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Cupping therapy can relieve muscle fatigue and pain after exercise by increasing blood flow at the treatment site, which may lead to dynamic changes of the local skin temperature. This study aimed to analyze the effect of cupping on local skin temperature under two different negative pressures using infrared thermography (IRT). Cupping therapy was performed on the forearms of 22 healthy subjects using the negative pressures of − 0.03 and − 0.04 MPa. IRT was used to record the dynamic changes in skin temperature before, during, and after cupping. Both cupping pressures induced a non-linear skin temperature response: temperature decreased first and then increased during cupping, while it first increased and then decreased after cupping. A significant difference was noted between the two negative pressure groups in the maximum temperature increment after cupping (P < 0.001). Compared with the basal temperature before cupping, the maximum increase in skin temperature after cupping in the − 0.03 and − 0.04 MPa groups was 0.92 and 1.42 °C, respectively. The findings of this study can lay the foundation evaluating the curative effect of cupping based on IRT and provide an objective reference for selecting the cupping negative pressure. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT), as a noncontact tool for temperature measurement, is widely applied in the study of acupuncture modernization. The aim of this study was to assess the intra- and interrater reliability of infrared image analysis of facial acupoints of subjects with facial paralysis and determine the factors influencing the variability of the measured values. A total of 26 patients with facial paralysis on one side, aged 26 to 53 years, participated voluntarily in the study. Facial infrared thermal images of all participants were analyzed by two trained raters at two different time points at a one-week interval. The intraclass correlation coefficient (ICC) was used to determine the intra- and interrater reliability of IRT measurements. The ICC values varied depending on the analyzed acupoints. The reliability of temperature measurement ranged from moderate to excellent (intrarater, ICC ranged from 0.669 to 0.990; interrater, ICC ranged from 0.661 to 0.987). The reliability of temperature difference measurement ranged from low to excellent (intrarater, ICC ranged from 0.412 to 0.882; interrater, ICC ranged from 0.334 to 0.828). The main influencing factor of reliability is the incomplete consistency in selecting acupoint positions when repeatedly positioning the same acupoint manually. Despite low reliability of temperature difference measurement at some acupoints, some auxiliary measures can be used to reduce the error of manual positioning. Thus, infrared thermal imaging still has the potential to assist in objective and quantitative research on acupuncture. [ABSTRACT FROM AUTHOR]

Facial temperature distribution in healthy people shows contralateral symmetry, which is generally disrupted by facial paralysis. This study aims to develop a quantitative thermal asymmetry analysis method for early diagnosis of facial paralysis in infrared thermal images. First, to improve the reliability of thermal image analysis, the facial regions of interest (ROIs) were segmented using corner and edge detection. A new temperature feature was then defined using the maximum and minimum temperature, and it was combined with the texture feature to represent temperature distribution of facial ROIs. Finally, Minkowski distance was used to measure feature symmetry of bilateral ROIs. The feature symmetry vectors were input into support vector machine to evaluate the degree of facial thermal symmetry. The results showed that there were significant differences in thermal symmetry between patients with facial paralysis and healthy people. The accuracy of the proposed method for early diagnosis of facial paralysis was 0.933, and the area under the ROC curve was 0.947. In conclusion, temperature and texture features can effectively quantify thermal asymmetry caused by facial paralysis, and the application of machine learning in early detection of facial paralysis in thermal images is feasible. [ABSTRACT FROM AUTHOR]

The temperature distribution of normal human skin is symmetrical. Facial paralysis generally changes this thermal symmetry. The aim of this study is to analyze facial thermal asymmetry during the early onset of Bell's palsy, and to assess the feasibility of the diagnosis of early-onset Bell's palsy using infrared thermography (IRT). Fifteen subjects with Bell's palsy and 15 healthy volunteers were considered in this study. The infrared thermal images of the front, left, and right sides of all the subjects were collected and analyzed. Each group of facial thermograms was divided into 16 symmetrical regions of interest (ROIs) with respect to the left and right sides. Three different temperature difference calculation methods were used to express the degree of thermal symmetry between the left- and right-side ROIs, namely, the mean temperature difference (ΔT roi), maximum temperature difference (ΔT max), and minimum temperature difference (ΔT min). Among the facial ROIs, there were significant differences in the thermal symmetries of the frontal region, medial canthus region, and infraorbital region between subjects with and without Bell's palsy (p < 0.05). Based on the results, ΔT roi was more effective than the other two methods for the diagnosis of early-onset Bell's palsy. The area under the ROC curve (AUC) of ΔT roi in the infraorbital region was 0.818; and the sensitivity and specificity were 0.867 and 0.800, respectively. Subjects with early-onset Bell's palsy exhibited thermal asymmetry on the left and right sides of their faces. The diagnosis of early-onset Bell's palsy using IRT is therefore necessary. Nevertheless, more effective thermal symmetry analysis methods will be investigated further in future research. • Facial thermal asymmetry due to Bell's palsy was evaluated by infrared thermography. • Differences in facial thermal distributions of subjects with/without Bell's palsy. • Facial thermal asymmetry is not consistent with the symptoms of Bell's palsy. • Frontal-face thermal imaging is helpful for the early diagnosis of Bell's palsy. [ABSTRACT FROM AUTHOR]

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China's high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption. [ABSTRACT FROM AUTHOR]

Cancer recurrence and metastasis after surgical resection is a vital reason of treatment failure. The modification of immune cells through implanted biomaterials is a promising postoperative immunotherapy. Herein, an injectable hydrogel scaffold loaded with engineered exosome mimetics that in vivo recruits and programs endogenous macrophages into M1 binding with anti‐CD47 antibody (M1‐aCD47 macrophages) for postoperative cancer immunotherapy is developed. Briefly, M1 macrophages‐derived exosome mimetics co‐modified with vesicular stomatitis virus glycoprotein (VSV‐G) and aCD47 (V‐M1EM‐aCD47) are encapsulated in injectable chitosan hydrogel. Such hydrogel recruits inherent macrophages in situ and releases V‐M1EM‐aCD47 that programs M2 to M1‐aCD47 macrophages. M1‐aCD47 macrophages own dual‐functions of tumor‐homing and enhanced phagocytosis. They can actively target to tumor cells for delivery of aCD47 that blocks the "don't eat me" signal, thereby promoting phagocytosis of macrophages to cancer cells. Furthermore, V‐M1EM‐aCD47 hydrogel implanted into resection site of 4T1 breast tumor inhibits tumor recurrence and metastasis by phagocytosis of M1‐aCD47 macrophages and T cell‐mediated immune responses. The findings demonstrate that biomaterials can be designed in vivo to program inherent macrophages, thereby activating the innate and adaptive immune systems for prevention of postoperative tumor recurrence and metastasis. [ABSTRACT FROM AUTHOR]

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast. [ABSTRACT FROM AUTHOR]

Herein, a random-forest-based Kriging downscaling method (RFRKriging) was tested to increase the spatial resolution of Landsat 8 thermal-infrared remote sensing (TIRS) brightness temperature (Tb) over the central areas of Guangzhou, China. The 240-m Landsat 8 Tb was upscaled from the resampled 30-m Landsat 8 Tb by using the spatial averaging method. Tb was downscaled based on its relationship to spectral reflectance in visible/near-infrared and shortwave infrared bands, spectral indices, including normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and modified normalized difference water index (MNDWI), digital elevation model (DEM), longitude, latitude, impervious surface, vegetation, and soil fractions. The linear regression Kriging (LRKriging) and NDVI-based thermal sharpening algorithm (TsHARP) regression Kriging (TsHARPKriging) were also implemented. Relative to the reference data from the resampled Landsat 8 Tb, the RFRKriging produced satisfactory downscaling results with respect to the maximum coefficient of determination (R2), the minimum root-mean-square error (RMSE), and the minimum ratio between RMSE and the correspondent Landsat 8 Tb standard deviation (RSD). RFRKriging captured Tb differences over roads, forests, water bodies, and buildings and enhanced the spatial details of downscaled Tb in all urban areas. Furthermore, integrating the multi-type predictor variables from 10-m Sentinel-2A and 30-m Landsat 8 image sets, RFRKriging was further applied to downscale 30-m Landsat 8 Tb to 10 m. Then, it was upscaled to 30 m and assessed with the original 30-m Landsat 8 Tb. RFRKriging was an effective and practical method for increasing the spatial resolution, with a low RSD of 0.254, a low RMSE of 0.525 K and a high R2 of 0.935. The RSD of less than 0.5 indicates that RFRKriging could achieve an acceptable downscaling accuracy. Furthermore, RFRKriging enhances the spatial details of a 10-m Tb in complex urban environments while maintaining the intrinsic information and spatial patterns of a downscaled Tb. Due to the unavailability of the 30-m and 10-m Tb truth, this study may only provide a quantitative assessment from some theoretical experiments. Also, a comparison of the RFRKriging downscaled Tb with the resampled Landsat 8 Tb was performed. The results suggest that the proposed RFRKriging downscaling method shows better downscaling performance with more spatial details than the cubic convolution method applied by the National Aeronautics and Space Administration (NASA) Centre. [ABSTRACT FROM AUTHOR]

This study evaluated the spatio-temporal change characteristics of urban development at different scales with time-series impervious surface fractions. Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) images were used to extract impervious surface fractions using a modified linear spectral mixture analysis method in Guangzhou from 1988 to 2015. The results indicated that the impervious surface area has substantially increased, from 70.3 km 2 in 1988 to 580.5 km 2 in 2015. In 2015, the impervious surfaces were distributed almost throughout the whole region of the study area, except in the forest region. Next, impervious surface weighted mean centre (ISWMC) and the standard deviational ellipse (SDE) methods were used to systematically analyse the principle orientation, direction, spatio-temporal expansion trends, and the distribution differences of impervious surfaces at the whole and local region scales from 1988 to 2015. The spatio-temporal dynamics of ISWMC exhibited different expansion directions and intensities of impervious surfaces at the whole and local region scales. On a whole region scale, the principle expansion direction of impervious surfaces was northward. However, the expansion trend of impervious surfaces in the different districts was significantly different from other trends at the local region scale. The parameters of SDE were used to investigate the orientation and the clustering or dispersion degree of impervious surface at different scales. The results from SDE analysis indicated that the impervious surfaces exhibited uncertainty in the expansion direction at the whole region scale; in contrast, they had a distinct preferred orientation and expansion direction at the local region scale. The analysis revealed that urban expansion exhibited different change characteristics in various directions at the local region scale. In summary, the results at the local region scale can better reflect the change trajectory of spatio-temporal dynamics of urban development and its fine spatial structure than at the whole region scale. [ABSTRACT FROM AUTHOR]

• WEP-L model is an effective measure to quantitatively evaluate water conservation. • Multi-temporal scales of water conservation were quantitatively evaluated. • Quantity of water conservation were different under extreme precipitation. • Precipitation was the main factor affecting water conservation. Water conservation is one of the most critical ecosystem service functions of river basins and is an important indicator of regional eco-environmental conditions. Beijiang River Basin (BRB) is an important water source and ecological barrier for the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), and its water conservation function is of widespread concern. In this study, based on the hydrological model WEP-L (Water and Energy transfer Processes in Large River basin), and the water balance principle, we analysed the multiple time-scale characteristics of water conservation in the BRB during 1960–2018 and revealed the response of water conservation to climate change based on scenario setting and hydrological simulation. The results showed that annual water conservation exhibited an insignificant reducing trend of −4.69 mm/10a. Water conservation reduced insignificantly in spring, summer, and autumn, while it showed a nonsignificant increasing trend of 4.69 mm/10a in winter. The spatial distribution of water conservation in the BRB was low in the south and high in the north, mainly due to the spatial distribution of land use. Precipitation was the main factor affecting water conservation. The results can provide a reference for water resource planning and management, and provide a theoretical foundation for ecosystem restoration and reconstruction. [ABSTRACT FROM AUTHOR]

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality. [ABSTRACT FROM AUTHOR]

Changes in cropping intensity reflect not only changes in land use but also the transformation of land functions. Although both natural conditions and socioeconomic factors can influence the spatial distribution of the cropping intensity and its changes, socioeconomic developments related to human activities can exert great impacts on short term cropping intensity changes. The driving force of this change has a high level of uncertainty; and few researchers have implemented comprehensive studies on the underlying driving forces and mechanisms of these changes. This study produced cropping intensity maps in China from 2001 to 2016 using remote sensing data and analyzed the impacts of socioeconomic drivers on cropping intensity and its changes in nine major agricultural zones in China. We found that the average annual cropping intensity in all nine agricultural zones increased from 2001 to 2016 under rapid socioeconomic development, and the trends in the seven major agricultural zones were significantly increased (p < 0.05), based on a Mann–Kendall test, except for the Northeast China Plain (NE Plain) and Qinghai Tibet Plateau (QT Plateau). Based on the results from the Geo-Detector, a widely used geospatial analysis tool, the dominant factors that affected cropping intensity distribution were related to the arable land output in the plain regions and topography in the mountainous regions. The factors that affected cropping intensity changes were mainly related to the arable land area and crop yields in northern China, and regional economic developments, such as machinery power input and farmers' income in southern China. These findings provide useful cropping intensity data and profound insights for policymaking on how to use cultivated land resources efficiently and sustainably. [ABSTRACT FROM AUTHOR]