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The main component of China’s income gap is the urban–rural income gap, which is largely affected by urbanization. It is worth studying how new-type urbanization affects the income gap between urban and rural areas. Research mostly focuses on the urbanization rate as the core explanatory variable to explain the impact using one or two factors. This paper analyzes the mechanism of the effect using a comprehensive number of factors, with the quality of new-type urbanization development as the core explanatory variable. In terms of theoretical research, we believe that new-type urbanization affects the urban–rural income gap by promoting the transfer of labor, changing industrial structure, and policy tendency. Using both static and dynamic empirical analyses, we test the impact of new-type urbanization on the urban–rural income gap based on China’s provincial data. We find that new-type urbanization is conducive to narrowing the income gap between urban and rural areas. The transfer of labor significantly reduces the urban–rural income gap. However, the upgrading of industrial structure will enlarge the gap. The impact of China’s policy orientation is negligible. Policy should focus on promoting urbanization and improving the marginal rate of return of agriculture, improve the level of human capital, reverse the mismatch between employment structure and industrial structure, increase support for rural areas, and make substantial progress in promoting common prosperity.

Accurate water quality estimation is important for water environment monitoring and water resource management and has emerged as a pivotal aspect of ecological rehabilitation and sustainable development. However, due to the strong spatial heterogeneity of water quality parameters, it is still challenging to obtain highly accurate spatial patterns of them. Taking chemical oxygen demand as an example, this study proposes a novel estimation method for generating highly accurate chemical oxygen demand fields in Poyang Lake. Specifically, based on the different water levels and monitoring sites in Poyang Lake, an optimal virtual sensor network was first established. A Taylor expansion-based method with integration of spatial correlation and spatial heterogeneity was developed by considering environmental factors, the optimal virtual sensor network, and existing monitoring stations. The proposed approach was evaluated and compared with other approaches using a leave-one cross-validation process. Results show that the proposed method exhibits good performance in estimating chemical oxygen demand fields in Poyang Lake, with mean absolute error improved by 8% and 33%, respectively, on average, when compared with classical interpolators and remote sensing methods. In addition, the applications of virtual sensors improve the performance of the proposed method, with mean absolute error and root mean squared error values reduced by 20% to 60% over 12 months. The proposed method provides an effective tool for estimating highly accurate spatial fields of chemical oxygen demand concentrations and could be applied to other water quality parameters.

Obtaining precipitation estimates with high resolution and high accuracy is critically important for regional meteorological, hydrological, and other applications. Although satellite precipitation products can provide precipitation fields at various scales, their applications are limited by the relatively coarse spatial resolution and low accuracy. In this study, we propose a multi-source merging approach for generating accurate and high-resolution precipitation fields on a daily time scale. Specifically, a random effects eigenvector spatial filtering (RESF) method was first applied to downscale satellite precipitation datasets. The RESF method, together with Kriging, was then applied to merge the downscaled satellite precipitation products with station observations. The results were compared against observations and a data fusion dataset, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). It was shown that the estimates of the proposed method significantly outperformed the individual satellite precipitation product, reducing the average value of mean absolute error (MAE) by 52%, root mean square error (RMSE) by 63%, and improving the mean value of Kling–Gupta efficiency (KGE) by 157%, respectively. Daily precipitation estimates exhibited similar spatial patterns to the MSWEP products, and were more accurate in almost all cases, with a 42% reduction in MAE, 46% reduction in RMSE, and 79% improvement in KGE. The proposed approach provides a promising solution to generate accurate daily precipitation fields with high spatial resolution.

The existence of breast cancer stem cells (BCSCs) is a major reason underlying cancer metastasis and recurrence after chemotherapy and radiotherapy. Targeting BCSCs may ameliorate breast cancer relapse and therapy resistance. Here we report that expression of the pseudokinase Tribble 3 (TRIB3) positively associates with breast cancer stemness and progression. Elevated TRIB3 expression supports BCSCs by interacting with AKT to interfere with the FOXO1-AKT interaction and suppress FOXO1 phosphorylation, ubiquitination, and degradation by E3 ligases SKP2 and NEDD4L. The accumulated FOXO1 promotes transcriptional expression of SOX2, a transcriptional factor for cancer stemness, which in turn, activates FOXO1 transcription and forms a positive regulatory loop. Disturbing the TRIB3-AKT interaction suppresses BCSCs by accelerating FOXO1 degradation and reducing SOX2 expression in mouse models of breast cancer. Our study provides insights into breast cancer development and confers a potential therapeutic strategy against TRIB3-overexpressed breast cancer. Cancer stem cells contribute to breast cancer metastasis and recurrence. Here the authors show that TRIB3 enhances breast cancer stemness through interaction with AKT to promote FOXO1 stability, which then increases SOX2 activity.

Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.

In recent years, lanthanide metal-organic frameworks (LnMOFs) have developed to be an interesting subclass of MOFs. The combination of the characteristic luminescent properties of Ln ions with the intriguing topological structures of MOFs opens up promising possibilities for the design of LnMOF-based chemical sensors. In this review, we present the most recent developments of LnMOFs as chemical sensors by briefly introducing the general luminescence features of LnMOFs, followed by a comprehensive investigation of the applications of LnMOF sensors for cations, anions, small molecules, nitroaromatic explosives, gases, vapors, pH, and temperature, as well as biomolecules.

•Differentiation of lower and upper alpha bands in visual working memory.•Incorporation of spatial extent as a key variable.•Consistency between time-frequency and neural decoding analyses.•Innovative experimental design with both control of over set size and spatial extent. Alpha oscillations play a critical role in visual working memory (VWM), but the specific contributions of lower and upper alpha sub-bands remain unclear. To address this, we employed a whole-field change detection paradigm to investigate how alpha power modulation and decoding accuracy differ between these sub-bands in response to varying set sizes and spatial extents of memory arrays. Our results revealed that lower alpha (8–9 Hz) exhibits widespread event-related desynchronization (ERD) during the early maintenance phase, which increases with set size and reflects attentional allocation to individual memory items. In contrast, upper alpha (10–12 Hz) demonstrates posteriorly localized ERD that is strongly associated with the spatial extent of memory arrays. During the late maintenance phase, upper alpha transitions to event-related synchronization (ERS), suggesting a role in suppression of irrelevant sensory inputs and enhancement of alertness. Multivariate decoding analyses showed that all alpha sub-bands accurately decoded both set size and spatial extent across time windows, with lower alpha achieving better decoding performance during the early maintenance phase and upper alpha excelling in later stages. These findings suggest that alpha oscillations encode both the number and spatial distribution of memory items, with lower and upper alpha sub-bands serving complementary roles in encoding and maintaining VWM representations.

Background Equity and efficiency are basic value dimensions to evaluate the effectiveness of China’s medical and health service system (MHS) reform and development. Coordinated development of equity and efficiency is necessary to realize high-quality development of medical and health services. This study aims to evaluate the equity, efficiency, and combined efforts in coordinating the MHS during 1991–2020 reform. Methods Data on China’s MHS were obtained from the China Statistical Yearbook 1992–2021. Ratios of urban to rural residents’ medical expenditure and number of medical professionals per 10,000 people were employed to evaluate MHS’s equity. The data envelopment analysis-Malmquist model was employed to evaluate MHS’s efficiency. We constructed a combined-efforts-in-coordination model to examine the coordination degree between equity and efficiency. Results Equity of medical expenditure burden significantly improved from during 1991–2007. Urban residents’ 1991 medical expenditure burden was 87.8% of that of rural residents, which increased to 100.1% in 2007. Urban areas’ mean medical expenditure burden was 105.94% of that in rural areas during 1991–2007. The gap in equity of medical expenditure burden between urban and rural areas slowly widened after 2007, with urban areas’ mean burden being 68.52% of that in rural areas during 2007–2020. Medical and health resources allocation shows an alarming inequity during this period, with mean number of medical professionals per 10,000 people in urban areas being 238.30% of that in rural areas. Efficiency experienced several fluctuations before 2008. Since 2008, efficiency was high (0.915) and remained stable, except in 2020. The combined-efforts-in-coordination score for medical expenditure burden was less than 0.2 for 80% of the years, while that for in medical and health resources was more than 0.5 for 99.67% of the years. Conclusions MHS inequity remains between urban and rural China, primarily because of disproportionate allocation of medical and health resources. The government should enhance rural medical professionals’ salary and welfare and provide medical subsidies for rural residents to adjust resource allocation levels in urban and rural areas, control differences in medical expenditure burden between urban and rural residents to a reasonable range, and continuously improve urban and rural residents’ equity level.

This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band- (between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches.