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Simple Summary: As a zoonotic pathogen, the Hepatitis E virus (HEV) is extensively distributed worldwide. Animal infections are typically asymptomatic, while human infections can result in both acute and chronic viral hepatitis. The transmission pathway is typically a fecal–oral one. Pigs are the primary host of HEV genotype 4 (HEV-4), which primarily causes sporadic infections in China. The aim of this study was to evaluate HEV infection in certain farms in Guangdong, China. Twenty-five pig farms provided 1568 blood samples, of which 902 (902/1568, 57.53%) were positive for anti-HEV IgG. Such a high serum positive rate indicates that pigs in Guangdong are widely exposed to HEV, which is a considerable public health and safety concern. The Hepatitis E virus (HEV) causes acute and chronic Hepatitis E and is a global public health concern. HEV genotypes 3 (HEV-3) and 4 (HEV-4) are common to humans and animals, and domestic pigs and wild boars have been identified as the main reservoirs. However, limited information is available on the status of HEV infection in pigs, particularly in the Guangdong Province, China. This study aimed to investigate the seroprevalence of HEV in pig farms within the Guangdong Province. A total of 1568 serum samples were collected from 25 farms and tested for anti-HEV IgG antibodies. Enzyme-linked immunosorbent assay (ELISA) results revealed that 57.53% (902/1568) of serum samples from 24 farms (24/25, 96%) were positive for anti-HEV IgG antibodies. Year, season, region, and age were all linked risk factors for HEV in Guangdong, with season and region showing more significant impacts. The results showing a high seroprevalence of HEV confirmed its circulation among domestic pigs in the Guangdong Province, China. The presence of this antibody indicates that HEV infection was or is present on farms, posing a risk of zoonotic transmission of HEV from pigs to exposed workers and from pork or organs to consumption. [ABSTRACT FROM AUTHOR]

The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) "The greatest truths are concise" is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of vehicle ownership, energy consumption, and carbon emissions in Guangdong Province, China, from 2020 to 2035 under different scenarios. Key findings highlight the province's pursuit of carbon peak goals and provide valuable insights into strategies to achieve them. Vehicle ownership in Guangdong is projected to exceed 48 million by 2035, which represents a doubling from 2020. Under both scenarios, internal combustion engine vehicle ownership will peak around 2030 and then gradually decline, while under the enhanced scenario, electric vehicle ownership will exceed 40% by 2035. Enhanced vehicle energy efficiency and reduced annual mileage will lead to a 17% reduction in gasoline and diesel consumption by 2035 in both scenarios. At the same time, there will be a substantial five- to six-fold increase in electricity consumption for vehicles compared to 2020. Both scenarios peak in carbon emissions before 2030, with the enhanced scenario achieving this peak a year earlier. The enhanced scenario outperforms the baseline, reducing carbon emissions by about 21.2% from the peak and 8% relative to 2020. Pure electric vehicles exhibit a significant advantage in reducing carbon emissions per vehicle compared to their internal combustion engine counterparts. Encouraging new energy vehicles, especially pure electric ones, accelerates the carbon emissions peak and lowers overall peak emissions. Accelerating the adoption of electric vehicles, reducing per-vehicle fuel consumption and annual average mileage, and optimizing transportation modes are crucial for carbon peaking from the vehicle fuel cycle. Policy recommendations focus on promoting new energy vehicles, optimizing transportation, and advancing research and technology. [ABSTRACT FROM AUTHOR]

Simple Summary: Shitou geese are the exclusive representative of a large-sized goose breed in China, typically exhibiting gray feathers but also possessing a recessive white plumage. However, the precise mechanism underlying the formation of this white plumage coloration in Shitou geese remains unclear. Here, a transcriptome sequencing analysis was conducted to identify candidate genes and microRNAs (miRNAs) that may be associated with the white plumage color in Shitou geese. A number of pigmentation genes and miRNAs were identified as candidates that may regulate white plumage color formation in Shitou geese. This result may be important for understanding the genetic mechanism underlying the formation of white plumage color in Shitou geese. The Shitou goose, a highly recognized indigenous breed with gray plumage originating from Chaozhou Raoping in Guangdong Province, China, is renowned for being the largest goose species in the country. Notably, during the pure breeding process of Shitou geese, approximately 2% of the offspring in each generation unexpectedly exhibited white plumage. To better understand the mechanisms underlying white plumage color formation in Shitou geese, we conducted a comparative transcriptome analysis between white and gray feather follicles, aiming to identify key genes and microRNAs that potentially regulate white plumage coloration in this unique goose breed. Our results revealed a number of pigmentation genes, encompassing TYR, TYRP1, EDNRB2, MLANA, SOX10, SLC45A2, GPR143, TRPM1, OCA2, ASIP, KIT, and SLC24A5, which were significantly down-regulated in the white feather follicles of Shitou geese. Among these genes, EDNRB2 and KIT emerged as the most promising candidate genes for white plumage coloration in Shitou geese. Additionally, our analysis also uncovered 46 differentially expressed miRNAs. Of these, miR-144-y may play crucial roles in the regulation of feather pigmentation. Furthermore, the expression of novel-m0086-5p, miR-489-y, miR-223-x, miR-7565-z, and miR-3535-z exhibits a significant negative correlation with the expression of pigmentation genes including TYRP1, EDNRB2, MLANA, SOX10, TRPM1, and KIT, suggesting these miRNAs may indirectly regulate the expression of these genes, thereby influencing feather color. Our findings provide valuable insights into the genetic mechanisms underlying white plumage coloration in Shitou geese and contribute to the broader understanding of avian genetics and coloration research. [ABSTRACT FROM AUTHOR]

Tracking long-term tidal flat dynamics is crucial for coastal restoration decision making. Accurately capturing the loss and gain of tidal flats due to human-induced disturbances is challenging in the micro-tidal areas. In this study, we developed an automated method for mapping the annual tidal flat changes in the micro-tidal areas under intense human activities, by integrating spectral harmonization, time series segmentation from dual spectral indices, and the tide-independent hierarchical classification strategy. Our method has two key novelties. First, we adopt flexible temporal segments for each pixel based on the dual full-time series spectral indices, instead of solely using a fixed period window, to help obtain more reliable inundation frequency features. Second, a tide-independent hierarchical classification strategy based on the inundation features and the Otsu algorithm capture the tidal flat changes well. Our method performed well in Guangdong, Hong Kong, and Macao (GHKM), a typical area with micro-tidal range and intense human activities, with overall accuracies of 89% and 92% for conversion types and turning years, respectively. The tidal flats in GHKM decreased by 24% from 1986 to 2021, resulting from the loss of 504.45 km2, partially offset by an accretion of 179.88 km2. Further, 70.9% of the total loss was in the Great Bay Area, concentrated in 1991–1998 and 2001–2016. The historical trajectories of tidal flat loss were driven by various policies implemented by the national, provincial, and local governments. Our method is promising for extension to other micro-tidal areas to provide more scientific support for coastal resource management and restoration. [ABSTRACT FROM AUTHOR]

Organophosphate esters (OPEs) are frequently used as flame retardants and plasticizers in various commercial products. While initially considered as substitutes for brominated flame retardants, they have faced restrictions in some countries due to their toxic effects on organisms. We collected 37 soil and crop samples in 20 cities along the coast of South China, and OPEs were detected in all of them. Meanwhile, we studied the contamination and potential human health risks of OPEs. In soil samples, the combined concentrations of eight OPEs varied between 74.7 and 410 ng/g, averaging at 255 ng/g. Meanwhile, in plant samples, the collective concentrations of eight OPEs ranged from 202 to 751 ng/g, with an average concentration of 381 ng/g. TDCIPP, TCPP, TCEP, and ToCP were the main OPE compounds in both plant and soil samples. Within the study area, the contaminants showed different spatial distributions. Notably, higher OPEs were found in coastal agricultural soils in Guangdong Province and crops in the Guangxi Zhuang Autonomous Region. The results of an ecological risk assessment show that the farmland soil along the southern coast of China is at high or medium ecological risk. The average non-carcinogenic risk and the carcinogenic risk of OPEs in soil through ingestion and dermal exposure routes are within acceptable levels. Meanwhile, this study found that the dietary intake of OPEs through food is relatively low, but twice as high as other studies, requiring serious attention. The research findings suggest that the human risk assessment indicates potential adverse effects on human health due to OPEs in the soil–plant system along the coast of South China. This study provides a crucial foundation for managing safety risks in agricultural operations involving OPEs. [ABSTRACT FROM AUTHOR]

Timely and accurate acquisition of spatial distribution and changes in cropland is of significant importance for food security and ecological preservation. Most studies that monitor long-term changes in cropland tend to overlook the rationality in the process of cropland evolution, and there are conflicts between the interannual cropland data, so they cannot be used to analyze land use change. This study focuses on the rationality of annual identification results for cropland, considering the long-term evolution and short-term variations influenced by natural environmental changes and human activities. An approach for annual monitoring of cropland based on long time series and deep learning is also proposed. We acquired imagery related to cropland's vegetation lush period (VLP) and vegetation differential period (VDP) from Landsat images on the Google Earth Engine (GEE) platform and used the ResUNet-a structural model for training. Finally, a long-time-series cropland correction algorithm based on LandTrendr is introduced, and interannual cropland maps of Guangdong Province from 1991 to 2020 were generated. Evaluating the cropland monitoring results in Guangdong Province every five years, we found an overall accuracy of 0.91–0.93 and a kappa coefficient of 0.80–0.83. Our results demonstrate good consistency with agricultural statistical data. Over the past 30 years, the total cropland area in Guangdong Province has undergone three phases: a decrease, significant decrease, and stabilization. Significant regional variations have also been observed. Our approach can be applied to long-time-series interannual cropland monitoring in the southern regions of China, providing valuable data support for the further implementation of cropland protection. [ABSTRACT FROM AUTHOR]

The rent-free farmland transfer that exists widely in China's rural areas is a topic worthy of attention. Particularly, the regional heterogeneity of its occurrences implies regional cultural heterogeneities. Using local dialects to proxy regional cultural features, this study applies econometric methods to examine the impacts of dialect diversity on rent-free farmland transfers. It also considers possible mechanisms through a mediation analysis, based on a combined two-year rural household survey dataset from the Guangdong and Jiangxi Provinces in 2015 and 2016. Robust estimation results reveal that dialect diversity increases the probability of rent-free farmland transfers at the household and village levels. According to the mediation analysis, dialect diversity influences villages' farmland abandonment, rural farmland market development, and the flexibility of farmland transfer contracts, which further affects rent-free farmland transfer. Rent-free farmland transfer depends on social trust and contracts' self-fulfilling advantages. Therefore, cultural and traditional factors should be taken into consideration, which would form beneficial interactions between the selections of rural farmland institutional arrangement and land rights policy implementations. [ABSTRACT FROM AUTHOR]

Aflatoxins are liver carcinogens and are common contaminants in unpackaged peanut (UPP) oil. However, the health risks associated with consuming aflatoxins in UPP oil remain unclear. In this study, aflatoxin contamination in 143 UPP oil samples from Guangdong Province were assessed via liquid chromatography–tandem mass spectrometry (LC-MS). We also recruited 168 human subjects, who consumed this oil, to measure their liver functions and lipid metabolism status. Aflatoxin B1 (AFB1) was detected in 79.72% of the UPP oil samples, with levels ranging from 0.02 to 174.13 μg/kg. The average daily human intake of AFB1 from UPP oil was 3.14 ng/kg·bw/day; therefore, the incidence of liver cancer, caused by intake of 1 ng/kg·bw/day AFB1, was estimated to be 5.32 cases out of every 100,000 persons per year. Meanwhile, Hepatitis B virus (HBV) infection and AFB1 exposure exerted a synergistic effect to cause liver dysfunction. In addition, the triglycerides (TG) abnormal rate was statistically significant when using AFB1 to estimate daily intake (EDI) quartile spacing grouping (p = 0.011). In conclusion, high aflatoxin exposure may exacerbate the harmful effects of HBV infection on liver function. Contamination of UPP oil with aflatoxins in Guangdong urgently requires more attention, and public health management of the consumer population is urgently required. [ABSTRACT FROM AUTHOR]

Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, most of the current research on storm surge water level prediction based on machine learning methods is primarily focused on point predictions. In this study, we explore the feasibility of spatial water level prediction using the ConvLSTM model. We focus on the coastal area of Guangdong Province and employ MIKE21(2019) software to simulate historical typhoons that have made landfall in the region from 1991 to 2018. We construct two datasets: one for direct water level prediction and the other for indirect water level prediction based on water level changes. Utilizing the ConvLSTM network, we employ it to forecast storm surges on both datasets, effectively capturing both temporal and spatial characteristics and thus ensuring the production of dependable results. When directly predicting water levels, we achieve an MAE (mean absolute error) of 0.026 m and an MSE (mean squared error) of 0.0038 m2. In contrast, the indirect prediction approach yields even more promising results, with an MAE of 0.014 m and an MSE of 0.0007 m2. Compared to traditional numerical simulation methods, the ConvLSTM-based approach is simpler, faster, and able to predict water levels accurately without boundary conditions or topographies. Furthermore, we consider worst-case scenarios by predicting the maximum water increase value using the random forest model. Our results indicate that the random forest model can serve as a valuable reference for forecasting the maximum water increase value of typhoon storm surges, supporting effective emergency responses to disasters. [ABSTRACT FROM AUTHOR]

The variation in the urban spatial structure (USS) has profound impacts on carbon emissions. Studying the relationship between the two can provide guidance for carbon neutrality strategies and the construction of low-carbon cities in China. However, there is currently a lack of comparative research on the different regions within a province. In this paper, the spatiotemporal evolution of the USS and carbon emissions, at five-year intervals from 2000 to 2020, is investigated in 21 prefecture-level cities in Guangdong Province, China, and the overall relationship of the USS to carbon emissions and their spatiotemporal variations are analyzed by using a two-way fixed-effects model and a geographically and temporally weighted regression model, respectively. The results show that, first, over the past twenty years, the scale of cities has continued to expand, with increasing continuity and aggregation in the built-up areas, while the complexity and fragmentation of their shapes have gradually decreased. Second, the gap in carbon emissions between the Pearl River Delta and other regions in Guangdong shows a trend of first decreasing and then increasing, with high values concentrated in the Pearl River Delta region and the city of Shantou in the east. Third, compared to socio-economic factors, the USS has a more direct and pronounced impact on carbon emissions. Urban expansion and the increased complexity of land patches promote carbon emissions, whereas improving urban spatial continuity and compactness can reduce carbon emissions. Fourth, the dominant spatial structure indicators of carbon emissions differ among the regions of eastern, western, and northern Guangdong and the Pearl River Delta. This study proposes spatial optimization strategies for the low-carbon development of cities in Guangdong Province, providing a new perspective for integrating urban layout and emission reduction policies. [ABSTRACT FROM AUTHOR]

Droughts are natural disasters that have significant implications for agricultural production and human livelihood. Under climate change, the drought process is accelerating, such as the intensification of flash droughts. The efficient and quick monitoring of droughts has increasingly become a crucial measure in responding to extreme drought events. We utilized multi-imagery data from the geostationary meteorological satellite FY-4A within one day; implemented the daily Maximum Value Composite (MVC) method to minimize interference from the clouds, atmosphere, and anomalies; and developed a method for calculating the daily-scale Temperature Vegetation Drought Index (TVDI), which is a dryness index. Three representative drought events (Yunnan Province, Guangdong Province, and the Huanghuai region) from 2021 to 2022 were selected for validation, respectively. We evaluated the spatial and temporal effects of the TVDI with the Soil Relative Humidity Index (SRHI) and the Meteorological Drought Composite Index (MCI). The results show that the TVDI has stronger negative correlations with the MCI and SRHI in moderate and severe drought events. Meanwhile, the TVDI and SRHI exhibited similar trends. The trends of drought areas identified by the TVDI, SRHI, and MCI were consistent, while the drought area identified by the TVDI was slightly higher than the SRHI. Yunnan Province has the most concentrated distribution, which is mostly between 16.93 and 25.22%. The spatial distribution of the TVDI by FY-4A and MODIS is generally consistent, and the differences in severe drought areas may be attributed to disparities in the NDVI. Furthermore, the TVDI based on FY-4A provides a higher number of valid pixels (437 more pixels in the Huanghuai region) than that based on MODIS, yielding better overall drought detection. The spatial distribution of the TVDI between FY-4A and Landsat-8 is also consistent. FY-4A has the advantage of acquiring a complete image on a daily basis, and lower computational cost in regional drought monitoring. The results indicate the effectiveness of the FY-4A TVDI in achieving daily-scale drought monitoring, with a larger number of valid pixels and better spatial consistency with station indices. This study provides a new solution for drought monitoring using a geostationary meteorological satellite from different spatial–temporal perspectives to facilitate comprehensive drought monitoring. [ABSTRACT FROM AUTHOR]

In 2019–2020, an array weather radar (AWR) network consisting of seven X-band phased-array radars (PARs), with a detection spatial resolution of 30 m and a temporal resolution of 30 s, was built in the city of Foshan in China's Guangdong Province. The detection time deviation in the same space is within 5 s. Through variational data assimilation, the three-dimensional wind field inside the storm can be obtained. This study selected instances of hail, thunderstorms, strong winds, and short-duration heavy precipitation in 2020 to conduct a detailed analysis. The results show the following: (1) The fine detection ability enables phased-array radars to detect the complete evolution process of convective storms, including development, strengthening, and weakening, providing a useful reference for judging the future variation trends of convective storms. (2) Through evolutionary analysis of the three-dimensional wind field, the dynamic mechanisms of storm strengthening and weakening could be obtained, which could serve as a reference to predict the development of storms. The gust wind index and convection index calculated based on the three-dimensional wind field could provide advanced warning for nowcasting. When the gust wind index was greater than 263, the probability of gale-force wind (above 17.0 m/s) was determined to be high. Moreover, the warning could be provided 10–20 min in advance. A convection index greater than 35 and the presence of concentrated contour lines were found to be conducive to the strengthening and formation of a convection, and the warning could be provided 20 min in advance. These results show that the application of PAR can provide important technical support for nowcasting severe convective weather. [ABSTRACT FROM AUTHOR]

Residual current analysis of multiple stations' periodic observational data for sea currents, and multiple voyages, multiple seasons from 2018 to 2022, revealed the existence of a strong southwest current zone, 20–30 m underwater and within the coastal current area of Yangjiang, western Guangdong, in the northwest region of the South China Sea. The velocity of the residual currents in the strong–current zone was 38.2–100.3 cm/s. Observational data for wind, sea currents, salinity and tide from multiple coastal stations in the spring (from March 2019 to May 2019) and summer (from June 2019 to August 2019) of 2019 demonstrated the existence of abrupt strong currents in the coastal current sea area of Yangjiang, western Guangdong. Analyses of continuous sequence hydro meteorological data for the Yangjiang area indicated that wind stress was the main factor determining the direction of sea currents; increases in the near-shore water level produce a westward geostrophic current and a wind current in the shallow sea area; their joint effect was the key factor determining the velocity of the coastal current in this sea area. In spring and summer, when Pearl River runoff into the sea reaches a peak, and under the action of the northeast wind, the water level on the west coast of Guangdong rose. This created a barotropic pressure–gradient force from the shore to the outer sea, generating a southwestward geostrophic current in the same direction as the wind-driven current. The joint action of the wind-driven and geostrophic currents then generated a sudden southwestward coastal current. [ABSTRACT FROM AUTHOR]

The spatiotemporal distributions and physical–optical properties of aerosols are of great scientific significance for the study of climate change and atmospheric environment. What are the characteristics of aerosols in constant high humidity? Continuous wet weather (CWW) is a special weather phenomenon that occurs frequently during the late winter and early spring in South China. In this study, the CALIPSO satellite data and the ERA5 and MERRA-2 reanalysis data are used to analyze the aerosol optical properties of a total of 68 CWW processes from 2012 to 2021 in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). We attempt to explore the variations in meteorological conditions and physical–optical properties of aerosols during the before-stage, wet-stage, and after-stage under different humidity levels. The results show that the prevailing wind direction is northeasterly and that the temperature and humidity are lower under the influence of cold high pressure in the before-stage. Moreover, the high aerosol optical depth (AOD) mainly results from regional transport. During the wet-stage, clean ocean airflow causes AOD to remain at a low level, whereas temperature and humidity increase significantly. The wet-stage ends with coldness when it is controlled by cold high pressure again. The atmospheric circulation in the after-stage is similar to that in the before-stage. However, a remarkable feature is that there is a temperature and humidity inversion layer, which results in a significant increase in AOD. This study reveals the physical–optical properties of aerosols during the three stages and the influence mechanism of meteorological factors on aerosols, which can provide a scientific basis for the study of CWW in the future. [ABSTRACT FROM AUTHOR]

In order to enable urban economic development, the use of the right value and asset value of rural collective construction land (RCCL) is increasingly becoming apparent and this market is experiencing rapid development. However, the arrangement of the governance structure of rural shareholding cooperatives (RSCs) can seriously affect the efficiency of collective construction land market transactions, since the governance of RSCs is related to the interests of farmers. Protecting the rights and interests of farmers while improving the governance efficiency of RSCs is a considerable challenge worldwide. To better deal with this challenge, this study used a field survey in Nanhai District, Guangdong Province, China, to estimate how the governance structure of RSCs affect the efficiency of RCCL market transactions. Tobit models were constructed, and the results show that (1) most of the governance functions of RSCs were not separate from the administrative management of the village committees, which leads to low efficiency of RSCs' governance; (2) leaders of rural collective economic organizations played a key role in governance efficiency; (3) from the perspective of collective land property rights, most village shareholders did not have decision-making power or supervisory authority in the RCCL transfers because they could not complete access to transaction information. Furthermore, most villagers felt that the amount of income distributed was unreasonable, and the rights and interests of farmers and village shareholders were not guaranteed by the RSCs. Therefore, we suggest that the Chinese authorities should strengthen their current efforts to construct a more open and fair governance structure of the RSCs and thus improve their market transaction efficiency. Our work provides some insights into ways to improve the governance structure and market transaction efficiency of RSCs, which can further contribute to the development of the RCCL market in other areas of China and worldwide. [ABSTRACT FROM AUTHOR]

Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 104 ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance. [ABSTRACT FROM AUTHOR]

Retinopathy of prematurity is an ophthalmic disease with a very high blindness rate. With its increasing incidence year by year, its timely diagnosis and treatment are of great significance. Due to the lack of timely and effective fundus screening for premature infants in remote areas, leading to an aggravation of the disease and even blindness, in this paper, a deep learning-based collaborative edge-cloud telemedicine system is proposed to mitigate this issue. In the proposed system, deep learning algorithms are mainly used for classification of processed images. Our algorithm is based on ResNet101 and uses undersampling and resampling to improve the data imbalance problem in the field of medical image processing. Artificial intelligence algorithms are combined with a collaborative edge–cloud architecture to implement a comprehensive telemedicine system to realize timely screening and diagnosis of retinopathy of prematurity in remote areas with shortages or a complete lack of expert medical staff. Finally, the algorithm is successfully embedded in a mobile terminal device and deployed through the support of a core hospital of Guangdong Province. The results show that we achieved 75% ACC and 60% AUC. This research is of great significance for the development of telemedicine systems and aims to mitigate the lack of medical resources and their uneven distribution in rural areas. [ABSTRACT FROM AUTHOR]

In the background of constructing a wold-class Guangdong-Hong Kong-Macao Great Bay Area (GBA), the growing demand for industrial, transportation and energy development in the Pearl River Delta (PRD) will put considerable pressure on improvement of regional air quality. It is important to choose a scientific development path to achieve both economic goal and air quality improvement target. This study uses scenario analysis method to construct three "industry-transport-energy" development scenarios within the region while the improvement level of air quality is simulated and analyzed. The results show that: (1) Considering the mutual constraints and influence relations between industry, transportation and energy in scenario analysis, the "industry-transport-energy" development scenario can be established to meets the same economic goal but has different development paths. (2) Along the historical track and established policy path, concentration of fine particulate matter (PM2.5) in the PRD can be reduced to 16.2 µg/m3 by 2035 as regional gross domestic product (GDP) reaching about 23.5 trillion. (3) Under the same economic goals, raising the proportion of emerging industries, freight by rail, public transport travel and non-fossil power to 95%, 10%, 73%, and 46% respectively leads to 29.6~49.2% reductions in the emissions of sulphur dioxide (SO2), nitrous oxides (NOx), primary PM2.5 and volatile organic compounds (VOCs) compared with those in 2017 that the regional PM2.5 concentration will further drop to 14.1 µg/m3. The results show that, under the constraints of economic development objectives, deepening structural adjustment can improve air quality, which gives advice for the PRD to choose its development path. Furthermore, this study can provide reference for the PRD to promote the transformation of industrial, transportation and energy development modes and structural adjustment under the dual objective of promoting the world-class bay area economic level and high-quality air level. [ABSTRACT FROM AUTHOR]

Climate extremes, particularly drought, often affect the ecosystem. Guangdong Province is one of the most vulnerable areas in China. Using the normalized difference vegetation index (NDVI) to capture vegetation dynamics, this study investigated vegetation responses to drought, temperature, and precipitation extremes on a monthly scale in the vegetation area of Guangdong without vegetation type changes from 1982 to 2015. As extreme temperatures rose, a drought trend occurred in most months, with a higher rate in February and April. The vegetation evenly showed a significant greening trend in all months except June and October. The vegetation activity was significantly positively correlated with the increased extreme temperatures in most months. However, it exerted a negative correlation with drought in February, April, May, June, and September, as well as precipitation extremes in February, April, and June. The response of vegetation to drought was the most sensitive in June. The vegetation tended to be more sensitive to short-term droughts (1–2 months) and had no time lag in response to drought. The results are helpful to provide references for ecological management and ecosystem protection. [ABSTRACT FROM AUTHOR]

Diaporthe species are endophytes, pathogens, and saprobes with a wide host range worldwide. However, little is known about endophytic Diaporthe species associated with Morinda officinalis. In the present study, 48 endophytic Diaporthe isolates were obtained from cultivated M. officinalis in Deqing, Guangdong Province, China. The nuclear ribosomal internal transcribed spacer (ITS), partial sequences of translation elongation factor 1-α (tef1-α), partial calmodulin (cal), histone H3 (his), and Beta-tubulin (β-tubulin) gene regions were sequenced and employed to construct phylogenetic trees. Based on morphology and combined multigene phylogeny, 12 Diaporthe species were identified, including five new species of Diaporthe longiconidialis, D. megabiguttulata, D. morindendophytica, D. morindae, and D. zhaoqingensis. This is the first report of Diaporthe chongqingensis, D. guangxiensis, D. heliconiae, D. siamensis, D. unshiuensis, and D. xishuangbanica on M. officinalis. This study provides the first intensive study of endophytic Diaporthe species on M. officinalis in China. These results will improve the current knowledge of Diaporthe species associated with this traditional medicinal plant. Furthermore, results from this study will help to understand the potential pathogens and biocontrol agents from M. officinalis and to develop a disease management platform. [ABSTRACT FROM AUTHOR]

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, its impact on forests and subsequent recovery has not yet been assessed. Here we detected forest damage area and severity from Typhoon Rammasun using Landsat 8 OLI imagery, the Random Forest (RF) machine-learning algorithm, and univariate image differencing (UID) methods, and the controlling factors on damage severity and canopy greenness recovery were further analyzed. The accuracy evaluations against sample plot data indicated that the RF approach can more accurately detect the affected forest area and damage severity than the UID-based methods, with higher overall accuracy (94%), Kappa coefficient (0.92), and regression coefficient (R2 = 0.81; p < 0.01). The affected forest area in Guangdong and Hainan was 13,556 km2 and 3914 km2, accounting for 13.8% and 18.5% total forest area, respectively. The highest affected forest fractions reached 70% in some cities or counties. The proportions of severe damage category accounted for 20.85% and 21.31% of all affected forests in Guangdong and Hainan, respectively. Our study suggests that increasing tree density and choosing less sensitive tree species would reduce damage from typhoons in vulnerable areas such as fringe, scattered, and high-slope forests. The canopy greenness of damaged forests recovered rapidly within three months for both provinces; however, management strategies should still be applied in the severely damaged areas to sustain forest functions since the persistent forest canopy structure and biomass may require a longer time to recover. [ABSTRACT FROM AUTHOR]

Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research. [ABSTRACT FROM AUTHOR]

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPPp), actual NPP (NPPa), and human-induced NPP (NPPh), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPPa variability were quantitatively analyzed and identified. The results showed that the NPPp, NPPa, and NPPh had heterogeneous spatial distributions, and the average NPPp and NPPa values over the whole study area increased at rates of 3.63 and 6.94 gC·m−2·yr−1 from 2000 to 2020, respectively, while the NPPh decreased at a rate of −4.43 gC·m−2·yr−1. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPPa increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPPa values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPPa over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA. [ABSTRACT FROM AUTHOR]

Pogostemon cablin is one of the well-known Southern Chinese medicinal plants with detoxification, anti-bacterial, anti-inflammatory, and other pharmacological functions. Identification and characterization of phytopathogens on P. cablin are of great significance for the prevention and control of diseases. From spring to summer of 2019 and 2020, a leaf spot disease on Pogostemon cablin was observed in Guangdong Province, South China. The pathogen was isolated and identified based on both morphological and DNA molecular approaches. The molecular identification was conducted using multi-gene sequence analysis of large subunit (LSU), the nuclear ribosomal internal transcribed spacer (ITS), beta-tubulin (β-tubulin), and RNA polymerase II (rpb2) genes. The causal organism was identified as Stagonosporopsis pogostemonis, a novel fungal species. Pathogenicity of Stagonosporopsis pogostemonis on P. cablin was fulfilled via confining the Koch's postulates, causing leaf spots and stem blight disease. This is the first report of leaf spot diseases on P. cablin caused by Stagonosporopsis species worldwide. [ABSTRACT FROM AUTHOR]

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover. [ABSTRACT FROM AUTHOR]

With the development of the wind power industry in China, accurate simulation of near-surface wind plays an important role in wind-resource assessment. Numerical weather prediction (NWP) models have been widely used to simulate the near-surface wind speed. By combining the Weather Research and Forecast (WRF) model with the Three-dimensional variation (3DVar) data assimilation system, our work applied satellite data assimilation to the wind resource assessment tasks of coastal wind farms in Guangdong, China. We compared the simulation results with wind speed observation data from seven wind observation towers in the Guangdong coastal area, and the results showed that satellite data assimilation with the WRF model can significantly reduce the root-mean-square error (RMSE) and improve the index of agreement (IA) and correlation coefficient (R). In different months and at different height layers (10, 50, and 70 m), the Root-Mean-Square Error (RMSE) can be reduced by a range of 0–0.8 m/s from 2.5–4 m/s of the original results, the IA can be increased by a range of 0–0.2 from 0.5–0.8 of the original results, and the R can be increased by a range of 0–0.3 from 0.2–0.7 of the original results. The results of the wind speed Weibull distribution show that, after data assimilation was used, the WRF model was able to simulate the distribution of wind speed more accurately. Based on the numerical simulation, our work proposes a combined wind resource evaluation approach of numerical modeling and data assimilation, which will benefit the wind power assessment of wind farms. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic's epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down. [ABSTRACT FROM AUTHOR]