Search

In recent years, remote sensing analysis has gained significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, the existing research has predominantly focused on single-period RGB image analysis, thus overlooking the complexities of remote sensing image capture, especially in highly vegetated land parcels. In this paper, we provide a large-scale vegetation remote sensing (VRS) dataset and introduce the VRS-Seg task for multi-modal and multi-temporal vegetation segmentation. The VRS dataset incorporates diverse modalities and temporal variations, and its annotations are organized using the Vegetation Knowledge Graph (VKG), thereby providing detailed object attribute information. To address the VRS-Seg task, we introduce VRSFormer, a critical pipeline that integrates multi-temporal and multi-modal data fusion, geometric contour refinement, and category-level classification inference. The experimental results demonstrate the effectiveness and generalization capability of our approach. The availability of VRS and the VRS-Seg task paves the way for further research in multi-modal and multi-temporal vegetation segmentation in remote sensing imagery. [ABSTRACT FROM AUTHOR]

There is an urgent need for a mass population screening tool for diabetes. Skin tissue contains a large number of endogenous fluorophores and physiological parameter markers related to diabetes. We built an excitation‐emission spectrum measurement system with the excited light sources of 365, 395, 415, 430, and 455 nm to extract skin characteristics. The modeling experiment was carried out to design and verify the accuracy of the recovery of tissue intrinsic discrete three‐dimensional fluorescence spectrum. Blood oxygen modeling experiment results indicated the accuracy of the physiological parameter extraction algorithm based on the diffuse reflectance spectrum. A community population cohort study was carried out. The tissue‐reduced scattering coefficient and scattering power of the diabetes were significantly higher than normal control groups. The Gaussian multi‐peak fitting was performed on each excitation‐emission spectrum of the subject. A total of 63 fluorescence features containing information such as Gaussian spectral curve intensity, central wavelength position, and variance were obtained from each person. Logistic regression was used to construct the diabetes screening model. The results showed that the area under the receiver operating characteristic curve of the model for predicting diabetes was 0.816, indicating a high diagnostic value. As a rapid and non‐invasive detection method, it is expected to have high clinical value. [ABSTRACT FROM AUTHOR]

Objective To investigate the glucose metabolic status in patients with previous gestational diabetes mellitus (GDM) in the 5-6 years after delivery, and to explore the changes in insulin resistance and islet β cell function from postpartum short-term (6-12 weeks) to the long-term (5-6 years) after delivery, as well as the relationship with glucose metabolic status. Methods A total of 72 patients with a history of GDM who delivered in Peking University First Hospital and had complete short-and long-term postpartum follow-up data were included. They were divided into normal glucose metabolism (NGT) and abnormal glucose metabolism (AGT) groups according to their glucose metabolism status in 5-6 years after delivery. Pre-pregnancy, pregnancy, perinatal, short-and long-term postpartum clinical data were collected and compared between the two groups, including demographic indicators, glucose and lipid metabolism indicators [oral glucose tolerance test (OGTT) fasting blood glucose (FPG), 1-hour blood glucose (1hPG), 2-hour blood glucose (2hPG), 3-hour blood glucose (3hPG), total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol], insulin use ratio, fasting insulin (FINS), OGTT 2-hour insulin (2hINS), insulin sensitivity index (ISI), homeostasis model assessment of insulin resistance index (HOMA-IR) and homeostasis model assessment of β cell function index (HOMA-β) were calculated. The comparison between the two groups was conducted using independent sample t-test, rank sum test χ² test or Fisher's exact probability test. Multivariate logistic regression analysis was used to analyze the influence factors of abnormal glucose metabolism. Results There were 38 (52.78%) cases in NGT group and 34 (47.22%) cases in AGT group. Compared with the NGT group, there are differences in related metabolic indicators in AGT group from pregnancy to long-term postpartum period: During pregnancy, 3hPG in 75 g OGTT and the proportion of insulin users were higher in AGT group. As for 6-12 weeks postpartum, OGTT 2hPG and the incidence of blood glucose abnormalities and the proportion of hypertriglyceridemia were higher (all P<0.05). However, there was no statistically significant difference in HOMA-IR and HOMA-β between the two groups(P>0.05). At 5-6 years postpartum, FPG, OGTT 2hPG, and FINS were all higher, with a higher degree of insulin resistance and lower insulin sensitivity (ISI) in AGT group (all P<0.05). Compared with short-term postpartum period (6-12 weeks), FPG, FINS, HOMA-IR, HOMA-β were all significantly increased regardless of glucose metabolic status at 5-6 years postpartum, while OGTT 2hPG and 2hINS increased further in the AGT group (all P<0.05). OGTT 2hPG and insulin use during pregnancy were risk factors for abnormal glucose metabolism 5-6 years postpartum according to multivariate logistic regression analysis, with OR values (95%CI) of 1.646 (1.015-2.671) and 3.570 (1.009-12.624), respectively. Conclusions The incidence of AGT was still high in patients with previous GDM 5-6 years after delivery. Insulin resistance progressed gradually after delivery regardless of glucose metabolism, especially in patients with abnormal glucose metabolism. OGTT 2hPG and insulin use in pregnancy were risk factors for abnormal glucose metabolism in postpartum 5-6 years, suggesting that there is continuity in time and similarity in pathogenesis between GDM and T2DM. [ABSTRACT FROM AUTHOR]

Bacterial biofilm-associated infectious diseases remain serious menaces to human health. Recently, photodynamic therapy (PDT) has become a prospective strategy for combating biofilm infection. However, anaerobic conditions in a biofilm greatly inhibit its therapeutic efficacy. Here, a nanozyme-reinforced injectable hydrogel is prepared using Ca2+-crosslinked sodium alginate incorporated with photosensitizer-loaded MnO2 nanosheets and CaO2 nanoparticles for O2 self-sufficient PDT to eradicate biofilm infection. In our design, CaO2 reacts with water to produce locally concentrated H2O2, which could be catalyzed by MnO2 nanosheets (catalase-mimic nanozymes) to generate O2 and greatly relieve the hypoxic conditions in the biofilm, thus significantly strengthening PDT efficacy. In vitro assays confirmed that the hybrid hydrogel not only exhibits high-performance bactericidal activity in combating both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli but also shows great efficacy in eliminating biofilm infection. Moreover, benefiting from its good syringeability, the hybrid hydrogel is prone to fit irregular wounds and exhibits high efficiency in promoting wound healing in a biofilm-infected mice model. Besides, no obvious toxicity is detected in the hybrid hydrogel. Overall, we envision that our designed hydrogel could provide a prospective solution for combating biofilm-associated infections. [ABSTRACT FROM AUTHOR]

Aqueous zinc batteries are promising candidates for energy storage and conversion devices in the "post‐lithium" era due to their high energy density, high safety, and low cost. The electrolyte plays an important role in zinc batteries by conducting and separating the positive and negative electrodes. However, the issues of zinc dendrites growth, corrosion, by‐product formation, hydrogen evolution and leakage, and evaporation of the aqueous electrolytes affect the commercialization of the batteries. Moreover, the widely used aqueous electrolytes result in large battery sizes, which are not conducive to the emerging smart devices. The intrinsic properties of gel polymer electrolytes (GPEs) can solve the above problems. In order to promote the wider application of GPEs‐based zinc batteries, in this review, the working principle and the current problems of zinc batteries are first introduced, andthe merits of GPEs compared to aqueous electrolytes are then summarized. Subsequently, a series of challenges and corresponding strategies faced by GPE is discussed, and an outlook for its future development is finally proposed. [ABSTRACT FROM AUTHOR]