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Abstract Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion in inland lakes and reservoirs is still in the initial and exploratory stage, and the research results are relatively few. Especially for mid-to-high latitude lakes, the research is even more blank. Therefore, this study proposes a machine learning method based on OLCI/Sentinel-3 satellite imagery to retrieve algal biomass abundance. Remote sensing models were developed to estimate the biomass abundance of three major algal groups: Cyanophyta, Chlorophyta, and Bacillariophyta. This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). The results indicated that XGBoost exhibited the highest accuracy (R2 = 0.92, RMSE = 1.78%, MAPE = 9.96%) in estimating Cyanophyta’s biomass abundance. The RF model demonstrated the highest accuracy for estimating Chlorophyta’s biomass abundance (R2 = 0.72, RMSE = 6.57%, MAPE = 50.8%), while the GBDT model exhibited the highest accuracy for estimating Bacillariophyta’s biomass abundance (R2 = 0.9, RMSE = 4.66%, MAPE = 47.87%). The models were subsequently applied to all cloud-free OLCI images from Hulun Lake during the ice-free periods from 2016 to 2023, producing spatiotemporal distribution maps of the different phytoplankton biomass abundance. Cyanophyta dominated the biomass abundance (44.62 ± 3.47%), followed by Bacillariophyta (36.35 ± 2.68%), and Chlorophyta had the lowest proportion (10.42 ± 1.08%). Together, these three algae groups constituted 91.4 ± 1.55% of all phytoplankton in Hulun Lake. Significant annual variations in the biomass abundance of Cyanophyta and Bacillariophyta were observed, whereas those of Chlorophyta remained stable. Additionally, this study examined the effects of climatic factors and water quality parameters on the biomass abundance of algae. The findings suggest that temperature, wind speed, and atmospheric pressure are critical factors influencing the biomass abundance of the different algae groups. This study not only fills the gaps in the related field, but also provides a new method for monitoring algae, as well as a strong support for realizing the goals of sustainable management of water resources and ecological protection.

Abstract Background Rheumatoid arthritis (RA) is a chronic autoimmune disease which afflicts about nearly 1% of global population. RA results in synovitis and cartilage/bone damage, even disability which aggravates the health burden. Many drugs are used to relieve RA, such as glucocorticoids (GCs), non-steroidal anti-inflammatory drugs (NSAIDs), and disease-modifying anti-rheumatic drugs (DMARDs) in the clinical treatment. However, present clinical drugs have various disadvantages such as poor bioavailability and short biological half-life and drug resistance, or adverse effects. A recent study showed autophagy modulation may be a novel strategy in the treatment of RA. 3-Methylademine (3-MA), is the most widely used autophagy inhibitor, which blocks autophagy at the initiation and maturation stages. The aim of this study is to evaluate the effect of 3-MA in collagen-induced-arthritis (CIA) mice and further elucidate how 3-MA attenuated inflammation, and cartilage/bone damage in arthritis. Methods An in-vivo mouse collagen-induced arthritis model was applied to compare differences in ankle destruction among control mice and CIA mice treated with or without 3-MA. Bone and cartilage destruction degree was evaluated by histology and micro-computed tomography (µCT). Further in-vivo assays utilized mouse serum samples to investigate inflammatory levels, oxidative levels, and bone resorption cytokines. At last, an immunofluorescence assay was applied to detect the autophagy level among the three groups. Results The in-vivo mouse collagen-induced arthritis model showed that CIA mice revealed apparent hind paw and ankle swelling which was aggravated gradually along with time, while 3-MA treatment attenuated swelling gradually. µCT and histological results showed typical lesions in CIA group while 3-MA treatment alleviated arthritis-related destruction. Serum assay showed that 3-MA significantly reduced inflammatory cytokines levels, suppressed oxidative levels and bone resorption cytokines. Immunofluorescence assay revealed 3-MA significantly inhibited the abnormal autophagy level in CIA mouse ankle. Conclusions 3-MA protects bone destruction in CIA-induced mice arthritis by anti-inflammatory effect and autophagy inhibition.

Accurate detection of small objects plays an important role in the application of Autonomous aerial vehicles (AAV). However, current works mainly extract comprehensive features from unimodal images, which can obtain very limited distinguishable features for objects, especially those with small sizes. To address this issue, we propose a dynamic cascade cross-modal coassisted network, which integrates multimodal images fusion and fine-grained feature learning to generate powerful object semantic representations. Specifically, we design a multimodal high-order interaction module to achieve collaborative interaction of spatial details and channel dependencies between modalities, thereby enhancing object discrimination. To preserve multimodal fine-grained details, we devise a scale-adaptive dynamic feature prompt module, which dynamically motivates the backbone network to capture feature degradation clues. Meanwhile, to maintain the spatial correlation of multimodal cross-scale features and improve the quality of feature fusion, we derive a global collaborative enhancement module into the feature pyramid network for enhancing the detection accuracy across multiple scales. Extensive experimental results on multimodal datasets have shown that our method achieves favorable performance, surpassing other state-of-the-art methods.

Abstract Background Autophagy is a crucial mechanism for maintaining cellular homeostasis and responding to environmental stress, and it is closely linked to tumor drug resistance. Through multi-omics analysis, this study explores the expression patterns, functions, and potential role of the autophagy-related gene Angiotensinogen (AGT) in colorectal cancer (CRC), particularly in relation to chemotherapy resistance. Methods This study first compared AGT expression between CRC and normal tissues using the GTEx and TCGA databases. Differences in expression were assessed using Wilcoxon Rank Sum Tests, and the prognostic impact of AGT was evaluated through univariate Cox survival analysis and meta-analysis. Functional enrichment was performed using the limma and fgsea packages. Drug sensitivity analysis was conducted based on the CTRP database, while immune infiltration was assessed using the CIBERSORT and ESTIMATE methods. Spatial transcriptomic characteristics were explored through 10x Visium technology and deconvolution analysis to investigate the correlation between AGT expression levels and tumor cell content.scRNA-seq data from CRC tissues were sourced from Tumor Immune Single Cell Hub (TISCH).Functional annotation was performed with Single-sample gene set enrichment analysis (SSGSEA), and pseudotime analysis using Monocle 2 mapped their developmental trajectories. The potential of AGT inhibitors in the treatment of CRC was analyzed using drug-target Mendelian randomization.Finally, Phenome-Wide Association Study (PheWAS) was conducted to evaluate genetic associations and potential side effects of AGT inhibitors. Results AGT expression was significantly higher in CRC tissues compared to normal tissues and was associated with shorter recurrence-free survival (RFS). Autophagy signaling pathways were markedly enriched in the high AGT expression group. AGT expression was positively correlated with resistance to chemotherapeutic agents such as gemcitabine, cisplatin, paclitaxel, and 5-fluorouracil. Spatial transcriptomic analysis revealed that AGT was predominantly expressed in malignant tumor regions. Single-cell analysis identified 21 distinct cell subpopulations across 13 major types. AGT expression was significantly higher in tumor samples, especially in the fibroblast C6 subpopulation. Tumor-related pathways were enriched in C1, C5, C6, and C8 subpopulations. Pseudotime analysis revealed that these subpopulations, particularly C6, were in terminal developmental stages.Drug-target Mendelian randomization analysis indicated a negative causal relationship between AGT inhibitors and the risk of both heart failure(ORdrug = 0.950, 95% CI, 0.912–0.990; P = 0.014) and CRC(ORdrug = 0.874, 95% CI: 0.792–0.964; P = 0.007).PheWAS analysis showed no genetic associations between AGT inhibitors and other traits, indicating its specificity and low risk of side effects. Conclusion Elevated AGT expression in CRC is associated with resistance to chemotherapy, and its inhibition may offer a therapeutic avenue for the treatment of colorectal cancer.

Study region: Two boundary-scale watersheds (Yangjiagou and Dongzhuanggou) and three typical vegetation plots [a single plantation, a mixed plantation, and a natural grassland] in the Loess Plateau. Study focus: The performance of different Generalized Complementary Relationships (four versions of GCR) in simulating actual evapotranspiration (ETa) at boundary (∼1 km2) and plot scales (much less than 1 km2) remains unclear, particularly under heterogeneous vegetation cover. Furthermore, the feasibility of using GCR directly to obtain accurate net ETa (ETan, the sum of soil evaporation and plant transpiration) through parameter calibration is investigated. The influence of timescale and vegetation diversity is also discussed. New hydrological insights for the region: Dynamic Scaling of the GCR could achieve high accuracy at boundary-scale watersheds (NSE=0.78) and different vegetation plots (NSE=0.79), indicating the universality and stability of the GCR, in line with the conclusions of previous large-scale studies. Reducing the timescale will decrease the GCR's simulation performance for both ETa and ETan. Interestingly, as vegetation heterogeneity increased, GCR's performance improved when simulating ETa but decreased when simulating ETan. Furthermore, using GCR to obtain ETan directly may generate bias near extreme points at a weekly timescale but becomes feasible at longer timescales. This study expanded recognition of GCR's performance under varying spatiotemporal scales and provided evidence for the possibility of simulating evapotranspiration components by the method.

Gynecological cancers present significant treatment challenges due to drug resistance and adverse side effects. This review explores advancements in lysosomal escape mechanisms, essential for enhancing nano-therapeutic efficacy. Strategies such as pH-sensitive linkers and membrane fusion are examined, showcasing their potential to improve therapeutic outcomes in ovarian, cervical, and uterine cancers. We delve into novel materials and strategies developed to bypass the lysosomal barrier, including pH-sensitive linkers, fusogenic lipids, and nanoparticles (NPs) engineered for endosomal disruption. Mechanisms such as the proton sponge effect, where NPs induce osmotic swelling and rupture of the lysosomal membrane, and membrane fusion, which facilitates the release of therapeutic agents directly into the cytoplasm, are explored in detail. These innovations not only promise to improve therapeutic outcomes but also minimize side effects, marking a significant step forward in the treatment of ovarian, cervical, and uterine cancers. By providing a comprehensive analysis of current advancements and their implications for clinical applications, this review sheds light on the potential of lysosomal escape strategies to revolutionize gynecological cancer treatment, setting the stage for future research and development in this vital area.

Background Thrombocytopenia is commonly observed in patients with sepsis and is an independent risk factor for poor prognosis. However, the changes of platelet count caused by different pathogens can vary significantly. Our study aims to evaluate the quantitative changes in platelet count in response to various pathogens.Material and Methods We retrospectively analysed data of 3044 patients with sepsis from Medical Information Mart for Intensive Care (MIMIC, 2008–2019) database and prospectively collected data of 364 patients with sepsis from our local cohort of the Shandong Bloodstream Infection and Sepsis Collaboration Study (SBISC, 2020–2022). Propensity score matching (PSM) was employed to control for baseline differences in variables, except for the causative pathogen.Results Multivariate logistic analyses of both original and PSM populations identified Candida, Escherichia, Klebsiella, and Serratia species posing a higher risk for thrombocytopenia compared to others. Restricted cubic spline (RCS) curves showed L- or U-shaped associations between platelet count and 28-mortality with various cut-off values among different pathogens: ranging from 96 × 109/L in Candida species − 190 × 109/L in Klebsiella species.Conclusion Our present findings indicate a pathogen-specific effect on platelet count, highlighting the importance of monitoring thrombocytopenia in patients infected with above microorganisms. Clinicians need to consider pathogen-specific thresholds when intervene on platelet count.

Abstract Preterm delivery (PTD) is associated with severe adverse maternal and neonatal outcomes and higher medical costs. Therefore, PTD warrants more attention. However, predicting PTD remains a challenge for researchers. This study aimed to investigate potential prenatal predictors of PTD. We retrospectively recruited pregnant women who experienced either PTD or term delivery (TD) and underwent laboratory examinations at 32 weeks of gestation. We compared the test results between the two groups and performed logistic regression analysis and receiver operating characteristic (ROC) curve analysis to identify risk factors and predictive factors for PTD. Our investigation revealed that the PTD cohort exhibited statistically significant elevations in lymphocyte count, mean corpuscular hemoglobin concentration, calcium, uric acid, alkaline phosphatase, triglycerides, and total bile acids. Conversely, the PTD group demonstrated statistically significant reductions in mean corpuscular volume, homocysteine, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), neutrophils to (white blood cells–neutrophils) ratio (dNLR), and (neutrophils × monocytes) to lymphocyte ratio (SIRI). The ROC curve analysis revealed that calcium had an area under the curve (AUC) of 0.705, with a cut-off value of 2.215. Logistic regression analysis showed that premature rupture of membranes was an independent risk factor for PTD. Our study demonstrated that serum calcium levels, NLR, dNLR, and other laboratory tests conducted at 32 weeks of gestation can serve as predictors for PTD. Furthermore, we identified premature rupture of membranes as a risk factor for PTD.

Abstract Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5–20.43% accuracy, 3.6–23.56% F1-score, 3.5–25.71% AUROC and 8.1–36.98% specificity, respectively.

Abstract Interregional grain trade affects the environment while balancing supply and demand. However, what relationship exist between national carbon emissions reduction and nitrogen losses reduction in each export region is unclear. Here we used maize as a case study to address this gap by combining life cycle assessment method with linear programming model. We found that interprovincial maize trade can reduce the country’s overall carbon emissions and nitrogen losses. However, the carbon and nitrogen reduction potential varied across exporting provinces. Northeast provinces have great potential to reduce the country’s overall carbon emissions and nitrogen losses via interprovincial maize trade. Northwest provinces increase the overall carbon emissions but reduce the overall nitrogen losses when transferring out maize. Optimizing interprovincial maize trade schemes can further enhance the country’s overall carbon and nitrogen reduction potential. The research results provide a theoretical reference for China to develop carbon neutral and nitrogen reduction trade schemes.

Multi-source social sensing data provide new opportunities to identify urban functions from the perspective of human activity. The information embedded in multi-source data typically needs to be fused to obtain a comprehensive view of urban functions. Although multi-view clustering has been successfully used to fuse multi-source social sensing data, the adaptive determination of fusion weights for high-dimensional and noisy multi-source social sensing data remains challenging. Therefore, this study proposes an adaptive weighted multi-view subspace clustering (AWMSC) method. First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism. Finally, the specific latent representations of the multi-source data are weighted and fused into a shared subspace representation, which is used as the input of the spectral clustering algorithm to obtain clustering results. AWMSC is applied to identify urban functional zones in Beijing using bus transactions, taxi trajectories, and points of interest datasets. The results show that AWMSC outperforms the typical single-view, weighted-average, and representative multi-view methods. AWMSC can obtain a comprehensive understanding of urban functional zones which may help government departments make more accurate strategic decisions.

Recently, the frequency of cyanobacterial blooms in lakes across Northeast China has noticeably increased, because of the combined effect of global climate change and human activities. Phycocyanin (PC), a distinctive pigment found in cyanobacteria, plays a pivotal role in detecting and predicting harmful cyanobacterial blooms. A novel segmented PC concentration remote sensing inversion model was developed using the support vector regression (SVR) algorithm and Sentinel-3 OLCI data to examine the spatiotemporal variations in PC concentrations across 11 representative lakes in Northeast China. The model showed high accuracy (PC ≤ 45 μg/L: R2 = 0.882, MAPE = 34.53 %, MAE = 3.96 μg/L; PC > 45 μg/L: R2 = 0.814, MAPE = 31.80 %, MAE = 70.58 μg/L). Significant variations in PC concentrations were observed across the watersheds in Northeast China. Over seven years, the PC concentrations declined in half of the lakes. Although no significant changes in the annual average PC concentrations were noticed, intra-annual fluctuations were observed. Seasonal patterns in PC concentrations were also observed. The contribution rates of natural and anthropogenic factors were determined using spatiotemporal data on PC concentrations from 2016 to 2022 and redundancy analysis. The primary drivers of PC concentrations varied across different spatial and temporal scales. These findings highlight the monitoring of PC concentrations in the lakes of Northeast China using Sentinel-3 OLCI images. This study can provide valuable insights for water quality management authorities, enabling them to understand the changing characteristics and trends of PC concentrations and to issue early warnings of cyanobacterial blooms.

The objective was to compare the assessment of beef produced in Ireland from a 19-month bull or a 24-month steer dairy beef production system by consumers in Ireland (Cork) and the United Kingdom (Belfast and Reading). Carcass sides were suspended by the Achilles tendon or by the pelvic bone and 21-d aged longissimus muscle assessed using Meat Standards Australia protocols. Carcass weight and classification were similar for bulls and steers. Consumers in Belfast and Cork rated aroma liking, tenderness, juiciness, overall liking and the composite meat quality score (MQ4) similarly, but lower (P < 0.05) than consumers in Reading. Consumers in Belfast and Cork rated flavour liking similarly as did consumers in Cork and Reading, but consumers in Reading rated flavour liking higher (P < 0.05) than consumers in Belfast. Muscle from steers had higher scores for aroma liking, flavour liking, overall liking and MQ4 scores than bulls (P < 0.05). On average, pelvic suspension increased (P < 0.05) the scores for aroma liking and flavour liking compared with conventional suspension but increased (P < 0.05) tenderness, juiciness, overall liking and MQ4 scores only in bulls. Consumers in Reading rated striploin from the traditional Achilles tendon-suspended steers similarly to striploin from pelvic-suspended bulls (MQ4 score of 71.8 and 68.2, respectively). Beef from the latter system could replace the traditional steer beef in this market, thereby benefiting the beef producer and the environment. [ABSTRACT FROM AUTHOR]

Abstract Rechargeable aqueous zinc batteries are promising for large‐scale energy storage due to their low cost and high safety; however, their energy density has reached the ceiling based on conventional cathodes with a single cationic redox reaction mechanism. Herein, a highly reversible cathode of typical layered vanadium oxide is reported, which operates on both the cationic redox couple of V5+/V3+ accompanied by the Zn2+ storage and the anionic O–/O2– redox couple by anion hosting in an aqueous deep eutectic solvent electrolyte. The reversible oxygen redox delivers an additional capacity of ∼100 mAh g–1 at an operating voltage of ∼1.80 V, which increases the energy density of the cathode by ∼36%, endowing the cathode system a record high energy density of ∼506 Wh kg–1. The findings highlight new opportunities for the design of high‐energy zinc batteries with both Zn2+ and anions as charge carriers.

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models, and to analyze its clinical value. Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease, the patients were divided into the severe group (n=88) and the non-severe group (n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features. Results In the training set, the area under curve (AUC) of the improved machine learning model was 0.922.In the testing set, the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein, blood chlorine, blood magnesium and fibrinogen level, which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis. Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.

Suspended particulate matter (SPM) in lakes exerts strong impact on light propagation, aquatic ecosystem productivity, which co-varies with nutrients, heavy metal and micro-pollutant in waters. In lakes, SPM exerts strong absorption and backscattering, ultimately affects water leaving signals that can be detected by satellite sensors. Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy. There are still rooms for model accuracy improvements, and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters. We assembled more than 16,400 in situ measured SPM in lakes from six continents (excluding the Antarctica continent), of which 9640 samples were matched with Landsat overpasses within ±7 days. Seven machine learning algorithms and two simple regression methods (linear and partial least squares models) were used to estimate SPM in lakes and the performance were compared. To overcome the problem of imbalance datasets in regression, a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise (SMOGN) was adopted in this study. Through comparison, we found that gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) models demonstrated good spatiotemporal transferability with SMOGN processed dataset, and has potential to map SPM at different year with good quality of Landsat land surface reflectance images. In all the tested modeling approaches, the GBDT model has accurate calibration (n = 6428, R2 = 0.95, MAPE = 29.8%) from SPM collected in 2235 lakes across the world, and the validation (n = 3214, R2 = 0.84, MAPE = 38.8%) also exhibited stable performance. Further, the good performances were also exhibited by RF model with calibration (R2 = 0.93) and validation (R2 = 0.86, MAPE = 24.2%) datasets. We applied GBDT and RF models to map SPM of typical lakes, and satisfactory result was obtained. In addition, the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, and L8-OLI), thus the model has the potential to map SPM of lakes for monitoring temporal variations, and tracks lake water SPM dynamics in approximately the past four decades (1984–2021) since Landsat-5/TM was launched in 1984.

Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R2 = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R2 = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R2 = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R2 = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals.

Human activity recognition by radar sensors plays an important role in healthcare and smart homes. However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks. Firstly, an MRW image encoder is used to extract salient feature representations from all time-Doppler images through contrastive learning. This can extract the representative vector of each image and also obtain the pre-training parameters of the MRW image encoder. During the pre-training process, large-scale residual networks, medium-scale residual networks, and small-scale residual networks are used to extract global information, texture information, and semantic information, respectively. Moreover, the time–channel weighting mechanism can allocate weights to important time and channel dimensions to achieve more effective extraction of feature information. The model parameters obtained from pre-training are frozen, and the classifier is added to the backend. Finally, the classifier is fine-tuned using a small amount of labeled data. In addition, we constructed a new dataset with eight dangerous activities. The proposed MRW-CN model was trained on this dataset and achieved a classification accuracy of 96.9%. We demonstrated that our method achieves state-of-the-art performance. The ablation analysis also demonstrated the role of multi-scale convolutional kernels and time–channel weighting mechanisms in classification.

BackgroundHigh interleukin-6 levels correlate with diseases like cancer, autoimmune disorders, and infections. IL-6 receptor inhibitors (IL-6Ri), used for rheumatoid arthritis and COVID-19, may have wider uses. We apply drug-target Mendelian Randomization (MR) to study IL-6Ri’s effects.MethodTo simulate the effects of genetically blocking the IL-6R, we selected single nucleotide polymorphisms (SNPs) within or near the IL6R gene that show significant genome-wide associations with C-reactive protein. Using rheumatoid arthritis and COVID-19 as positive controls, our primary research outcomes included the risk of asthma, asthmatic pneumonia, cor pulmonale, non-small cell lung cancer, small cell lung cancer, Parkinson’s disease, Alzheimer’s disease, ulcerative colitis, Crohn’s disease, systemic lupus erythematosus, type 1 diabetes, and type 2 diabetes. The Inverse Variance Weighted (IVW) method served as our principal analytical approach, with the hypotheses of MR being evaluated through sensitivity and colocalization analyses. Additionally, we conducted Bayesian Mendelian Randomization analyses to minimize confounding and reverse causation biases to the greatest extent possible.ResultsIL-6 inhibitors significantly reduced the risk of idiopathic pulmonary fibrosis (OR= 0.278, 95% [CI], 0.138–0.558; P 0.05) and were excluded from further analysis. Similarly, Small cell lung cancer were excluded due to inconsistent results. Notably, the colocalization evidence for asthmatic pneumonia (coloc.abf-PPH4 = 0.811) robustly supports its association with CRP. The colocalization evidence for Parkinson’s disease (coloc.abf-PPH4 = 0.725) moderately supports its association with CRP.ConclusionIL-6Ri may represent a promising therapeutic avenue for idiopathic pulmonary fibrosis, Parkinson’s disease, and Type 2 diabetes.

To alleviate bone loss, most current drugs target osteoclasts. Saikosaponin A (Ssa), a triterpene saponin derived from Bupleurum falcatum (also known as Radix bupleuri), has immunoregulatory, neuromodulatory, antiviral, anticancer, anti-convulsant, anti-inflammatory, and anti-proliferative effects. Recently, modulation of bone homeostasis was shown to involve ferroptosis. Herein, we aimed to determine Ssa’s inhibitory effects on osteoclastogenesis and differentiation, whether ferroptosis is involved, and the underlying mechanisms. Tartrate‐resistant acid phosphatase (TRAP) staining, F‐actin staining, and pit formation assays were conducted to confirm Ssa-mediated inhibition of RANKL-induced osteoclastogenesis in vitro. Ssa could promote osteoclast ferroptosis and increase mitochondrial damage by promoting lipid peroxidation, as measured by iron quantification, FerroOrange staining, Dichloro-dihydro-fluorescein diacetate, MitoSOX, malondialdehyde, glutathione, and boron-dipyrromethene 581/591 C11 assays. Pathway analysis showed that Ssa can promote osteoclasts ferroptosis by inhibiting the Nrf2/SCL7A11/GPX4 axis. Notably, we found that the ferroptosis inhibitor ferrostatin-1 and the Nrf2 activator tert-Butylhydroquinone reversed the inhibitory effects of Ssa on RANKL-induced osteoclastogenesis. In vivo, micro-computed tomography, hematoxylin and eosin staining, TRAP staining, enzyme-linked immunosorbent assays, and immunofluorescence confirmed that in rats with periodontitis induced by lipopolysaccharide, treatment with Ssa reduced alveolar bone resorption dose-dependently. The results suggested Ssa as a promising drug to treat osteolytic diseases.

The KCNQ1 gene encodes a voltage-gated potassium channel, which plays an important role in the repolarization of myocardial action potentials. Mutations in this gene often result in type 1 long QT syndrome (LQT1). Here, we generated a KCNQ1 (c.1032 + 2 T > C) mutant human embryonic stem cell line (WAe009-A-1D) based on the transient expression adenine base editing system that converts base A to G. The WAe009-A-1D cell maintains the morphology, pluripotency, and normal karyotype of the stem cells and is capable of differentiating into all three germ layers in vivo.

BackgroundCuprizone (CPZ)-treated mice show significant demyelination, altered gut microbiome, and depressive-like behaviors. However, the effects of venlafaxine (Ven) on the gut microbiome and depressive-like behavior of CPZ-treated mice are largely unclear.MethodsMale C57BL/6J mice were fed a chow containing 0.2% cuprizone (w/w) for 5 weeks to induce a model of demyelination. Meanwhile, the gut microbiota and depressive-like behaviors were assessed after the mice were fed with Ven (20 mg/kg/day) or equal volumes of distilled water for 2 weeks by oral gavage from the third week onward during CPZ treatment.ResultsCPZ treatment decreased the sucrose preference rate in the sucrose preference test and increased the immobility time in the tail-suspension test, and it also induced an abnormality in β-diversity and changes in microbial composition. Ven alleviated the depressive-like behavior and regulated the composition of the gut microbiota, such as the increase of Lactobacillus and Bifidobacterium in CPZ-treated mice.ConclusionThe anti-depressant effects of Ven might be related to the regulation of gut microbiota in the CPZ-treated mice.

Abstract Here, four MOFs, namely Sc-TBAPy, Al-TBAPy, Y-TBAPy, and Fe-TBAPy (TBAPy: 1,3,6,8-tetrakis(p-benzoic acid)pyrene), were characterized and evaluated for their ability to remediate glyphosate (GP) from water. Among these materials, Sc-TBAPy demonstrates superior performance in both the adsorption and degradation of GP. Upon light irradiation for 5 min, Sc-TBAPy completely degrades 100% of GP in a 1.5 mM aqueous solution. Femtosecond transient absorption spectroscopy reveals that Sc-TBAPy exhibits enhanced charge transfer character compared to the other MOFs, as well as suppressed formation of emissive excimers that could impede photocatalysis. This finding was further supported by hydrogen evolution half-reaction (HER) experiments, which demonstrated Sc-TBAPy’s superior catalytic activity for water splitting. In addition to its faster adsorption and more efficient photodegradation of GP, Sc-TBAPy also followed a selective pathway towards the oxidation of GP, avoiding the formation of toxic aminomethylphosphonic acid observed with the other M3+-TBAPy MOFs. To investigate the selectivity observed with Sc-TBAPy, electron spin resonance, depleted oxygen conditions, and solvent exchange with D2O were employed to elucidate the role of different reactive oxygen species on GP photodegradation. The findings indicate that singlet oxygen (1O2) plays a critical role in the selective photodegradation pathway achieved by Sc-TBAPy.

A supercritical water gasification strategy was employed to investigate the effects of different conditions on hydrogen-rich gas production from oily sludge, based on the Aspen Plus platform. Meanwhile, the entire process was assessed with a life cycle assessment (LCA) method. The results indicated that the gas yield was decreased by increasing the sludge concentration. The yield of hydrogen-rich gas (H2 and CH4) was improved with increasing temperature. Higher temperatures were more favorable to gas production. Excessive addition of oxidants could reduce the CH4 yield, thereby lowering the energy efficiency of the process. The LCA analysis found that, in comparison to the stages of raw material transportation, heat recovery, and wastewater treatment, the effect of more CO2 produced in cooling and separation stage on global warming potential (GWP) was more obvious. The corresponding process can be improved to mitigate the environmental effect of the whole gasification.

Background: The timing and incidence of recurrent bone metastasis (BM) after radical gastrectomy in patients with gastric cancer (GC) as well as the survival of these patients were not fully understood. The aim of this study was to analyze the data of an observational GC cohort and identify patients who underwent curative gastrectomy and had recurrent BM to describe and clarify the pattern and profile of BM evolution after surgery. Methods: Data were retrieved from a hospital-based GC cohort, and patients who underwent upfront radical gastrectomy were selected. The time points of specific organ metastatic events were recorded, and the person-year incidence rate of metastatic events was calculated. The latency period of BM events after gastrectomy was measured and compared with that of the other two most common metastatic events, liver metastasis (LM) and distant lymph node metastasis (LNM), using analysis of variance. Propensity score matching and subgroup analysis were used for sensitivity analysis. Results: A total of 1324 GC cases underwent radical gastrectomy between January 2011 and December 2021. Of these, 67 BM, 218 LM, and 248 LNM occurred before the last follow-up. The incidence of BM events was 1.7/100 person-years, which was approximately 3-fold lower than that of LM and distant LNM events (5.5 and 6.3 per 100 person-years, respectively). BM events had a significantly longer latency (median time, 16.5 months) than LM and LNM events (11.1 and 12.0 months, respectively). Recurrent BM led to a worse prognosis (median survival, 4.5 months) than those of LM and LNM events (median survival, 7.7 and 7.1 months, respectively). However, no difference in overall survival after gastrectomy was observed among the groups. Conclusions: Compared with other common metastatic events, BM in GC after gastrectomy is a late-onset event indicating poor survival. Trial registration: No. ChiCTR1800019978; http://www.chictr.org.cn/.

Remote sensing image change detection (RSCD) based on deep learning technology has made remarkable achievements. Meanwhile, the enhancement of network architectures and advancements in optimization algorithms have pushed RSCD performance to a higher stage. However, the existing RSCD methods mainly focus on extracting differential information between pixel pairs from bitemporal images, while neglecting the importance of using complementary multiscale features to uncover hierarchical semantic change information. To address these issues, we propose a multiscale feature interaction hybrid network (MFIHNet), which aims to enhance feature discriminability by multiscale features interaction and fusion to enhance model RSCD performance. Specifically, we first design a cascaded convolutional neural networks (CNN)-Transformer feature extraction network to capture hierarchical features at different scales. This strategy enables the network to preserve detailed information in shallow layers while grasping more contextual information in high layers. Subsequently, based on the differences between hierarchical features, we design a novel edge enhancement module (EM) to adaptively focus on key areas under the guidance of edge information to make the changed information clearer. Furthermore, to ensure complementary advantages among different feature layers, we devise a novel cross-scale feature interaction module, which introduces a region-specific atrous convolution into the multiscale attention mechanism for improving feature coassistance capacity. In this way, the MFIHNet not only effectively obtains different types of fine-grained information but also reduces the loss incurred during the feature fusion process, thereby improving the performance of remote sensing RSCD tasks. Extensive experimental results on the challenging CDD and GZ-CD datasets, with mean F1 scores reaching 98.1% and 87.4%, respectively, demonstrate that the proposed method achieves competitive performance. Our source codes are available at https://github.com/qliu520/MFIHNet.

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

Abstract To further evaluate the causal relationships between inflammatory cytokines and migraine, we conducted a bidirectional, two-sample Mendelian randomization (MR) analysis using genetic data from publicly available genome-wide association studies (GWAS). We used several MR methods, including random-effect inverse-variance weighting (IVW), weighted median, MR-Egger, to test the causal relationships. Sensitivity analyses were also conducted to evaluate the robustness of the results. The results showed that hepatocyte growth factor (HGF) was positively associated with the risk of migraine (odds ratio [OR], 1.004; 95% confidence interval [CI], 1.001–1.008; P = 0.022). In addition, Interleukin-2 (IL-2) was considered a downstream consequence of migraine (OR, 0.012; 95% CI, 0.000–0.0929; P = 0.046). These findings suggest that HGF may be a factor associated with the etiology of migraine, while IL-2 is more likely to be involved in the downstream development of migraine.

Abstract Background This study aimed to investigate the effect of the pathological staging of acute histological chorioamnionitis (HCA) on laboratory indicators and to conduct further studies to reassess the threshold values used by clinicians to identify acute HCA in febrile parturients undergoing epidural analgesia. Methods A retrospective study of febrile mothers receiving epidural analgesia at Nanjing Maternal and Child Health Care Hospital from January 1, 2018 to December 31, 2018. The participants were grouped by the progression of acute HCA, and the laboratory parameters were compared between groups. The ability of C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and monocyte-leukocyte ratio (M%), alone or in combination, to identify acute HCA in febrile parturients undergoing epidural analgesia was assessed using logistic regression and ROC curves. Results The area under the curve (AUC) of the best logistic regression model predicting HCA climbed to 0.706 (CRP + MLR). Maternal CRP, NLR, and MLR significantly and progressively increased with the progression of acute HCA (p

Objective To explore the role and possible mechanism of lysyl oxidase (LOX) in age-related degeneration of intervertebral disc. Methods Six SD rats, aged of 2, 8 and 20 months respectively, were subjected, and then underwent intervertebral disc MRI and X-ray to evaluate the effect of age on intervertebral disc degeneration. Then, the total proteins of intervertebral disc from different age groups were isolated and detected for the expression of LOX protein by Western blotting. The hypoxia apoptosis model of nucleus pulposus cells was established by CoCl2 treatment. Through LOX protein inhibitor BAPN and plasmid pcDNA3.1 (+)-LOX, the effect of LOX protein on apoptosis of nucleus pulposus cells under different hypoxia conditions was studied. Fifteen 6-week-old SD rats were randomly divided into blank control group, vehicle group and BAPN group, with 5 animals in each group. PBS buffer of 20 μL or BAPN of 100 μg/mL was injected into the rats from the vehicle or BAPN group via caudal vertebrae. X-ray film examination was taken at 2 and 4 weeks after operation to evaluate intervertebral disc degeneration. Results With the increase of age, X-ray films showed that the intervertebral disc height of 20-month-old rats was decreased significantly compared with 2-month-old rats (P