National Center for Science and Technology Evaluation

Although zinc oxide nanoparticles (ZnONPs) are widely used, they have raised concerns of toxicity in humans. Previous studies have indicated that reactive oxygen species (ROS) and autophagy are involved in the cytotoxicity of ZnONPs, but the regulatory mechanisms between autophagy and ROS remain to be elucidated. Herein, we comprehensively investigated the regulatory mechanism of autophagy and the link between autophagy and ROS in ZnONPs-treated lung epithelial cells. We demonstrated that ZnONPs could induce autophagy, and this process could enhance the dissolution of ZnONPs in lysosomes to release zinc ions. Sequentially, zinc ions released from ZnONPs were able to damage not only lysosomes, leading to impaired autophagic flux, but also mitochondria. Impaired autophagic flux resulted in the accumulation of damaged mitochondria, which could generate excessive ROS to cause cell death. We further demonstrated that the inhibition of autophagy by either pharmacological inhibitors or small interfering RNA (siRNA)-mediated knockdown of Beclin-1 and AMP-activated protein kinase could ameliorate ZnONPs-induced cell death. Moreover, we found that lysosomal-associated membrane protein 1/2 (LAMP-1/2), which were the most abundant highly glycosylated protein in late endosomes/lysosomes, exhibited aberrant expression pattern upon treatment with ZnONPs. Intriguingly, LAMP-2 knockdown, but not LAMP-1 knockdown, could exacerbate the ROS generation and cell death induced by ZnONPs treatment. Meanwhile, LAMP-2 overexpression alleviated ZnONPs-induced cell death, suggesting that LAMP-2 was linked to this toxic phenotype induced by ZnONPs. Our results indicate that autophagic dysfunction could contribute to excessive ROS generation upon treatment with ZnONPs in lung epithelial cells, suggesting that modulating the autophagy process would minimize ZnONPs-associated toxicity.

Population is one of the core elements of sustainable development. Quantifying the estimation accuracy of population spatial distribution has been recognized as a critical and challenging task. This study aims to evaluate the data accuracy of four population datasets in China, including three global gridded population datasets, the Gridded Population of the World (GPW), Global Rural and Urban Mapping Project (GRUMP), and WorldPop project (WorldPop), and a Chinese regional gridded population dataset, the China 1 km Gridded Population (CnPop) dataset. These datasets are assessed using a specific method based on a GIS-linked 2000 census dataset at the township level in China. The results indicate that WorldPop had the highest estimation accuracy, estimating about 60% of the total population. CnPop accurately estimated about half of the total population, showing a good mapping performance. The GPW had an acceptable estimation accuracy in a few plain and basin areas, accounting for about 30% of the total population. Compared to the GPW, GRUMP accurately estimated about 40% of the total population. The relative estimation error analysis discovered the disadvantages of the generation strategies of these datasets. The conclusions are expected to serve as a quality reference for potential dataset users and producers, and promote accuracy assessment for population datasets in other regions and globally.

As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research.

In this article we examine some sequences of teacher–student interaction in which a teacher generates and acts on formative assessment data. We look at the teacher's practices of question construction and her decisions about in situ next pedagogical steps made in real time to support and further student learning. Our observations are guided by the following research questions: (a) What are the interactional practices that constitute formative assessment? (b) Are there observable classroom routines and organization that support these interactional practices? Our observations suggest that open and respectful pedagogical questioning is a key resource in eliciting students' current learning status, and for making decisions about next steps in student learning. Stable classroom routines and mutually understood interactional goals and practices are significant supports for these processes.

Abstract Crystals of γ‐HNIW were transformed into crystals of ε‐HNIW by application of a drowning‐out process in the presence of different additives, namely ethylene glycol, triacetin, and aminoacetic acid. They show different effects on the crystal morphology of ε‐HNIW and cause less angular and more regular structures. Investigation of the sensitivities of the different ε‐HNIW crystals shows that their angles and regularity have an influence on the impact sensitivity. Aminoacetic acid selectively inhibits the growth of individual ε‐HNIW crystal faces to modify the morphology into spherical shape, these ε‐HNIW crystals are of much lower sensitivity, even compared with general RDX and HMX explosives.

SUMMARY Recent advances on the Internet of Things (IoT) have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real‐world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real‐time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial‐temporal searches and value‐based approximate searches, as IoT sampling data are generally from spatial‐temporal scenario. To meet these challenges, we propose a ‘Hybrid Real‐time Search Engine Framework for the Internet of Things based on Spatial‐Temporal, Value‐based, and Keyword‐based Conditions’ (‘IoT‐SVK Search Engine’ or simply ‘IoT‐SVKSearch’ for short) in this paper. The experiments show that the IoT‐SVK search engine has satisfactory performances in supporting real‐time, multi‐modal retrieval of massive sensor sampling data in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.

The Qinling-Daba Mountains region (Qinba) is an important geographical transitional zone across the north and south of China. To comprehensively understand the ecological transition in the Qinba over past two decades, this study assessed and predicted the spatiotemporal variations of vegetation comparatively, using 250-m time series MODIS NDVI and EVI products. From 2000 to 2019, remarkable increases were observed both in annual and seasonal NDVI and EVI (P < 0.05), and the increasing rate of NDVI was higher than that of EVI for all temporal scales, except summer. Compared to NDVI, larger areas of no significant change were obtained in annual, autumn, and winter EVI, primarily distributed in the high-altitude regions of western Qinba. All assessed vegetation types increased significantly during past two decades except alpine vegetation and marsh, however, the seasons in which the significant increase in NDVI and EVI occurred varied for different vegetation types. Hurst exponent analysis suggested that inconsistent characteristics of vegetation dynamic trends were stronger in the future across Qinba. The area proportion of unfavorable trends, identified mainly covered with cultivated vegetation and scrub, was much larger than that of favorable trends, especially detected by EVI. Areas likely to experience vegetation variation of unfavorable and undetermined trends deserve high focus.

Studying the spatial and temporal water distribution in the Lake Baikal Basin, which hosts the freshwater lake with the most water storage in the world, is essential to understand the water resources and environment of the basin and its impact and influence in terms of climate change and disaster prevention and mitigation. The basin spans two countries, Russia and Mongolia, which, along with its vastness, makes it challenging to accurately automate the acquisition of large-scale and long-term series data. The Google Earth Engine (GEE) is capable of processing large amounts of remote sensing imagery but does not support the computation and application of deep learning models. This study uses a combination of local deep learning training and GEE cloud-based big data intelligent computing to empower GEE with deep learning computing power, enabling it to rapidly automate the deployment of deep learning models. Visible light, near infrared (NIR), modified normalized difference water index (MNDWI), short-wave infrared 1 (SWIR1), linear enhancement band (LEB), and digital elevation model (DEM), which are more sensitive to water bodies, were selected as input features, along with the optimized input features of the existing pixel-based convolutional neural network (CNN) model. This method corrects the initial water labels from the Landsat quality assessment bands to reduce the time cost of manually drawing the labels and improving the classification accuracy of the water bodies. On average, only 1–2 h are required to generate the results for each water body product for each period in Lake Baikal Basin. The extraction of water bodies from the Lake Baikal Basin was achieved for nine yearly periods between 2013 and 2021. The validation accuracy was 92.9 %, 92.7 %, and 92.4 % for the three years 2013, 2017 and 2021, respectively. The results showed that the mean area of water bodies in the basin was 37,500 km2 and that the area of water bodies in the basin fluctuated without significant change from 2013 to 2021. This study provides methodological support for the continuous monitoring and assessment of water body dynamics at more catchment scales and other large scenarios.

In this study we investigated the feasibility of a novel user interface to support the measurement of problem-solving processes.Our research questions addressed the use of a "click-through" interface to measure the "generate-and-test" problem-solving process for a design problem.A click-through interface requires the user to explicitly perform an online action (e.g., to view time, the user has to click on a "time" icon).This interface allowed us to measure participants' intentional acts.Freshman college students were given the task of modifying a given, computer-interactive bicycle pump to satisfy performance requirements.The simulation interface provided participants with pointand-click access to controls to modify pump parameters, to run the simulation, to view important information, and to attempt to solve the task.Lag sequential analyses of participants' problem-solving processes over time showed cyclical behavior consistent with the generate-and-test strategy of modifying the pump design, running the simulation, viewing the information, and then either modifying the design or attempting to solve the problem and then modifying the design again.This behavior set was remarkably stable, with most lag associations greater than .80.Our approach to measuring problem-solving processes appears feasible and promising, but more work is needed to gather additional validity evidence.

The coexistence of microorganisms in complex soil environments greatly affects the environmental behavior and ecological effects of microplastics (MPs). However, relevant studies are sparse, and internal mechanisms remain unclear. Herein, arbuscular mycorrhizal fungi (AMF), a common symbiotic microorganism in the soil-plant system, was proved to significantly affect MPs absorption and migration with a "size effect". Specifically, the existence of AMF accelerated small-sized MPs (0.5 μm) uptake but slowed large-sized MPs (2 μm) uptake in lettuce. The content of 0.5 μm MPs absorbed by plants with AMF was 1.26 times that of the non-AMF group, while the content of 2 μm MPs was only 77.62 % that of non-AMF group. Additionally, the different effects of microorganisms on the intake content of MPs with different particle sizes in plants also led to different toxic effects of MPs on lettuce, that is, AMF exacerbated small-size MPs toxicity in lettuce (e.g., reduced plant biomass, photosynthesis, etc), and it weakened large-sized MPs toxicity (e.g., increased plant height, antioxidant enzyme activity, etc). The above phenomenon mainly because of the change in AMF on the plant root structure, which can be visually observed through the intraradical and extraradical hyphae. The symbiotic structure (hyphae) formed by AMF and host plants root could enhance the absorption pathway for small-sized MPs in lettuce, although not for large-sized MPs. Additionally, the effects of AMF varied with the soil environment of differently sized MPs, which promoted the migration of small-particle MPs to plants but aggravated large-particle MPs fixation at the soil interface. These findings could deepen the understanding of MPs pollution in terrestrial systems and provide theoretical basis and technical support to accurately assess soil MPs pollution.

In this study, we investigated the physicochemical properties of soil, and the diversity and structure of the soil ammonia-oxidizing archaea (AOA) community, when subjected to fertilizer treatments for over 35 years. We collected soil samples from a black soil fertilization trial in northeast China. Four treatments were tested: no fertilization (CK); manure (M); nitrogen (N), phosphorus (P), and potassium (K) chemical fertilizer (NPK); and N, P, and K plus M (MNPK). We employed 454 high-throughput pyrosequencing to measure the response of the soil AOA community to the long-term fertilization. The fertilization treatments had different impacts on the shifts in the soil properties and AOA community. The utilization of manure alleviated soil acidification and enhanced the soybean yield. The soil AOA abundance was increased greatly by inorganic and organic fertilizers. In addition, the community Chao1 and ACE were highest in the MNPK treatment. In terms of the AOA community composition, Thaumarchaeota and Crenarchaeota were the main AOA phyla in all samples. Compared with CK and M, the abundances of Thaumarchaeota were remarkably lower in the MNPK and NPK treatments. There were distinct shifts in the compositions of the AOA operational taxonomic units (OTUs) under different fertilization management practices. OTU51 was the dominant OTU in all treatments, except for NPK. OTU79 and OTU11 were relatively abundant OTUs in NPK. Only Nitrososphaera AOA were tracked from the black soil. Redundancy analysis indicated that the soil pH and soil available P were the two main factors that affected the AOA community structure. The abundances of AOA were positively correlated with the total N and available P concentrations, and negatively correlated with the soil pH.

Vegetation coverage has changed substantially in the Yellow River Basin in recent decades, which may have influenced the interaction between midstream and upstream hydrological processes. On the basis of trend analysis and grey correlation analysis, we used structural equation modeling to explore relationships between hydrology and vegetation, and to clarify how these relationships affect the hydrological interconnections between the upper and mid-lower reaches of the Yellow River Basin. During the observation period, the normalized difference vegetation index showed an increasing trend. The area of grassland and cropland decreased, while the area of shrubland and forest increased. Precipitation, evapotranspiration and total water consumption increased. Precipitation, total water consumption, and vegetation dynamics exerted stronger influence than temperature on upstream export runoff. Upstream export runoff influenced midstream runoff to an even greater extent than the local vegetation and meteorological conditions. Our results suggest that upstream vegetation dynamics should be carefully considered when exploring the ecohydrology of the middle and lower reaches of the Yellow River Basin. These findings may provide a theoretical basis for implementation of ecological restoration projects in the upper parts of the Yellow River Basin.

Abstract The spatiotemporal distribution of lakes in the Yangtze River basin (YRB) has changed tremendously; however, research on how and why lakes changed across this basin is limited. In this study, based on Google Earth Engine, Landsat images were used to track lakes (&gt;1 km 2 ) across the YRB from 2000 to 2019. Then, the anthropogenic and climatic impacts on the evolution of lakes were fully discussed. Results showed that the distribution of lakes across the YRB was extremely uneven. From 2000 to 2019, the total number and the area (referring to the surface area) of lakes increased by 30 and declined by 885 km 2 , respectively, but the trends of them were unobvious. In contrast, these changes and related causes exhibited high spatial heterogeneity. Lakes in the upper reaches significantly increased ( P &lt; 0.01). The expanded and new lakes were mainly distributed in the headwater catchment of the Yangtze River, which was closely related to increased precipitation. The number of lakes in the middle reaches decreased significantly ( P &lt; 0.05), which was primarily affected by intense human activities, such as land reclamation and agricultural irrigation. Precipitation played a dominant role in the fluctuation of the lake area here. In the lower reaches, the number of lakes increased ( P &lt; 0.01) as a result of the policy of “returning farmland to lakes,” while the expansion of area ( P &lt; 0.05) was closely related to the precipitation and runoff. These findings have important policy implications for the conservation of lake resources.

Supply chain finance (SCF) plays an increasingly important role in global enterprise competition. The credit risk accompanying SCF has attracted the attention of the government, enterprises, and academia. However, with the absence of data and inaccurate information, traditional risk assessment methods are frequently failed to assess the credit risk in SCF, especially for small‐ and medium‐sized enterprises (SMEs). In this study, a grey correlation model is introduced and applied to the SCF risk assessment process for 15 firms in the Chinese home appliance industry with 15 performance indicators that represent profitability, solvency, operational capability, and development capability. The empirical study displays the operability and effectiveness of the grey correlation model, which is superior to traditional methods in the supply chain financial risk assessment.

In this paper, we focus on mining attacks targeting the Proof of Work (PoW) consensus mechanism in blockchain-based systems. Specifically, we model mining as a game and propose a mining attack &#x2013; the Selfish mining-based denial of service (SDoS) attack. By studying the choices (mining or stopping) of honest miners under the attack and the adversary&#x2019;s revenue, we demonstrate that selfish mining is incentive-compatible with game-level denial of service attack, and that SDoS can be more threatening than existing mining attacks. Even under the worst assumption, the adversary only needs to master more than 19.6% of the total mining power to increase the revenue, and can launch a 51% attack with much less than 50%. In addition, we show that honest miners may make decisions based on the overall or current utility, and choosing the current utility is more beneficial to the adversary.

Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.

Water yield (WY) refers to the difference between precipitation and evapotranspiration (ET), which is vital for available terrestrial water. Climate change has led to significant changes in precipitation and evapotranspiration on a global scale, which will affect the global WY. Nevertheless, how terrestrial WY has changed during the past few decades and which factors dominated the WY changes are not fully understood. In this study, based on climate reanalysis and remote sensing data, the spatial and temporal patterns of terrestrial WY were revisited from 1981 to 2018 globally using an improved Mann-Kendall trend test method with a permutation test. The response patterns of WY to precipitation and ET are also investigated. The results show that the global multi-year mean WY is 297.4 mm/a. Based on the traditional Mann-Kendall trend test, terrestrial WY showed a significant (p &lt; 0.05) increase of 5.72% of the total valid grid cells, while it showed a significant decrease of 7.68% of those. After correction using the calibration method, the significantly increasing and decreasing areas are reduced by 10.52% and 10.58% of them, respectively. After the correction, the confirmed increase and decrease in WY are mainly located in Africa, eastern North America and Siberia, and parts of Asia and Oceania, respectively. The dominant factor for increasing WY is precipitation, while that for decreasing WY was the combined effect of precipitation and evapotranspiration. The achievements of this study are beneficial for improving the understanding of WY in response to hydrological variables in the context of climate change.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is an international public health threat, and people's participation in disease-related preventive behaviours is the key to controlling infectious diseases. This study aimed to assess the differences in adopting preventive behaviours among populations to explore potential individual and household factors and inequalities within families. METHODS: This online survey was conducted in April 2020. The directional stratified convenient sampling method was used to select 4704 participants from eight provinces in eastern, central, and western China. The questionnaire included demographic information, household variables, and five target prevention behaviours. The chi-squared test, binary multilevel model, and Mantel-Haenszel hierarchical analysis were used for data analysis in the study. RESULTS: Approximately 71.2% of the participants had appropriate outdoor prevention, and 32.9% of the participants had indoor protection in place. Sharing behaviours (P < 0.001) and education level (P < 0.001) were positively associated with adopting preventive measures. The inhibiting effect of household crowding and stimulating effect of high household income on preventive behaviours were determined in this study. Household size was negatively associated with living area (β = -0.057, P < 0.05) and living style (β = -0.077, P < 0.05). Household income was positively associated with age (β = 0.023, P < 0.05), and relationship with friends (β = 0.053, P < 0.05). Vulnerable groups, such as older adults or women, are more likely to have inadequate preventive behaviours. Older adults (OR = 1.53, 95% CI 1.09-2.15), women (OR = 1.37, 95% CI 1.15-1.64), and those with more than 2 suspected symptoms (OR = 1.85, 95% CI 1.07-3.19) were more likely to be affected by the inhibiting effect of household crowding, while the stimulating effect of high household income was limited in these groups. CONCLUSIONS: Inequalities in COVID-19 prevention behaviours exist between families and inadequate adoption of prevention by vulnerable groups are noteworthy. This study expands the research perspective by emphasizing the role of household factors in preventive behaviours and by focusing on family inequalities. The government should use traditional media as a platform to enhance residents' public health knowledge. Targeted additional wage subsidies, investments in affordable housing, financial support for multigenerational households, and temporary relocation policies may deserve more attention. Communities could play a critical role in COVID-19 prevention.

The microorganisms that co-exist between soil and rice systems in heavy metal-contaminated soil environments play important roles in the heavy metal pollution states of rice, as well as in the growth of the rice itself. In this study, in order to further examine the effects of soil microorganisms on the mercury (Hg) uptake of rice plants and determine potential soil phytoremediation agents, an enriched 199Hg isotope was spiked in a series of pot experiments to trace the absorption and migration of Hg and rice growth in the presence of arbuscular mycorrhizal fungi (AMF). It was observed that the AMF inoculations significantly reduced the Hg concentration in the rice. The Hg concentration in rice in the AMF inoculation group was between 52.82% and 96.42% lower than that in the AMF non-inoculation group. It was also interesting to note that the presence of AMF tended to cause Hg (especially methyl-Hg (Me199Hg)) to migrate and accumulate in the non-edible parts of the rice, such as the stems and leaves. Under the experimental conditions selected in this study, the proportion of Me199Hg in rice grains decreased from 9.91% to 27.88%. For example, when the exogenous Hg concentration was 0.1 mg/kg, the accumulated methyl-Hg content in the grains of the rice in the AMF inoculation group accounted for only 20.19% of the Me199Hg content in the rice plants, which was significantly lower than that observed in the AMF non-inoculated group (48.07%). AMF also inhibited the absorption of Hg by rice plants, and the decrease in the Hg concentration levels in rice resulted in significant improvements in growth indices, including biomass and micro-indexes, such as antioxidant enzyme activities. The improvements occurred mainly because the AMF formed symbiotic structures with the roots of rice plants, which fixed Hg in the soil. AMF also reduce the bioavailability of Hg by secreting a series of substances and changing the physicochemical properties of the rhizosphere soil. These findings suggest the possibility of using typical co-existing microorganisms for the remediation of soil heavy metal contamination and provide valuable insights into reducing human Hg exposure through rice consumption.

The changes in land cover patterns in the Mongolian Plateau can reveal the regional status of sustainable development. Based on land cover data from 1990–2020, the study reveals the process of land cover change on the Mongolian plateau and integrates those changes with UN Sustainable Development Goals (SDGs) to further evaluate regional sustainable development status. Result revealed there is a stable rate of land cover change (0.16%) for the Mongolian Plateau, but with diverse shifting trends for various land cover types and SDGs indicators in past 30 years. Croplands (SDG2) showed a growth trend in the last five years, which was different from its initial obviously decreasing trend. The status of water (SDG6) showed a clear decreasing trend, which presents a major threat to this arid-to-semi-arid region. The built area (SDG11) increased continuously, but the long upward trend has slowed in recent years. The forest area (SDG15) declined, but it has recently recovered. Grasslands showed diverse changes in various steppe types (including real, meadow, and desert steppe types) while still experiencing land degradation. The expansion of sand areas presents a hidden risk of increasing sandstorms. Comparative analysis revealed that there have clear differences between Mongolia and Inner Mongolia due to the various government policies. In general, the land use degree in Mongolian Plateau increased annually. This indicated that the climate change and human activities have more and more influences, and it is still facing severe challenges for specific SDGs indicators in the region.