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Spatiotemporal fusion in remote sensing plays an important role in Earth science applications by using information complementarity between different remote sensing data to improve image performance. However, several problems still exist, such as edge contour blurring and uneven pixels between the predicted image and the real ground image, in the extraction of salient features by convolutional neural networks (CNNs). We propose a spatiotemporal fusion method with edge-guided feature attention based on remote sensing, called STF-EGFA. First, an edge extraction module is used to maintain edge details, which effectively solves the boundary blurring problem. Second, a feature fusion attention module is used to make adaptive adjustments to the extracted features. Among them, the spatial attention mechanism is used to solve the problem of weight variation in different channels of the network. Additionally, the problem of uneven pixel distribution is addressed with a pixel attention (PA) mechanism to highlight the salient features. We transmit the different features extracted by the edge module and the encoder to the feature attention (FA) module at the same time after the union. Furthermore, the weights of edges, pixels, channels and other features are adaptively learned. Finally, three remote sensing spatiotemporal fusion datasets, Ar Horqin Banner (AHB), Daxing and Tianjin, are used to verify the method. Experiments proved that the proposed method outperformed three typical comparison methods in terms of the overall visual effect and five objective evaluation indexes: spectral angle mapper (SAM), peak signal-to-noise ratio (PSNR), spatial correlation coefficient (SCC), structural similarity (SSIM) and root mean square error (RMSE). Thus, the proposed spatiotemporal fusion algorithm is feasible for remote sensing analysis.

Background Patients with type 2 diabetes mellitus (T2DM) have been reported to be more susceptible to 2019 novel coronavirus (2019-nCoV) and more likely to develop severe pneumonia. However, the safety and immunological responses of T2DM patients after receiving the inactivated vaccines are not quite definite. Therefore, we aimed to explore the safety, antibody responses, and B-cell immunity of T2DM patients who were vaccinated with inactivated coronavirus disease 2019 (COVID-19) vaccines. Methods Eighty-nine patients with T2DM and 100 healthy controls (HCs) were enrolled, all of whom had received two doses of full-course inactivated vaccines. At 21–105 days after full-course vaccines: first, the safety of the vaccines was assessed by questionnaires; second, the titers of anti-receptor binding domain IgG (anti-RBD-IgG) and neutralizing antibodies (NAbs) were measured; third, we detected the frequency of RBD-specific memory B cells (RBD-specific MBCs) to explore the cellular immunity of T2DM patients. Results The overall incidence of adverse events was similar between T2DM patients and HCs, and no serious adverse events were recorded in either group. Compared with HCs, significantly lower titers of anti-RBD-IgG ( p  = 0.004) and NAbs ( p  = 0.013) were observed in T2DM patients. Moreover, the frequency of RBD-specific MBCs was lower in T2DM patients than in HCs ( p  = 0.027). Among the 89 T2DM patients, individuals with lower body mass index (BMI) had higher antibody titers (anti-RBD-IgG: p  = 0.009; NAbs: p  = 0.084). Furthermore, we found that sex, BMI, and days after vaccination were correlated with antibody titers. Conclusions Inactivated COVID-19 vaccines were safe in patients with T2DM, but the antibody responses and memory B-cell responses were significantly decreased compared to HCs. Trial registration number and date NCT05043246. September 14, 2021. (Clinical Trials.gov)

The first year of boarding senior high school marks a period of great change for students. The extent to which students are able to adjust to successfully navigate this change (adaptability) likely has an impact on their psychological wellbeing. It has also been theorized that students’ personality traits and perceived social support may impact upon their adaptability and, directly and/or indirectly through adaptability, influence their psychological wellbeing. However, the literature examining independent and mediating effects of adaptability on psychological wellbeing is sparse particularly among students from non-Western cultures. In the present study, 102 grade-one high school students in China, were surveyed for their personality, perceived social support, adaptability, and psychological wellbeing (life satisfaction, mental well-being, and psychological distress). Findings showed that adaptability (along with neuroticism, extraversion, and social support) made a significant independent contribution to students’ psychological wellbeing. Further, adaptability was found to fully mediate the relationships between personality (conscientiousness and neuroticism) and psychological wellbeing, and to partially mediate the relationships between extraversion and psychological wellbeing, and social support and psychological wellbeing. These findings have important theoretical and practical implications for researchers and educators who are seeking to support students’ adjustment to boarding senior high school.

This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image.

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer-related mortality with a dismal prognosis that has changed little over the past few decades. Further understanding of the molecular pathology of PDAC progression is urgently required in order to improve the prognosis of patients with PDAC. Herein, it was observed that trefoil factor 3 (TFF3) expression was elevated in PDAC, and was positively correlated with a worse overall patient survival outcome. Forced expression of TFF3 promoted oncogenic functions of PDAC cells in vitro including cell proliferation, survival, foci formation, cancer stem cell-like behavior and invasion, ex vivo colony growth in 3D-Matrigel, and xenograft growth in vivo. Depletion or pharmacological inhibition of TFF3 inhibited these same processes. RNA-Seq analysis and subsequent mechanistic analyses demonstrated that TFF3 increased the expression of various WNT ligands to mediate WNT pathway activation required for TFF3-stimulated PDAC progression. Combined pharmacological inhibition of TFF3 and WNT signaling significantly attenuated PDAC xenograft growth and potentiated the therapeutic efficacy of gemcitabine in both ex vivo and in vivo models. Hence, a mechanistic basis for combined inhibition of pathways enhancing PDAC progression is provided and suggests that inhibition of TFF3 may assist to ameliorate outcomes in PDAC.

Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R2 of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research.

Objective. The objective of the study is to investigate the relationships between fetuin-B, thyroid autoimmunity (TAI), and pregnancy outcomes in women undergoing in vitro fertilization and embryo transfer (IVF-ET). Design, Patients, and Measurements. In this prospective study, 180 women who were preparing for pregnancy with IVF-ET were included. There were 120 women with TAI positive and 60 negative controls matched with age and BMI. Results. The 180 women had mean ± SD age of 31.4 ± 4.0 years, with a mean ± SD BMI of 21.0 ± 1.6 kg/m2. There was a significant difference in the level of fetuin-B in women with TAI positive compared with TAI negative group (65.2 ± 18.5 vs. 76.4 ± 25.1, P=0.001). Fetuin-B had a negative relationship with thyroid antibodies even after adjusting for other variables (OR (95%CI) = 0.98 (0.96–0.99), P=0.002). Compared with women with TAI negative, those with TAI positive had a significantly higher risk of low fertilization (20.0% vs. 6.7%; P=0.035). And we found no difference in terms of pregnancy, abortion, implantation, and live birth rate between the two groups. Logistic regression analysis showed that both fetuin-B and TAI were the independent factors to lead the low fertilization of IVF-ET (OR (95%CI) = 0.96 (0.94–0.99) and 4.084 (1.39–15.30), P=0.004 and 0.019, respectively). Conclusion. Fetuin-B was significantly associated with TAI and low fertilization rate in women undergoing IVF-ET. Decreased fetuin-B in women with TAI may be the underlying reason for the lower IVF-ET success rate.