Search

Objective: To examine the association between urbanization and psychological well-being (PWB) in China. Methods: Data were obtained from the 2015 China Health and Nutrition Survey (CHNS) and included 3,071 men and 3,385 women aged 50 years or older who lived in 288 communities across 12 provinces and municipalities. The urbanization index, which summarizes 12 urbanization dimensions at community level, was categorized into tertiles representing low, medium, and high levels of urbanization. PWB was assessed by scoring three self-reported questions. Gender-stratified multilevel analyses were used to estimate the association between urbanization and PWB. Results: After controlling for individual-level factors, the urbanization index was associated positively with PWB. More specifically, an increase of one SD in the score of community population density, economic activity, housing, education, sanitation, or diversity was associated significantly with PWB in both genders. In addition, increases in the score of community communications, social services, transportation, or modern markets were associated significantly with PWB in women, but not in men. Conclusion: Urbanization has, on average, had an independent and positive effect on PWB in middle-aged and older Chinese populations, with many components of urbanization being separately and positively associated with PWB, especially in women.

To assessed the effects of solid-state fermentation product of yeast (SFPY) supplementation on high-fat diet (HFD) induced challenges on growth, hepatic lipid metabolism, epidermal mucus, intestine and liver health and gut microbiota in zebrafish (Danio rerio), four experimental diets were prepared for one-month-old zebrafish: basal diet (Control), high-fat diet (HFD), 0.5% SFPY (0.5 SFPY) or 1.0% SFPY (1.0 SFPY)-added HFD. After 3 weeks of feeding, the results illustrated that although HFD increased weight gain (WG), corresponding decreased the feed conversation ratio (FCR), it caused negative influences including liver steatosis, intestinal and liver damage in zebrafish. Compared with HFD group, 1.0% SFPY obviously promoted the lysozyme activity and complement 4 (C4) level on epidermal mucus (P

Background & Aims: Sepsis-induced acute liver dysfunction often occurs early in sepsis and can exacerbate the pathology by triggering multiple organ dysfunction and increasing lethality. Nevertheless, our understanding of the cellular heterogeneity and dynamic regulation of major nonparenchymal cell lineages remains unclear. Methods: Here, single-cell RNA sequencing was used to profile multiple nonparenchymal cell subsets and dissect their crosstalk during sepsis-induced acute liver dysfunction in a clinically relevant polymicrobial sepsis model. The transcriptomes of major liver nonparenchymal cells from control and sepsis mice were analysed. The alterations in the endothelial cell and neutrophil subsets that were closely associated with acute liver dysfunction were validated using multiplex immunofluorescence staining. In addition, the therapeutic efficacy of inhibiting activating transcription factor 4 (ATF4) in sepsis and sepsis-induced acute liver dysfunction was explored. Results: Our results present the dynamic transcriptomic landscape of major nonparenchymal cells at single-cell resolution. We observed significant alterations and heterogeneity in major hepatic nonparenchymal cell subsets during sepsis. Importantly, we identified endothelial cell (CD31+Sele+Glut1+) and neutrophil (Ly6G+Lta4h+Sort1+) subsets that were closely associated with acute liver dysfunction during sepsis progression. Furthermore, we found that ATF4 inhibition alleviated sepsis-induced acute liver dysfunction, prolonging the survival of septic mice. Conclusions: These results elucidate the potential mechanisms and subsequent therapeutic targets for the prevention and treatment of sepsis-induced acute liver dysfunction and other liver-related diseases. Impact and Implications: Sepsis-induced acute liver dysfunction often occurs early in sepsis and can lead to the death of the patient. Nevertheless, the pathogenesis of sepsis-induced acute liver dysfunction is not yet clear. We identified the major cell types associated with acute liver dysfunction and explored their interactions during sepsis. In addition, we also found that ATF-4 inhibition could be invoked as a potential therapeutic for sepsis-induced acute liver dysfunction.

The gill is an important organ in fish that is involved in various physiological functions, including respiration, filter-feeding, ammonia nitrogen excretion, and osmoregulation. The physiological functions of gills are closely related to their structure, external feeding conditions, and environmental factors. Herein, we first briefly summarize the basic structure and functions of the gills of teleosts, then explain the structure and function of the gill barrier (as a microbial barrier, a chemical barrier, a physical barrier, and an immune barrier) in detail. Finally, we introduce the regulation of fish gill barrier health by ubiquitous feeding factors and environmental factors, and provide an outlook on the prospects of gill barrier health. We found that improving health through modulating feed factors is more convenient than improving environmental factors. This review increases our understanding of how nutrient composition and level in fish diet influences gill health and overall growth performance of fish.

Probiotics are reported to improve the nutrition, immunity, and health of fish. Nuclease can hydrolyze nucleic acids of probiotics to produce nucleotides. The present study investigated the effect of stabilized fermentation product of nuclease-treated Lactobacillus rhamnosus GCC-3 (GCC-3 NT) on growth, non-specific immunity, liver health, and gut microbiota of zebrafish (Danio rerio). Compared to the high-fat diet (HFD) group, GCC-3 NT did not affect the growth performance of zebrafish. However, GCC-3 NT treatment can significantly increase the lysozyme activity and the total antioxidant capacity of body surface mucus. In addition, dietary GCC-3 NT significantly reduced the content of hepatic triglycerides (TAG) in zebrafish while significantly increased the expression of acyl-coenzyme A oxidases 3 (ACOX3) and proliferator-activated receptor γ coactivator 1α (PGC1α) compared with the HFD group. The 16S rRNA gene sequencing showed that GCC-3 NT reduced the relative abundance of Actinobacteria while increased Firmicutes at the phylum level. The relative abundance of Rhodococcus was significantly decreased and Lactobacillus and Staphylococcus abundance were significantly increased in the GCC-3 NT group compared to the HFD group. Furthermore, PCoA analysis showed GCC-3 NT diet had a significant effect on the autochthonous microbiota compared to the HFD diet. Together, our results showed that nuclease-treated L. rhamnosus fermentation product can improve the immunity, liver health and gut microbiota of zebrafish, suggesting that it can be potentially used as a functional feed additive for aquaculture.

This study was to investigate the effect of medium-chain fatty acids products Aromabiotic® Shrimp (ABS) on growth, gut and hepatopancreas health, gut microbiota and resistance to bacterial infection and adverse environment of white shrimp. Firstly, white shrimp were divided into five groups: control, ABS0.5, ABS1, ABS2, and ABS5 (with 0, 0.5, 1, 2 and 5 g/kg ABS in feed respectively). After 5-week feeding, there was no significant difference in weight gain, feed conversion ratio and survival among groups. However, ABS diets significantly improved the survival of the white shrimp after Vibrio parahaemolyticus challenge (ABS5, P

Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference Vegetation Index (NDVI)-Based Sugarcane Index (NBSI). NBSI analyzed the temporal variation of sugarcane’s NDVI over a year. Leveraging the distinct growth phases of sugarcane (transplantation, tillering, rapid growth and maturity) four measurement methodologies, f(W1), f(W2), f(V) and f(D), were developed to characterize the features of the sugarcane growth period. Utilizing imagery from Landsat-8, Sentinel-2, and MODIS, this study employed the enhanced gap-filling (EGF) method to reconstruct NDVI time-series data for seven counties in Chongzuo, Guangxi Zhuang Autonomous Region, during 2021, subsequently testing NBSI’s ability to extract sugarcane. The results demonstrate the efficiency of NBSI with simple threshold settings: it was able to map sugarcane cultivation areas, exhibiting higher accuracy when compared to traditional classifiers like support vector machines (SVM) and random forests (RF), with an overall accuracy (OA) of 95.24% and a Kappa coefficient of 0.93, significantly surpassing RF (OA = 85.31%, Kappa = 0.84) and SVM (OA = 85.87%, Kappa = 0.86). This confirms the outstanding generalizability and robustness of the proposed method in Chongzuo. Therefore, the NBSI methodology, recognized for its flexibility and practicality, shows potential in enabling the extensive mapping of sugarcane cultivation. This heralds a new paradigm of thought in this field.

Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision (mAP@0.5) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming.

A pyridine-tricarboxylic acid, 5-(3′,5′-dicarboxylphenyl)nicotinic acid (H3dpna), was employed as a adjustable block to assemble a series of coordination polymers under hydrothermal conditions. The seven new coordination polymers were formulated as [Co(μ3-Hdpna)(μ-dpey)]n·nH2O (1), [Zn4.5(μ6-dpna)3(phen)3]n (2), [Co1.5(μ6-dpna)(2,2′-bipy)]n (3), [Zn1.5(μ6-dpna)(2,2′-bipy)]n (4), [Co3(μ3-dpna)2(4,4′-bipy)2(H2O)8]n·2nH2O (5),[Co(bpb)2(H2O)4]n[Co2(μ3-dpna)2(H2O)4]n·3nH2O (6), and [Mn1.5(μ6-dpna)(μ-dpea)]n (7), wherein 1,2-di(4-pyridyl)ethylene (dpey), 1,10-phenanthroline (phen), 2,2′-bipyridine(2,2′-bipy),4,4′-bipyridine(4,4′-bipy),1,4-bis(pyrid-4-yl)benzene (bpb), and 1,2-di(4-pyridyl)ethane (dpea) were employed as auxiliary ligands. The structural variation of polymers 1–7 spans the range from a 2D sheet (1–4, 6, and 7) to a 3D metal–organic framework (MOF, 5). Polymers 1–7 were investigated as heterogeneous catalysts in the Knoevenagel condensation reaction, leading to high condensation product yields (up to 100%) under optimized conditions. Various reaction conditions, substrate scope, and catalyst recycling were also researched. This work broadens the application of H3dpna as a versatile tricarboxylate block for the fabrication of functional coordination polymers.

A 1550 nm all-fiber dual-polarization coherent Doppler lidar (DPCDL) was constructed to measure the depolarization ratio of atmospheric aerosols. In lidar systems, the polarization state of the laser source is typically required to be that of linearly parallel polarization. However, due to the influence of the fiber-optical transmission and the large-mode field output of the telescope, the laser polarization state changes. Hence, a polarizer was mounted to the emitting channel of the telescope to eliminate the depolarization effect. A fiber-optical polarization beam splitter divided the backscattered light into components with parallel and perpendicular polarization. The DPCDL used two coherent channels to receive each of these two polarization components. A calibration procedure was designed for the depolarization ratio to determine the differences in gain and non-responsiveness in the two polarization channels. The calibration factor was found to be 1.13. Additionally, the systematic error and the measured random error of the DPCDL were estimated to evaluate the performance of the system. The DPCDL’s systematic error was found to be about 0.0024, and the standard deviation was lower than 0.0048. The Allan deviations of a 1-min averaging window with a low SNR of 19 dB and a high SNR of 27 dB were 0.0104 and 0.0031, respectively. The random errors at different measured heights were mainly distributed below 0.015. To confirm the authenticity of the atmospheric depolarization ratio measured with the DPCDL, two field observations were conducted with the use of a co-located DPCDL and micro-pulse polarization lidar to perform a comparison. The results showed that the correlation coefficients of the aerosol depolarization ratios were 0.73 and 0.77, respectively. Moreover, the two continuous observations demonstrated the robustness and stability of the DPCDL. The depolarization ratios were detected in different weather conditions.

Vast amounts of monitoring data can be obtained through various optical sensors, and mask detection based on deep learning integrates neural science into a variety of applications in everyday life. However, mask detection poses technical challenges such as small targets, complex scenes, and occlusions, which necessitate high accuracy and robustness in multi-scene target detection networks. Considering that multi-scale features can increase the receptive field and attention mechanism can improve the detection effect of small targets, we propose the YOLO-MSM network based on the multi-scale residual (MSR) block, multi-scale residual cascaded channel-spatial attention (MSR-CCSA) block, enhanced residual CCSA (ER-CCSA) block, and enhanced residual PCSA (ER-PCSA) block. Considering the performance and parameters, we use YOLOv5 as the baseline network. Firstly, for the MSR block, we construct hierarchical residual connections in the residual blocks to extract multi-scale features and obtain finer features. Secondly, to realize the joint attention function of channel and space, both the CCSA block and PCSA block are adopted. In addition, we construct a new dataset named Multi-Scene-Mask, which contains various scenes, crowd densities, and mask types. Experiments on the dataset show that YOLO-MSM achieves an average precision of 97.51%, showing better performance than other detection networks. Compared with the baseline network, the mAP value of YOLO-MSM is increased by 3.46%. Moreover, we propose a module generalization improvement strategy (GIS) by training YOLO-MSM on the dataset augmented with white Gaussian addition noise to improve the generalization ability of the network. The test results verify that GIS can greatly improve the generalization of the network and YOLO-MSM has stronger generalization ability than the baseline.

Microwave absorbers that are lightweight and have good stability and high efficiency have attracted much attention for their applications in many contemporary fields. In this work, a 3D porous (Ni@NO-C)n/NO-C composite absorber was prepared using a wet chemistry method with Ni chains and melamine as precursors, in which NO-C (N,O-doped carbon)-encapsulated Ni particles are homogenously dispersed in the 3D porous networks of NO-C in the form of (Ni@NO-C)n chains. The special microstructure of the as-prepared material is proven to be beneficial for the improvement of its microwave absorption performance. The as-synthesized (Ni@NO-C)n/NO-C composite absorber exhibited an effective absorption bandwidth of 4.1 GHz and an extremely large reflection loss of −72.3 dB. The excellent microwave-absorbing performances can be ascribed to the cooperative consequences of dielectric loss and magnetic loss, along with the balance between attenuation capability and impedance matching.

In the realm of remote sensing image analysis, the task of road extraction poses significant complexities, especially in the context of intricate scenes and diminutive targets. In response to these challenges, we have developed a novel deep learning network, christened CDAU-Net, designed to discern and delineate these features with enhanced precision. This network takes its structural inspiration from the fundamental architecture of U-Net while introducing innovative enhancements: we have integrated CoordConv convolutions into both the initial layer of the U-Net encoder and the terminal layer of the decoder, thereby facilitating a more efficacious processing of spatial information inherent in remote sensing images. Moreover, we have devised a unique mechanism termed the Deep Dual Cross Attention (DDCA), purposed to capture long-range dependencies within images—a critical factor in remote sensing image analysis. Our network replaces the skip-connection component of the U-Net with this newly designed mechanism, dealing with feature maps of the first four scales in the encoder and generating four corresponding outputs. These outputs are subsequently linked with the decoder stage to further capture the remote dependencies present within the remote sensing imagery. We have subjected CDAU-Net to extensive empirical validation, including testing on the Massachusetts Road Dataset and DeepGlobe Road Dataset. Both datasets encompass a diverse range of complex road scenes, making them ideal for evaluating the performance of road extraction algorithms. The experimental results showcase that whether in terms of accuracy, recall rate, or Intersection over Union (IoU) metrics, the CDAU-Net outperforms existing state-of-the-art methods in the task of road extraction. These findings substantiate the effectiveness and superiority of our approach in handling complex scenes and small targets, as well as in capturing long-range dependencies in remote sensing imagery. In sum, the design of CDAU-Net not only enhances the accuracy of road extraction but also presents new perspectives and possibilities for deep learning analysis of remote sensing imagery.

Background:. Pancreatic signet ring cell carcinoma (SRCC) is an exceedingly rare histological subtype of pancreatic cancer. Previous studies have focused on the trends of incidence and independent predictors of pancreatic SRCC. Our objectives of the study were to analyze the prognostic value of the lymph node ratio (LNR) and to explore the minimal number of lymph nodes examined to accurately evaluate the N stage in resected pancreatic SRCC. Method:. We analyzed 120 patients diagnosed from January 1, 1990, to December 31, 2016, constituted the study cohort from the Surveillance, Epidemiology, and End Results (SEER) registry. We calculated the overall survival (OS) of these patients by using a Kaplan–Meier analysis. The Kaplan–Meier analysis was used to analyze the influence of various factors on the prognosis of patients in the univariate analysis. The multivariate Cox analysis were applied to find independent prognostic factors of patients with pancreatic SRCC. Receiver-operating characteristic curve (ROC) analysis to investigate the discriminatory ability of the total number of lymph nodes examined (TNLE) relative to whether lymph node metastasis was present. Results:. The median number of lymph nodes examined among 120 patients with resected pancreatic SRCC was 14 (interquartile range: 6.25–20.0). According to the univariate analysis of OS, age, grade, chemotherapy, LNR, and TNLE were significantly different (P < .05). We demonstrated the prognostic benefit of chemotherapy in resected pancreatic SRCC, whereas radiotherapy was not associated with improved survival. The multivariate survival analysis showed that LNR and grade were independent prognostic indicators after pancreatic SRCC resection for OS. TNLE ≥ 8 showed the highest discriminatory power for evaluating lymph node metastasis (Area under curve (AUC): 0.656, 95% confidence interval: 0.564–0.741, Youden index:0.2533, sensitivity: 78.67%, specificity: 46.67%, P = .003). Conclusion:. Our study indicated that the LNR was a valuable independent prognostic factor for resected pancreatic SRCC. Regional lymphadenectomy of at least 8 lymph nodes was necessary to accurately stage patients. An adequate number of lymph nodes examined are necessary for clinicians to accurately predict the significance of the LNR in resected pancreatic SRCC.

This paper utilises a trivariate VAR-BEKK-GARCH model to investigate the dynamic relationships between global oil price, gold price, and European stock markets. This paper observes weak return spillover effects from the oil market to 6 European stock markets (Netherlands, Lithuania, Portugal, Czech Republic, Romania, and Slovenia) and from gold to Iceland, while there is no evidence of return spillovers from stock markets to oil and gold. The non-existence of return linkages between gold and stock (oil) suggests that the gold market plays a haven role. With reference to volatility spillovers, the results show obvious asymmetric bidirectional volatility interaction between the European stock markets and the global oil/gold markets. Stronger shock and volatility contagions from the European stock market to both oil and gold markets are observed compared with the opposite direction. For the volatility nexus between oil and gold, weak and moderate evidence of shock and volatility transmission from gold to oil markets is reported. Additionally, the study documents important and effective empirical implications for portfolio management and investment hedge strategies: firstly, adding European stock markets to a diversified oil/gold portfolio can achieve the expected returns while reducing risk; and secondly, the European investors can use the gold and oil markets to hedge against their stock market portfolio.

ObjectiveEvidence shows that the impairment of executive function (EF) is mainly attributed to the degeneration of frontal-striatal dopamine pathway. Glial cell line-derived neurotrophic factor (GDNF), as the strongest protective neurotrophic factor for dopaminergic neurons (DANs), may play a role in EF to some extent. This study mainly explored the correlation between serum GDNF concentration and EF performance in Parkinson’s disease (PD).MethodsThis study recruited 45 healthy volunteers (health control, HC) and 105 PD patients, including 44 with mild cognitive impairment (PD-MCI), 20 with dementia (PD-D), and 20 with normal cognitive function (PD-N). Neuropsychological tests were performed to evaluate EF (working memory, inhibitory control, and cognitive flexibility), attention, language, memory, and visuospatial function. All subjects were tested for serum GDNF and homovanillic acid (HVA) levels by ELISA and LC-ESI-MS/MS, respectively.ResultsPD-MCI patients showed impairments in the trail making test (TMT) A (TMT-A), TMT-B, clock drawing test (CDT) and semantic fluency test (SFT), whereas PD-D patients performed worse in most EF tests. With the deterioration of cognitive function, the concentration of serum GDNF and HVA in PD patients decreased. In the PD group, the serum GDNF and HVA levels were negatively correlated with TMT-A (rGDNF = −0.304, P < 0.01; rHVA = −0.334, P < 0.01) and TMT-B (rGDNF = −0.329, P < 0.01; rHVA = −0.323, P < 0.01) scores. Serum GDNF levels were positively correlated with auditory verbal learning test (AVLT-H) (r = 0.252, P < 0.05) and SFT (r = 0.275, P < 0.05) scores. Serum HVA levels showed a positively correlation with digit span test (DST) (r = 0.277, P < 0.01) scores. Stepwise linear regression analysis suggested that serum GDNF and HVA concentrations and UPDRS-III were the influence factors of TMT-A and TMT-B performances in PD patients.ConclusionThe decrease of serum GDNF concentration in PD patients was associated with impaired inhibitory control, cognitive flexibility, and attention performances. The changes of GDNF and HVA might synergistically participate in the occurrence and development of executive dysfunction in PD patients.

We investigated the functional mechanism of long non-coding small nucleolar host gene 17 (SNHG17) in diffuse large B-cell lymphoma (DLBCL). lncRNAs related to the prognosis of patients with DLBCL were screened to analyze long non-coding small nucleolar host gene 17 (SNHG17) expression in DLBCL and normal tissues, and a nomogram established for predicting DLBCL prognosis. SNHG17 expression in B-cell lymphoma cells was detected using qPCR. The effects of SNHG17 with/without doxorubicin on the proliferation and apoptosis of DoHH2 and Daudi were detected. The effects of combined SNHG17 and doxorubicin were analyzed. The regulatory function of SNHG17 in DLBCL was investigated using a mouse tumor xenotransplantation model. RNA sequencing was used to analyze the signaling pathways involved in SNHG17 knockdown in B-cell lymphoma cell lines. The target relationships among SNHG17, microRNA, and downstream mRNA biomolecules were detected. A higher SNHG17 level predicted a lower survival rate. SNHG17 was highly expressed in DLBCL patient tissues and cell lines. We established a prognostic model containing SNHG17 expression, which could effectively predict the overall survival rate of DLBCL patients. SNHG17 knockdown inhibited the proliferation and induced the apoptosis of B-cell lymphoma cells, and the combination of SNHG17 and doxorubicin had a synergistic effect. SNHG17, miR-34a-5p, and ZESTE gene enhancer homolog 2 (EZH2) had common hypothetical binding sites, and the luciferase reporter assay verified that miR-34a-5p was the direct target of SNHG17, and EZH2 was the direct target of miR-34a-5p. The carcinogenic function of SNHG17 in the proliferation and apoptosis of DLBCL cells was partially reversed by a miR-34a-5p inhibitor. SNHG17 increases EZH2 levels by inhibiting miR-34a-5p. Our findings indicate SNHG17 as critical for promoting DLBCL progression by regulating the EZH2 signaling pathway and sponging miR-34a-5p. These findings provide a new prognostic marker and therapeutic target for the prognosis and treatment of DLBCL.

Allergic rhinitis (AR) is a chronic upper airway immune-inflammation response mediated by immunoglobulin E (IgE) to allergens and can seriously affect the quality of life and work efficiency. Previous studies have shown that interleukin-1β (IL-1β) acts as a key cytokine to participate in and promote the occurrence and development of allergic diseases. It has been proposed that IL-1β may be a potential biomarker of AR. However, its definitive role and potential mechanism in AR have not been fully elucidated, and the clinical sample collection and detection methods were inconsistent among different studies, which have limited the use of IL-1β as a clinical diagnosis and treatment marker for AR. This article systematically summarizes the research advances in the roles of IL-1β in allergic diseases, focusing on the changes of IL-1β in AR and the possible interventions. In addition, based on the findings by our team, we provided new insights into the use of IL-1β in AR diagnosis and treatment, in an attempt to further promote the clinical application of IL-1β in AR and other allergic diseases.

Background. Obesity-related diseases have important implications for the occurrence, severity, and outcome of ischemic heart disease. Patients with obesity, hyperlipidemia, and diabetes mellitus (metabolic syndrome) are at increased risk of heart attack with decreased plasma lipocalin levels, and lipocalin is negatively correlated with heart attack incidence. APPL1 is a signaling protein with multiple functional structural domains and plays an important role in the APN signaling pathway. There are two known subtypes of lipocalin membrane receptors, AdipoR1 and AdipoR2. AdioR1 is mainly distributed in skeletal muscle while AdipoR2 is mainly distributed in the liver. Objective. To clarify whether the AdipoR1-APPL1 signaling pathway mediates the effect of lipocalin in reducing myocardial ischemia/reperfusion injury and its mechanism will provide us with a new approach to treat myocardial ischemia/reperfusion injury using lipocalin as an intervention and therapeutic target. Methods. (1) Induction of hypoxia/reoxygenation in SD mammary rat cardiomyocytes to simulate myocardial ischemia/reperfusion; (2) downregulation of APPL1 expression in cardiomyocytes to observe the effect of lipocalin on myocardial ischemia/reperfusion and its mechanism of action. Results. (1) Primary mammary rat cardiomyocytes were isolated and cultured and induced to simulate MI/R by hypoxia/reoxygenation; (2) lipocalin inhibited H/R-induced apoptosis in cardiomyocytes; and (3) APN attenuated MI/R injury through AdipoR1-APPL1 and the possible mechanism. Conclusion. This study demonstrates for the first time that lipocalin can attenuate myocardial ischemia/reperfusion injury through the AdipoR1-APPL1 signaling pathway and that the reduction of AdipoR1/APPL1 interaction plays an important role in cardiac APN resistance to MI/R injury in diabetic mice.

To overcome the problems of wind turbine (WT) degradation assessment, a new kernel entropy method based on supervisory control and data acquisition (SCADA) was proposed. This approach can be used to effectively monitor and assess WT performance degradation. First, a new condition monitoring method based on a kernel entropy component analysis (KECA) was developed for nonlinear data. Then, the squared prediction error (SPE) was used to monitor the WT health state. Due to the diversity and nonlinearity of SCADA data, fault features are easily overwhelmed by other vibration signals. To address this, a new kernel entropy partial least squares (KEPLS) algorithm was introduced. The proposed kernel entropy method improves the performance prediction by considering higher order information. Furthermore, changes in the prediction residual can be used to define certain limits to realize early warning of WT faults. Finally, the method was applied to actual SCADA data of a wind farm. The results show that the method can accurately evaluate the health state of WTs, thus verifying the effectiveness and feasibility of the proposed method.

Timely and accurate estimation of the sugarcane planting area is of vital importance to the country’s agricultural production and sugar development. In remote sensing crop mapping based on spectral similarity, there will be have a phenomenon of foreign matters with the same spectrum by obtaining an accurate crop reference curve for crop identification, limiting mapping accuracy. In this study, we improved the spectral reconstruction method based on singular value decomposition (SR-SVD). A decision tree model was established based on the similarity of the sugarcane Normalized Difference Vegetation Index (NDVI) time series curve and the fluctuation range of NDVI in different growth periods. Using the Sentinel-2 (Level-2A) image data set to extract sugarcane planting area in two regions of Chongzuo City, Guangxi, China, the overall accuracy was higher than 96%, respectively. The results show that through the spectral similarity and the determination of the threshold fluctuation range, not only high-precision mapping of sugarcane can be achieved, but the problem of “same spectrum with different objects” can also be solved. Therefore, this method can provide accurate information on the sugarcane planting areas and technical support for monitoring the structure of sugarcane planting in the region.

In this study, we address the limitations of current deep learning models in road extraction tasks from remote sensing imagery. We introduce MixerNet-SAGA, a novel deep learning model that incorporates the strengths of U-Net, integrates a ConvMixer block for enhanced feature extraction, and includes a Scaled Attention Gate (SAG) for augmented spatial attention. Experimental validation on the Massachusetts road dataset and the DeepGlobe road dataset demonstrates that MixerNet-SAGA achieves a 10% improvement in precision, 8% in recall, and 12% in IoU compared to leading models such as U-Net, ResNet, and SDUNet. Furthermore, our model excels in computational efficiency, being 20% faster, and has a smaller model size. Notably, MixerNet-SAGA shows exceptional robustness against challenges such as same-spectrum–different-object and different-spectrum–same-object phenomena. Ablation studies further reveal the critical roles of the ConvMixer block and SAG. Despite its strengths, the model’s scalability to extremely large datasets remains an area for future investigation. Collectively, MixerNet-SAGA offers an efficient and accurate solution for road extraction in remote sensing imagery and presents significant potential for broader applications.

An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

In order to effectively utilize acquired remote sensing imagery and improve the completeness of information extraction, we propose a new road extraction model called C2S-RoadNet. C2S-RoadNet was designed to enhance the feature extraction capability by combining depth-wise separable convolution with lightweight asymmetric self-attention based on encoder and decoder structures. C2S-RoadNet is able to establish long-distance dependencies and fully utilize global information, and it better extracts road information. Based on the lightweight asymmetric self-attention network, a multi-scale adaptive weight module was designed to aggregate information at different scales. The use of adaptive weights can fully harness features at different scales to improve the model’s extraction performance. The strengthening of backbone information plays an important role in the extraction of road main branch information, which can effectively improve the integrity of road information. Compared with existing deep learning algorithms based on encoder–decoder, experimental results on various public road datasets show that the C2S-RoadNet model can produce more complete road extraction, especially when faced with scenarios involving occluded roads or complex lighting conditions. On the Massachusetts road dataset, the PA, F1 score, and IoU reached 98%, 77%, and 72%, respectively. Furthermore, on the DeepGlobe dataset, the PA, F1 score, and IoU reached 98%, 78%, and 64%, respectively. The objective performance evaluation indicators also significantly improved on the LSRV dataset, and the PA, F1 score, and IoU reached 96%, 82%, and 71%, respectively.

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique.

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect.

Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to improving the spatiotemporal targeting of fire prevention efforts. In this study, Nanning, China, was selected as the research area. The wildfire driving factors were chosen from both seasonal and nonseasonal aspects, and the datasets were divided into five periods: all seasons, spring, summer, autumn, and winter. The light gradient boosting machine (LGBM) was employed to construct wildfire danger models for different periods, evaluating the spatial distribution of high-wildfire-danger areas during these periods and the predictive performance differences. The SHapley Additive exPlanations (SHAP) method was utilized to analyze the differential contributions of various factors to wildfire occurrence in different seasons. Subsequently, the remote sensing ecological index (RSEI) was calculated using four indicators, greenness, heat, wetness, and dryness, to assess the ecological vulnerability in different seasons. Finally, by integrating danger and vulnerability information, wildfire risk models were developed to systematically assess the risk of wildfire disasters causing losses to the ecological environment in different seasons. The results indicate that: (1) The evaluation of wildfire danger based on individual seasons effectively compensates for the shortcomings of analyzing danger across all seasons, exhibiting higher predictive performance and richer details. (2) Wildfires in Nanning primarily occur in spring and winter, while the likelihood of wildfires in summer and autumn is relatively lower. In different seasons, NDVI is the most critical factor influencing wildfire occurrence, while slope is the most important nonseasonal factor. The influence of factors varies among different seasons, with seasonal factors having a more significant impact on wildfire danger. (3) The ecological vulnerability in Nanning exhibits significant differences between different seasons. Compared to spring and winter, the ecological environment is more vulnerable to wildfire disasters during summer and autumn. (4) The highest wildfire risk occurs in spring, posing the greatest threat to the ecological environment, while the lowest wildfire risk is observed in winter. Taking into account information on danger and vulnerability in different seasons enables a more comprehensive assessment of the risk differences in wildfire disasters causing ecological losses. The research findings provide a scientific theoretical basis for relevant departments regarding the prevention, control, and management of seasonal wildfires.

Enhancing the absorption and utilization of phosphorus by crops is an important aim for ensuring food security worldwide. However, the gene regulatory network underlying phosphorus use in foxtail millet remains unclear. In this study, the molecular mechanism underlying low-phosphorus (LP) responsiveness in foxtail millet was evaluated using a comparative transcriptome analysis. LP reduced the chlorophyll content in shoots, increased the anthocyanin content in roots, and up-regulated purple acid phosphatase and phytase activities as well as antioxidant systems (CAT, POD, and SOD). Finally, 13 differentially expressed genes related to LP response were identified and verified using transcriptomic data and qRT-PCR. Two gene co-expression network modules related to phosphorus responsiveness were positively correlated with POD, CAT, and PAPs. Of these, SiPHR1, functionally annotated as PHOSPHATE STARVATION RESPONSE 1, was identified as an MYB transcription factor related to phosphate responsiveness. SiPHR1 overexpression in Arabidopsis significantly modified the root architecture. LP stress caused cellular, physiological, and phenotypic changes in seedlings. SiPHR1 functioned as a positive regulator by activating downstream genes related to LP tolerance. These results improve our understanding of the molecular mechanism underlying responsiveness to LP stress, thereby laying a theoretical foundation for the genetic modification and breeding of new LP-tolerant foxtail millet varieties.

The global navigation satellite system–interferometric reflectometry (GNSS-IR) technique has emerged as an effective coastal sea-level monitoring solution. However, the accuracy and stability of GNSS-IR sea-level estimation based on quadratic fitting are limited by the retrieval range of reflector height (RH range) and satellite-elevation range, reducing the flexibility of this technology. This study introduces a new GNSS-IR sea-level estimation model that combines local mean decomposition (LMD) and Lomb–Scargle periodogram (LSP). LMD can decompose the signal-to-noise ratio (SNR) arc into a series of signal components with different frequencies. The signal components containing information from the sea surface are selected to construct the oscillation term, and its frequency is extracted by LSP. To this end, observational data from SC02 sites in the United States are used to evaluate the accuracy level of the model. Then, the performance of LMD and the influence of noise on retrieval results are analyzed from two aspects: RH ranges and satellite-elevation ranges. Finally, the sea-level variation for one consecutive year is estimated to verify the stability of the model in long-term monitoring. The results show that the oscillation term obtained by LMD has a lower noise level than other signal separation methods, effectively improving the accuracy of retrieval results and avoiding abnormal values. Moreover, it still performs well under loose constraints (a wide RH range and a high-elevation range). In one consecutive year of retrieval results, the new model based on LMD has a significant improvement effect over quadratic fitting, and the root mean square error and mean absolute error of retrieval results obtained in each month on average are improved by 8.34% and 8.87%, respectively.

Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResUnet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions.

Research on wildfire risk can quantitatively assess the risk of wildfire damage to the population, economy, and natural ecology. However, existing research has primarily assessed the spatial risk of wildfires across an entire region, neglecting the impact of different land-use types on the assessment outcomes. The purpose of the study is to construct a framework for assessing wildfire risk in different land-use types, aiming to comprehensively assess the risk of wildfire disasters in a region. We conducted a case study in Central China, collecting and classifying historical wildfire samples according to land-use types. The Light Gradient Boosting Machine (LGBM) was employed to construct wildfire susceptibility models for both overall and individual land-use types. Additionally, a subjective and objective combined weighting method using the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was utilized to build the wildfire vulnerability model. By integrating susceptibility and vulnerability information, we comprehensively assessed the combined risk of wildfire disasters across land-use types. The results demonstrate the following: (1) Assessing wildfire susceptibility based on different land-use types compensated for limitations in analyzing overall wildfire susceptibility, with a higher prediction performance and more detailed susceptibility information. (2) Significant variations in wildfire susceptibility distribution existed among different land-use types, with varying contributions of factors. (3) Using the AHP-EWM combined weighting method effectively addressed limitations of a single method in determining vulnerability. (4) Land-use types exerted a significant impact on wildfire risk assessment in Central China. Assessing wildfire risk for both overall and individual land-use types enhances understanding of spatial risk distribution and specific land use risk. The experimental results validate the feasibility and effectiveness of the proposed evaluation framework, providing guidance for wildfire prevention and control.

Abstract Objective To evaluate cognitive impairment and risk factors of elders in high fluoride drinking water areas and investigate whether DKK1 is involved in this disorder. Methods MoCA-B and AD-8 were used to measure the cognitive functions of 272 and 172 subjects over the age of 60 came from the high and normal fluoride drinking water areas respectively, general information and peripheral blood were collected, the level of SOD, GSH and MDA were measured, mRNA level of DKK1, the concentration of blood fluoride and the polymorphism of APOE were tested. Results The blood fluoride concentration, mRNA level of DKK1 and ratio of abnormal cognitive function of subjects in high fluorine drinking water areas were higher than those in normal areas. The level of SOD of subjects in high fluorine drinking water was low compared with those in normal areas. The level of MDA and GSH had no difference between the two crowds in different fluorine drinking water areas. There were differences in cigarette smoking, education, dental status, hypertension, hyperlipidaemia and APOE results between the two crowds in different fluorine drinking water areas. The mRNA level of DKK1 and the level of cognitive function showed a positive correlation and DKK1 was one of five risk factors involved in cognitive impairment of older people living in high fluorosis areas. Conclusions The cognitive functions could be impaired in the older people living in high fluoride drinking water areas, and DKK1 may as a potential intervention point of this brain damage process need attention.

Abstract Background Cervical cancer shows great differences in depth of invasion, metastasis, and other biological behaviors. The location of the lesion is special, so it is usually difficult to determine the clinical stage. This study aimed to explore the clinical value of magnetic resonance imaging (MRI) and tumor serum markers for the preoperative diagnosis of cervical cancer lymph node metastasis and para-uterine invasion. Methods A total of 200 patients with cervical cancer admitted to our hospital from January 2019 to January 2020 were collected as the research subjects. Comparing the diagnosis results of preoperative MRI scan, serum tumor markers, and postoperative pathological examination using single factor comparison, we determined the MRI scan results, the comprehensive matching rate between serum tumor markers (squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125)) and postoperative pathological results, and the differences of sensitivity, specificity, and accuracy in the prediction of lymph node metastasis and para-uterine infiltration of cervical cancer. Results The levels of SCCA and CA125 in patients with para-uterine invasion and lymph node metastasis were higher than those of patients without invasion and metastasis. Among them, the level of SCCA was significantly different (P0.05), so MRI combined with serum SCCA was selected for combined diagnosis in the later period. The sensitivity, specificity, and accuracy of MRI diagnosis of cervical cancer and para-uterine infiltrating lymph node metastasis and metastasis were 55.2, 91.6, and 89.5% and 55.2, 91.6, and 89.5%, respectively. These data in MRI combined with serum SCCA were 76.3, 95.3, and 94.3% and 63.2, 96.0, and 95.1%, respectively. The accuracy of tumor markers combined with MRI in the diagnosis of cervical cancer lymph node metastasis and para-uterine invasion was higher than that of MRI. Conclusions MRI combined with serum SCCA can more accurately identify cervical cancer lymph node metastasis and para-uterine invasion compared with MRI alone. Tumor marker combined with MRI diagnosis is an important auxiliary method for cervical cancer treatment and can provide comprehensive and reliable clinical evidence for evaluation before cervical cancer surgery.

Abstract Background In this study, we aimed to perform a comprehensive analysis on the metagenomic next-generation sequencing for the etiological diagnosis of septic patients, and further to establish optimal read values for detecting common pathogens. Methods In this single-center retrospective study, septic patients who underwent pathogen detection by both microbial culture and metagenomic next-generation sequencing in the intensive care unit of the Second People’s Hospital of Shenzhen from June 24, 2015, to October 20, 2019, were included. Results A total of 193 patients with 305 detected specimens were included in the final analysis. The results of metagenomic next-generation sequencing showed significantly higher positive rates in samples from disparate loci, including blood, bronchoalveolar lavage fluid, and cerebrospinal fluid, as well as in the determination of various pathogens. The optimal diagnostic reads were 2893, 1825.5, and 892.5 for Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae, respectively. Conclusions The metagenomic next-generation sequencing is capable of identifying multiple pathogens in specimens from septic patients, and shows significantly higher positive rates than culture-based diagnostics. The optimal diagnostic reads for frequently detected microbes might be useful for the clinical application of metagenomic next-generation sequencing in terms of timely and accurately determining etiological pathogens for suspected and confirmed cases of sepsis due to well-performed data interpretation.

The Paris Agreement has prompted much interest in the societal and health impacts of limiting global warming to 1.5 °C and 2 °C. Previous assessments of differential impacts of two targets indicate that 1.5 °C warming target would substantially reduce the impact on human health compared to 2 °C, but they mainly focused on the magnitude of temperature changes under future climate change scenarios without any consideration of greater frequency of cumulative heat exposures within a day. Here we quantified the health risks of compound daytime and nighttime hot extremes using morbidity data in a megacity of China, and also identified the time-period of heat exposure with higher risks. Then we projected future morbidity burden attributable to compound hot extremes due to the half-degree warming. We estimated that the 2 °C warming scenario by 2100 as opposed to 1.5 °C would increase annual heat-related ambulance dispatches by 31% in Shenzhen city. Substantial additional impacts were associated with occurrence of consecutive hot days and nights, with ambulance dispatches increased by 82%. Our results suggested that compound hot extremes should be considered in assessment of heat-related health impacts, particularly in the context of climate change. Minimizing the warming of climate in a more ambitious target can significantly reduce the health damage.

Abstract Tumor necrosis factor (TNF)-α-induced protein 8-like 2 (TIPE2) is a newly discovered negative immunoregulatory protein that is involved in various cellular immune responses to infections. However, the underlying mechanism by which TIPE2 affects the immune function of dendritic cells (DCs) is not yet understood. This study aimed to determine the correlations among DCs TIPE2 expression, autophagic activity and immune function in the context of sepsis. In addition, the signaling pathway by which TIPE2 regulates autophagy in DCs was investigated. We reported for the first time that TIPE2 overexpression (knock-in, KI) exerted an inhibitory effect on autophagy in DCs and markedly suppressed the immune function of DCs upon septic challenge both in vitro and in vivo. In addition, TIPE2 knockout (KO) in DCs significantly enhanced autophagy and improved the immune response of DCs in sepsis. Of note, we found that the transforming growth factor-β (TGF-β)-activated kinase-1 (TAK1)/c-Jun N-terminal kinase (JNK) pathway was inhibited by TIPE2 in DCs, resulting in downregulated autophagic activity. Collectively, these results suggest that TIPE2 can suppress the autophagic activity of DCs by inhibiting the TAK1/JNK signaling pathway and further negatively regulate the immune function of DCs in the development of septic complications.

BackgroundCyclin-dependent kinase 4/6 inhibitor (CDK4/6i) has become the commonest first-line treatment of hormonal receptor positive and human epidermal growth factor receptor 2 negative (HR+/HER2-) metastatic breast cancer (MBC). However, therapy is quite individualized after progression of disease (PD) when CDK4/6i fails. Estrogen receptor (ER) status of metastatic lesions of bone, lung or liver might be different from the primary tumor and biopsy of metastatic lesions was invasive and not always available. Prediction of treatment response after PD of CDK4/6i remains unsolved. 18F-fluoroestradiol (FES) PET/CT could non-invasively reveal ER expression both in primary and metastatic breast cancer and recognize heterogeneity of ER status.Case presentationA 70-year-old woman with Parkinson’s disease, osteoporosis and cardiovascular co-morbidity was diagnosed with HR+/HER2- breast cancer (pT2N2M0, stage IIIa). Three years later, she developed metastases in right lung and pleura with pleural effusion and received palbociclib + letrozole. After 8 months the disease progressed, and 18F-FES PET/CT revealed multiple ER-positive pleural lesions and ER-negative pulmonary nodules after PD and the progression-free survival (PFS) of first-line CDK4/6i was 8 months. Since most of the metastatic lesions were ER-positive, abemaciclib + fulvestrant were chosen as the second-line CDK4/6i treatment and the PFS was 15 months. Another 18F-FES PET/CT showed a new ER-positive pleural mass with multiple ER-negative pulmonary nodules. Since 18F-FES PET/CT revealed that the dominant lesions were still ER-positive, dalpiciclib + exemestane + fulvestrant were prescribed as the third-line CDK4/6i treatment. Currently the patient’s disease had been stable for 2 months.ConclusionThis case demonstrated that 18F-FES PET/CT could show ER heterogeneity non-invasively and reveal the treatment responses a predictive imaging tool of serial second- and third-line of CDK4/6i treatments when first-line CDK4/6i failed in HR+/HER2- MBC. So long as the dominant or newly-developed metastatic lesion was ER-positive on 18F-FES PET after first-line CDK4/6i, the patient might show certain therapeutic response towards endocrine-based treatment including second- and third-line of CDK4/6i, and thus increased the time to chemotherapy (TTC).

Triplex-forming oligonucleotides (TFOs) can bind to the major groove of double-stranded DNA with high specificity and affinity and inhibit gene expression. Triplex-forming oligonucleotides have gained prominence because of their potential applications in antigene therapy. In particular, the target specificity of triplex-forming oligonucleotides combined with their ability to suppress oncogene expression has driven their development as anti-cancer agents. So far, triplex-forming oligonucleotides have not been used for clinical treatment and seem to be gradually snubbed in recent years. But triplex-forming oligonucleotides still represent an approach to down-regulate the expression of the target gene and a carrier of active substances. Therefore, in the present review, we will introduce the characteristics of triplex-forming oligonucleotides and their anti-cancer research progress. Then, we will discuss the challenges in their application.