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A novel hybrid material with excellent microwave absorption property has been designed by decorating reduced graphene oxide with Ba Tb0.2Eu0.2Fe11.6O19/PANI composite, and the effect of graphene content on microwave absorption property has been investigated. The microstructure of the composite is characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, field emission scanning electron microscope, transmission electron microscope and Raman spectroscopy. The mechanism of microwave absorption is discussed minutely. The result shows that the ternary nanocomposites demonstrate unexceptionable microwave absorption property due to its special nanostructures and synergistic effect among BaTb0.2Eu0.2Fe11.6O19, PANI and RGO. The minimum reflection loss can reach − 60.9 dB at 16.4 GHz with a thickness of only 1.95 mm, and the corresponding effective absorption bandwidth (below − 10 dB) is 4.2 GHz. BaTb0.2Eu0.2Fe11.6O19/PANI/RGO composite can be one of the most promising microwave absorption materials.

Aims/hypothesisObesity is a risk factor for type 2 diabetes, yet little is known about how timing and cumulative exposure of obesity are related to disease risk. The aim of this study was to examine the associations between BMI trajectories, age of onset of obesity and obese-years (a product of degree and duration of obesity) over early adulthood and subsequent risk of type 2 diabetes.MethodsWomen aged 18–23 years at baseline (n = 11,192) enrolled in the Australian Longitudinal Study on Women’s Health (ALSWH) in 1996 were followed up about every 3 years via surveys for up to 19 years. Self-reported weights were collected up to seven times. Incident type 2 diabetes was self-reported. A growth mixture model was used to identify distinct BMI trajectories over the early adult life course. Cox proportional hazards regression models were used to examine the associations between trajectories and risk of diabetes.ResultsOne hundred and sixty-two (1.5%) women were newly diagnosed with type 2 diabetes during a mean of 16 years of follow-up. Six distinct BMI trajectories were identified, varying by different initial BMI and different slopes of increase. Initial BMI was positively associated with risk of diabetes. We also observed that age at onset of obesity was negatively associated with risk of diabetes (HR 0.87 [95% CI 0.79, 0.96] per 1 year increment), and number of obese-years was positively associated with diabetes (p for trend <0.0001).Conclusions/interpretationOur data revealed the importance of timing of obesity, and cumulative exposure to obesity in the development of type 2 diabetes in young women, suggesting that preventing or delaying the onset of obesity and reducing cumulative exposure to obesity may substantially lower the risk of developing diabetes.

Strontium ferrite nanoparticles were prepared by a coprecipitation method, and reduced graphene oxide/strontium ferrite/polyaniline (R-GO/SF/PANI) ternary nanocomposites were prepared by in situ polymerization method. The morphology, structure, and magnetic properties of the ternary nanocomposites were investigated by X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), TEM, Raman, and VSM. The microwave-absorbing properties of the composites were measured by a vector network analyzer. The XRD patterns show the single phase of strontium hexaferrite without other intermediate phases. TEM photographs reveal that strontium ferrite nanoparticles are uniformly dispersed on the surfaces of R-GO sheets. The R-GO/SF/PANI nanocomposite exhibited the best absorption property with the optimum matching thickness of 1.5 mm in the frequency of 2–18 GHz. The value of the maximum RL was −45.00 dB at 16.08 GHz with the 5.48-GHz bandwidth. The excellent absorption properties of R-GO/SF/PANI nanocomposites indicated their great potential as microwave-absorbing materials.

Introduction Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a relatively new class of antidiabetic drugs. Emerging findings from laboratory studies indicate that SGLT2 inhibitors can improve liver function and suppress the proliferation of hepatocellular carcinoma (HCC) cells. The aim of this study was to test the hypothesis that initiation of SGLT2 inhibitors improves HCC prognosis in a human population. Methods We used National Surveillance, Epidemiology and End Results (SEER)-Medicare linked data in the United States to evaluate the role of SGLT2 inhibitor initiation on the survival of HCC patients. 3,185 HCC patients newly diagnosed between 2014 and 2017 aged 66 years or older with pre-existing type 2 diabetes were included and followed to the end of 2019. Information on SGLT2 inhibitor initiation was extracted from the Medicare Part D file. Results SGLT2 inhibitor initiation was associated with significantly lower mortality risk after adjusting for potential confounders (HR = 0.68, 95% CI = 0.54-0.86) with stronger association for longer duration of use (HR = 0.60, 95% CI = 0.41-0.88). Further, we found that SGLT2 inhibitor initiation was associated with a lower risk mortality risk ranging from 14% to 60% regardless of patient demographic variables, tumor characteristics, and cancer treatments. Conclusion Our large SEER-Medicare linked data study indicates that SGLT2 inhibitor initiation was associated with improved overall survival of HCC patients with pre-existing type 2 diabetes compared with no SGLT2 inhibitor use. Further studies are needed to confirm our findings and elucidate the possible mechanisms behind the association.

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved water-quality in the middle and lower reaches of the Yangtze River (MLRYR) basin. However, its ecological benefits for key biotic components, particularly AV communities, remain unclear. To address this knowledge gap, this study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km2) within the MLRYR basin from 1989 to 2023. A U-Net deep learning model was employed to extract EA data (2016–2023), and a vegetation and bloom extraction algorithm was applied to map different AV groups (1989–2023). Results indicate that by 2023, 88% (22/25) of the lakes had completed EA removal. Over the 34-year period, floating/emergent aquatic vegetation (FEAV) exhibited fluctuating trends, while submerged aquatic vegetation (SAV) demonstrated a significant decline, particularly during the EA demolition phase (2016–2023), when its area sharply decreased from 804.8 km2 to 247.3 km2—a reduction of 69.3%. Spatial comparative analysis further confirmed that SAV degradation was substantially more severe in EA removal areas than in EA retention areas. This study demonstrates that EA demolition, while beneficial for improving water quality, exerts significant short-term negative impacts on AV. These findings highlight the urgent need for lake governance policies to shift from single-objective management toward integrated strategies that equally prioritize water-quality improvement and ecological restoration. Future efforts should enhance targeted restoration in EA removal areas through active vegetation recovery and habitat reconstruction, thereby preventing catastrophic regime shifts to phytoplankton-dominated turbid-water states in lake ecosystems.

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

Metformin has been suggested to reduce thyroid cancer incidence and to improve thyroid cancer prognosis. We aimed to evaluate the associations between metformin and thyroid cancer incidence and prognosis (metastasis/recurrence/progression-free survival). Cochrane Library, PubMed, ClinicalTrials.gov, and U.S. National Library of Medicine Clinical Trials were searched through the end of December 2021. Data were collected from original observational studies or clinical trials on the incidence or prognosis of thyroid carcinoma outcomes in type 2 diabetes mellitus (T2DM) patients with and without metformin use. Risk of bias in non-randomized studies of interventions (ROBINS-I) tool and Grading of Recommendations, and Assessment, Development and Evaluations (GRADE) approach were used to evaluate the risk of bias and quality of the body of evidence, respectively. In general, 4 studies were related to the thyroid cancer incidence, including 1,705,123 participants metformin users and non-users and yielding a total of 3,238 thyroid cancer events; 3 studies reported the prognosis of thyroid carcinoma based on a total of 4,972 individuals with primary thyroid carcinoma and comorbid type 2 diabetes, and the number of thyroid cancer prognosis cases ranged from 3 to 79. The overall risk of bias of the included studies ranged from moderate to serious. In the random-effects model, the summary relative risk (SRR) for thyroid cancer incidence was 0.743 (95% CI: 0.453–1.220; I 2 = 88.7%, low certainty) comparing metformin users to non-users; and SRR for the prognosis of thyroid cancer was 0.504 (95% CI: 0.178–1.430; I 2 = 57.5%, low certainty). Non-statistically significant negative associations between metformin use and incidence and prognosis of thyroid cancer were found in the current analysis, although the quantity and quality of the evidence were limited. Futher investigation is needed to evaluate the clinical benefits of metformin on thyroid cancer prevention and treatments.

There are no epidemiologic data on the relation of depression before colorectal cancer diagnosis to colorectal cancer mortality among women with colorectal cancer, especially those who are postmenopausal. Our aim was to fill this research gap. We analyzed data from a large prospective cohort in the US, the Women's Health Initiative (WHI). The study included 2,396 women with incident colorectal cancer, assessed for depressive symptoms and antidepressant use before cancer diagnosis at baseline (screening visit in the WHI study) during 1993-1998. Participants were followed up from cancer diagnosis till 2018. We used Cox proportional hazards regression to estimate adjusted hazard ratios (HRs) between depression (depressive symptoms or antidepressant use) at baseline, and all-cause mortality and colorectal cancer-specific mortality. Among women with colorectal cancer, there was no association between baseline depression and all-cause mortality or colorectal cancer-specific mortality after adjusting for age or multiple covariates. Among women with colorectal cancer, there was no statistically significant association between depression before colorectal cancer diagnosis and all-cause mortality or colorectal cancer-specific mortality. Further studies are warranted to assess depressive symptoms and antidepressant use, measured at multiple points from baseline to diagnosis, and their interactions with specific types of colorectal cancer treatment on the risk of death from colorectal cancer.

Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake’s trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development.