG63 cell migration and invasion on transwell assays. Further experiments showed that DDX5 knockdown not only inhibited the expression of TCF12 but also decreased the mRNA and protein levels of Cyclin E1, an important regulator of G1–S phase progression, suggesting that DDX5 was required for the entry of cells into S phase. Overexpression of TCF12 reversed the cell proliferation, migration and invasion in MG63 cells induced by DDX5 knockdown accompanied by the upregulation of Cyclin E1. Additionally, we observed that DDX5 interacted with TCF12 in both OS tissues and MG63 cells by Co-immunoprecipitation assays. Taken together, our study revealed that DDX5 interacts with TCF12 and promotes the progression of OS by stimulating cell cycle progression. Our results suggest that DDX5 and TCF12 could be potential biomarkers for the diagnosis and treatment of OS.

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

District heating networks (DHNs) are essential municipal infrastructure in the north of China. Obtaining accurate resistance characteristics is a critical step to improve the operating regulation level of DHNs. In this paper, pipe hydraulic resistances (PHRs) are introduced to express the resistance characteristics. A hydraulic model of a DHN can be established by using observed data of pressures and discharges. The boundary nodes are taken as observed sites. After establishing a matrix equation and analyzing the rank of its coefficient matrix, the authors propose a method to determine all the PHRs uniquely, by using a small number of observed sites and operating conditions. Furthermore, when observed errors are introduced, the adverse impact can be weakened by increasing the number of operating conditions and the accuracy of observed devices. When the observed error ranges are 1% and 0.5%, the results show that the average relative errors of identified PHRs are 2.4% and 1.1% respectively, which can be acceptable in engineering. Then, a loop DHN can be transformed into several branch DHNs, which are identified individually.

While in-situ estuarine discharge has been correlated and reconstructed well with localized remotely-sensed data and hydraulic variables since the 1990s, its correlation and reconstruction using averaged GPS-inferred water storage from satellite gravimetry (i.e., GRACE) at the basin upstream based on the water balance standardization (WBS) approach remains unexplored. This study aims to illustrate the WBS approach for reconstructing monthly estuarine discharge (in the form of runoff (R)) at Mekong River Delta, by correlating the averaged GPS-inferred water storage from GRACE of the upstream Mekong Basin with the in-situ R at the Mekong River Delta estuary. The resulting R based on GPS-inferred water storage is comparable to that inferred from GRACE, regardless of in-situ stations within Mekong River Delta being used for the R reconstruction. The resulting R from the WBS approach with GPS water storage converted by GRACE mascon solution attains the lowest normalized root-mean-square error of 0.066, and the highest Pearson correlation coefficient of 0.974 and Nash-Sutcliffe efficiency of 0.950. Regardless of using either GPS-inferred or GRACE-inferred water storage, the WBS approach shows an increase of 1–4% in accuracy when compared to those reconstructed from remotely-sensed water balance variables. An external assessment also exhibits similar accuracies when examining the R estimated at another station location. By comparing the reconstructed and estimated Rs between the entrance and the estuary mouth, a relative error of 1–4% is found, which accounts for the remaining effect of tidal backwater on the estimated R. Additional errors might be caused by the accumulated errors from the proposed approach, the unknown signals in the remotely-sensed water balance variables, and the variable time shift across different years between the Mekong Basin at the upstream and the estuary at the downstream.

In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture.

The Mt-HAP composites were achieved by combining layered montmorillonite (Mt) and hydroxyapatite (HAP) nanoparticles produced by in situ assembly technique. Amoxicillin (AMX) loading and release experiment proved that the synthetic Mt-HAP composites demonstrated high drug loading ability and pH-responsive sustained release property. The AMX load of original Mt was 18.5 mg g ^−1 , while that of Mt-HAP grew to 49.1 mg g ^−1 . Experiments in simulated gastric fluid (pH 1.2) release indicated that Mt drug carrier having a higher release rate of AMX within the initial 2 h. But after that, the drug release rate of AMX from Mt-HAP has a greater value (about 65% over 12 h) than that of Mt (about 50% over 12 h) because of the dissolution of HAP under acidic circumstance. However, the cumulative sustained release rate of Mt-HAP in simulated intestinal fluid (pH 6.8) over 12 h was only 30%, and the drug release amount of Mt was still about 49%. Compared to Mt@AMX, the drug release rate of Mt-HAP @ AMX is sensitive to changes in pH. The findings claimed that the Mt-HAP composite exhibited extreme potential as a drug carrier for controllable drug delivery.

The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.

Global navigation satellite systems (GNSS) techniques, such as GPS, can be used to accurately record vertical crustal movements induced by seasonal terrestrial water storage (TWS) variations. Conversely, the TWS data could be inverted from GPS-observed vertical displacement based on the well-known elastic loading theory through the Tikhonov regularization (TR) or the Helmert variance component estimation (HVCE). To complement a potential non-uniform spatial distribution of GPS sites and to improve the quality of inversion procedure, herein we proposed in this study a novel approach for the TWS inversion by jointly supplementing GPS vertical crustal displacements with minimum usage of external TWS-derived displacements serving as pseudo GPS sites, such as from satellite gravimetry (e.g., Gravity Recovery and Climate Experiment, GRACE) or from hydrological models (e.g., Global Land Data Assimilation System, GLDAS), to constrain the inversion. In addition, Akaike’s Bayesian Information Criterion (ABIC) was employed during the inversion, while comparing with TR and HVCE to demonstrate the feasibility of our approach. Despite the deterioration of the model fitness, our results revealed that the introduction of GRACE or GLDAS data as constraints during the joint inversion effectively reduced the uncertainty and bias by 42% and 41% on average, respectively, with significant improvements in the spatial boundary of our study area. In general, the ABIC with GRACE or GLDAS data constraints displayed an optimal performance in terms of model fitness and inversion performance, compared to those of other GPS-inferred TWS methodologies reported in published studies.

Based on a geophysical model for elastic loading, the application potential of Global Positioning System (GPS) vertical crustal displacements for inverting terrestrial water storage has been demonstrated using the Tikhonov regularization and the Helmert variance component estimation since 2014. However, the GPS-inferred terrestrial water storage has larger resulting amplitudes than those inferred from satellite gravimetry (i.e., Gravity Recovery and Climate Experiment (GRACE)) and those simulated from hydrological models (e.g., Global Land Data Assimilation System (GLDAS)). We speculate that the enlarged amplitudes should be partly due to irregularly distributed GPS stations and the neglect of the terrain effect. Within southwest China, covering part of southeastern Tibet as a study region, a novel GPS-inferred terrestrial water storage approach is proposed via terrain-corrected GPS and supplementary vertical crustal displacements inferred from GRACE, serving as "virtual GPS stations" for constraining the inversion. Compared to the Tikhonov regularization and Helmert variance component estimation, we employ Akaike’s Bayesian Information Criterion as an inverse method to prove the effectiveness of our solution. Our results indicate that the combined application of the terrain-corrected GPS vertical crustal displacements and supplementary GRACE spatial data constraints improves the inversion accuracy of the GPS-inferred terrestrial water storage from the Helmert variance component estimation, Tikhonov regularization, and Akaike’s Bayesian Information Criterion, by 55%, 33%, and 41%, respectively, when compared to that of the GLDAS-modeled terrestrial water storage. The solution inverted with Akaike’s Bayesian Information Criterion exhibits more stability regardless of the constraint conditions, when compared to those of other inferred solutions. The best Akaike’s Bayesian Information Criterion inverted solution agrees well with the GLDAS-modeled one, with a root-mean-square error (RMSE) of 3.75 cm, equivalent to a 15.6% relative error, when compared to 39.4% obtained in previous studies. The remaining discrepancy might be due to the difference between GPS and GRACE in sensing different surface water storage components, the remaining effect of the water storage changes in rivers and reservoirs, and the internal error in the geophysical model for elastic loading.

Osteosarcoma (OS) is an aggressive bone tumor, and proto-oncogene c-Fos is involved in this lethal disease. However, the role and molecular mechanism of c-Fos in the development and progression of OS remain enigmatic. As one of the Wnt family members, Wnt2 is closely associated with the development of several malignant tumors. In the present study, the expression of c-Fos, Wnt2, and its receptor Fzd9 in human OS tissues, MG63 OS cell line, and human osteoblast hFOB 1.19 cell line was detected by Western blot analysis, immunohistochemical staining, or reverse transcription-polymerase chain reaction. The role of c-Fos in the OS was clarified by treating MG63 cells with small interfering RNA to knockdown c-Fos. Then, cell migration and invasion were assayed by transwell assays and wound healing assay; cell proliferation was assayed by MTS method and 5-ethynyl-2'-deoxyuridine DNA proliferation in vitro detection; cell apoptosis was assayed by flow cytometric method. Co-immunoprecipitation kit was used to confirm the relationship between c-Fos and Wnt2/Fzd9. We found that the expression of c-Fos, Wnt2, and Fzd9 protein was distinctly higher in human OS tissues than that in the adjacent non-cancerous tissues, and their expression in the MG63 OS cell line was markedly increased compared with that in the human osteoblast hFOB 1.19 cell line. Knockdown of c-Fos inhibited the proliferation, migration, and invasion of MG63 cells, and promoted the apoptosis of MG63 cells. Moreover, knockdown of c-Fos inhibited the expression of Wnt2 and Fzd9 mRNA and protein. Our data enforced the evidence that knockdown of c-Fos inhibited cell proliferation, migration, and invasion, and promoted the apoptosis of OS cells accompanied by altered expression of Wnt2 and Fzd9. These findings offer new clues for OS development and progression, and c-Fos may be a potential therapeutic target for OS.

The rapid development of vision sensor based on artificial intelligence (AI) is reforming industries and making our world smarter. Among these trends, it is of great significance to adapt AI technologies into the intelligent agricultural management. In smart agricultural aviation spraying, the droplets’ distribution and deposition are important indexes for estimating effectiveness in plant protection process. However, conventional approaches are problematic, they lack adaptivity to environmental changes, and consumes non-reusable test materials. One example is that the machine vision algorithms they employ can’t guarantee that the division of adhesive droplets thereby disabling the accurate measurement of critical parameters. To alleviate these problems, we put forward an intelligent visual droplet detection node which can adapt to the environment illumination change. Then, we propose a modified marker controllable watershed segmentation algorithm to segment those adhesive droplets, and calculate their characteristic parameters on the basis of the segmentation results, including number, coverage, coverage density, etc. Finally, we use the intelligent node to detect droplets, and then expound the situation that the droplet region is effectively segmented and marked. The intelligent node has better adaptability and robustness even under the condition of illumination changing. The large-scale distributed detection result indicates that our approach has good consistency with the non-recyclable water-sensitive paper approach. Our approach provides an intelligent and environmental friendly way of tests for spraying techniques, especially for plant protection with Unmanned Aerial Vehicles.

In recent years, UAVs (Unmanned Aerial Vehicles) have been widely applied for data collection and image capture. Specifically, UAVs have been integrated with wireless sensor networks (WSNs) to create data collection platforms with high flexibility. However, most studies in this domain focus on system architecture and UAVs’ flight trajectory planning while event-related factors and other important issues are neglected. To address these challenges, we propose a cloud-assisted data gathering strategy for UAV-based WSN in the light of emerging events. We also provide a cloud-assisted approach for deriving UAV’s optimal flying and data acquisition sequence of a WSN cluster. We validate our approach through simulations and experiments. It has been proved that our methodology outperforms conventional approaches in terms of flying time, energy consumption, and integrity of data acquisition. We also conducted a real-world experiment using a UAV to collect data wirelessly from multiple clusters of sensor nodes for monitoring an emerging event, which are deployed in a farm. Compared against the traditional method, this proposed approach requires less than half the flying time and achieves almost perfect data integrity.