Search

Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.

Introduction The therapeutic needs of women with diminished ovarian reserve (DOR), coupled with the increasing application of acupuncture in improving ovarian function, have highlighted the need to verify the efficacy and safety of acupuncture for DOR. This study aims to provide high-quality evidence by evaluating both ovarian reserve and in vitro fertilisation (IVF) outcomes.Methods and analysis A large-scale, multicentre, randomised controlled trial will be carried out across seven hospitals in China. 400 women with DOR will be randomised in a 1:1 ratio to an acupuncture group or a sham acupuncture group. Acupuncture or sham acupuncture will consist of 36 sessions per participant over 12 weeks. The primary outcome will be the change in antral follicle count (AFC) at week 12 from baseline. Secondary outcomes are AFC at week 24, the serum levels of basal follicle-stimulating hormone and anti-Mullerian hormone at weeks 12 and 24, the scores of the Self-Rating Anxiety Scale at weeks 12 and 24, clinical pregnancy rate, and IVF embryo transfer related outcomes. Any adverse events during treatment will be documented.Ethics and dissemination The study protocol has been approved by all the participating institutions. Written informed consent will be obtained prior to participant enrolment. The results of this study will be published in peer-reviewed publications.Trial registration number ChiCTR2200062295Protocol version V2.0-20220317

The free and bound phenolic profiles and their bioactivities of radix puerariae thomsonii (RPT) cultivars from 7 growing regions in China were investigated. Total phenolic and flavonoid contents were from 148.71 to 435.32 mg gallic acid equivalents /100 g dry weight and 561.93 to 826.11 mg catechin equivalents /100 g dry weight, respectively, with 20.64–38.28% and 32.77–47.29% contribution from bound fractions. Sixteen phenolic compounds were detected in RPTs. Bound fractions contributed 28.15–70.84% to the total antioxidant activities. The cultivars from Qiannan and Guangzhou showed much higher regulatory effects on carbohydrate hydrolyzing enzymes and alcohol metabolizing enzymes than the other cultivars. The bound fractions exhibited equivalent EC50 values for alcohol metabolizing enzymes and IC50 values for carbohydrate hydrolyzing enzymes to the free fractions in RPT cultivars. Therefore, bound phenolics significantly contributed to the potential health benefits of RPT. The results provided information for the utilization of RPT for health promoting purpose.

Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted ℓ2/3-norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ℓq(q = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply ℓ2/3 regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method.

Abstract Background Although increasing physical activity (PA) has been suggested to prevent and manage cognitive decline and dementia, its economic impact on healthcare systems and society is largely unknown. This study aimed to summarize evidence on the cost-effectiveness of PA interventions to prevent and manage cognitive decline and dementia. Methods Electronic databases, including PubMed/MEDLINE, Embase, and ScienceDirect, were searched from January 2000 to July 2023. The search strategy was driven by a combination of subject-heading terms related to physical activity, cognitive function, dementia, and cost-effectiveness. Selected studies were included in narrative synthesis, and extracted data were presented in narrative and tabular forms. The risk of bias in each study was assessed using the Consolidated Health Economic Evaluation Reporting Standards and Consensus on Health Economic Criteria list. Results Five of the 11 identified studies focused on individuals with existing dementia. Six of the 11 identified studies focused on individuals with no existing dementia, including 3 on those with mild cognitive impairment (MCI), and 3 on those with no existing MCI or dementia. PA interventions focused on individuals with no existing dementia were found to be cost-effective compared to the control group. Findings were mixed for PA interventions implemented in individuals with existing dementia. Conclusions PA interventions implemented before or during the early stage of cognitive impairment may be cost-effective in reducing the burden of dementia. More research is needed to investigate the cost-effectiveness of PA interventions in managing dementia. Most existing studies used short-term outcomes in evaluating the cost-effectiveness of PA interventions in the prevention and management of dementia; future research should consider adding long-term outcomes to strengthen the study design.

Abstract Atherosclerosis is a chronic inflammatory disease with high morbidity and mortality rates worldwide. Doublecortin‐like kinase 1 (DCLK1), a microtubule‐associated protein kinase, is involved in neurogenesis and human cancers. However, the role of DCLK1 in atherosclerosis remains undefined. In this study, we identified upregulated DCLK1 in macrophages in atherosclerotic lesions of ApoE−/− mice fed an HFD and determined that macrophage‐specific DCLK1 deletion attenuates atherosclerosis by reducing inflammation in mice. Mechanistically, RNA sequencing analysis indicated that DCLK1 mediates oxLDL‐induced inflammation via NF‐κB signaling pathway in primary macrophages. Coimmunoprecipitation followed by LC–MS/MS analysis identified IKKβ as a binding protein of DCLK1. We confirmed that DCLK1 directly interacts with IKKβ and phosphorylates IKKβ at S177/181, thereby facilitating subsequent NF‐κB activation and inflammatory gene expression in macrophages. Finally, a pharmacological inhibitor of DCLK1 prevents atherosclerotic progression and inflammation both in vitro and in vivo. Our findings demonstrated that macrophage DCLK1 promotes inflammatory atherosclerosis by binding to IKKβ and activating IKKβ/NF‐κB. This study reports DCLK1 as a new IKKβ regulator in inflammation and a potential therapeutic target for inflammatory atherosclerosis.

River ecosystems receive a large amount of organic matter, which will increase the production of greenhouse gases (GHGs), including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Despite extensive research on the distribution of greenhouse gases and dissolved organic matter (DOM), little is known about the associations between greenhouse gases and DOM compositions. In this study, the distributions of GHGs (CO2, CH4 and N2O) and DOM in the overlying water of the main rivers around Taihu Lake were investigated. The results showed that the concentration of GHGs was positively correlated with dissolved organic carbon concentrations. Three-dimensional excitation-emission matrix fluorescence spectroscopy techniques were employed to identify the source of the DOM, which was related to protein-like and humic-like components. The DOM was a combination of terrigenous and endogenous origins. The GHGs (except CO2) were significantly associated with DOM composition. These results emphasize the importance of the relationship between GHGs (CO2, CH4 and N2O) and DOM compositions in river ecosystems.

Abstract Objective Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test. Results Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively. Conclusions The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

The excessive input of nutrients into rivers can lead to contamination and eutrophication, which poses a threat to the health of aquatic ecosystems. It is crucial to identify the sources of contaminants to develop effective management plans for eutrophication. However, traditional methods for identifying pollution sources have been insufficient, making it difficult to manage river health effectively. High-throughput sequencing offers a novel method for microbial community source tracking, which can help identify dominant pollution sources in rivers. The Wanggang River was selected for study, as it has suffered accelerated eutrophication due to considerable nutrient input from riparian pollutants. The present study identified the dominant microbial communities in the Wanggang River basin, including Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia, and Firmicutes. The Source Tracker machine-learning classification system was used to create source-specific microbial community fingerprints to determine the primary sources of contaminants in the basin, with agricultural fertilizer being identified as the main pollutant source. By identifying the microbial communities of potential pollution sources, the study determined the contributing pollutant sources in several major sections of the Wanggang River, including industry, urban land, pond culture, and livestock land. These findings can be used to improve the identification of pollution sources in specific environments and develop effective pollution management plans for polluted river water.

Recent small-scale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions? (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion? and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data? We address these questions by developing a mechanism for incremental training of semantic segmentation models. We call the resulting extension task-specific model updates (TSMU). A national compilation of landslide inventories by the U.S. Geological Survey (USGS) was used to develop a global database for this study. Our results indicate that the TSMU framework can be used to aid in the creation of new landslide inventories or expanding existing datasets, and also to rapidly develop hazard maps for situational awareness following a widespread landslide event.

BackgroundType 2 diabetes mellitus (T2DM) and hypertension commonly coexist, and we presumed that T2DM might mediate the relationship between some shared risk factors and systolic blood pressure (SBP).MethodsThe causal association between T2DM and SBP was first confirmed using Mendelian randomization (MR) analyses, and a two-step MR design was then used to test the causal mediating effect of T2DM on the relationship between 107 traits and SBP using summary statistics from genome-wide association studies.ResultsT2DM was causally associated with SBP. The univariable MR of the two-step causal mediation analyses suggested that 44 and 45 of the 107 traits had causal associations with T2DM and SBP, respectively. Five of the 27 traits that were significantly associated with both T2DM and SBP could not be reversely altered by T2DM and were included in the second step of the causal mediation analyses. The results indicated that most of the investigated traits causally altered SBP independent of T2DM, but the partial causal mediating effect of T2DM on the association between fasting insulin and SBP was successfully identified with a mediation proportion of 33.6%.ConclusionsOur study provides novel insights into the role of risk factors in the comorbidity of T2DM and high blood pressure, which is important for long-term disease prevention and management.

Intra-annual monitoring of stem radial growth dynamics of trees and detecting how tree growth responds to changing climatic conditions are crucial for predicting the future growth dynamics under increasing drought conditions. Here, we monitored the intra-annual stem radial growth dynamics of seven Larix principis-rupprechtii using point dendrometers to investigate the influence of climate variables on the intra-annual growth of L. principis-rupprechtii in the growing season. The average stem radial growth of L. principis-rupprechtii started on 29 April and stopped on 17 August based on the sigmoid Gompertz functions. The intra-annual stem radial growth of L. principis-rupprechtii showed a parabolic trend, with its growth decreased when the temperature and sunlight duration hours exceeded certain thresholds. The vapor pressure deficits (VPD) strongly influenced tree intra-annual growth over other climatic factors in the growing season. Stem radial growth of L. principis-rupprechtii decreased significantly with increasing VPD when VPD ranged from 0.5 to 0.8 kPa. Intra-annual stem radial growth of L. principis-rupprechtii was severely inhibited when VPD was higher than 0.8 kPa. In contrast, tree stem radial growth reached the maximum when VPD was lower than 0.5 kPa. Our study highlighted the important influences of major limiting climatic factors on the stem radial growth of trees in semi-arid regions.

The eccentric RHS (rectangular hollow sections) joint offers improved mechanical properties and better space utilization. Its use in frame structures has gained significant attention. Currently, the initial rotational stiffness of RHS joints, the simplified finite element analysis method of eccentric RHS joints, and the influence of the spatial effect of RHS joints are still unknown. The purpose of this research is to establish a calculation formula for the initial rotational stiffness of eccentric RHS joints, study the influence of the spatial effect under complex stress conditions, and propose a mathematical model that can be used to simplify the analysis of eccentric RHS joints. The research findings indicate that the web plate’s deformation stiffness primarily influences the joints’ initial rotational stiffness. This increases with a higher beam-to-column depth-to-width ratio, beam-to-column thickness ratio, and column width-to-thickness ratio. The form of the moment distribution in the joint changes, and begins to have a significant effect on the rotational stiffness when the beam-to-column flange width ratio reaches and exceeds 0.7. The stiffeners have a direct additive effect on the joint stiffness. The influence of adjacent beams on the joint is minimal, and the spatial effect of the joint can be disregarded. Furthermore, the finite element analysis confirmed the accuracy of the power function model in accurately simulating the static load behavior of the joint, particularly the bending moment–angle relationship.

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

ObjectiveAlthough alcohol abuse has been indicated to cause cerebral aneurysm development and rupture, there is limited data on the impact of alcohol abuse on outcomes after an aneurysmal subarachnoid hemorrhage (aSAH). This study aims to investigate whether alcohol abuse increases the risk of angiographic vasospasm and delayed cerebral ischemia (DCI) in critically ill patients with aSAH.MethodsWe conducted a secondary analysis based on a retrospective study in a French university hospital intensive care unit (ICU). Patients with aSAH requiring mechanical ventilation hospitalized between 2010 and 2015 were included. Patients were segregated according to alcohol abuse (yes or no). Multivariable logistic regression analysis was used to identify the independent risk factors associated with angiographic vasospasm and DCI.ResultsThe patient proportion of alcohol abuse was dramatically greater in males than that in females (p < 0.001). The Simplified Acute Physiology Score II (SAPSII) score on admission did not show a statistical difference. Neither did the World Federation of Neurosurgical Societies (WFNS) and Fisher scores. Patients with alcohol abuse were more likely to develop angiographic vasospasm (OR 3.65, 95% CI 1.17–11.39; p = 0.0260) and DCI (OR 3.53, 95% CI 1.13–10.97; p = 0.0294) as evidenced by multivariable logistic regression analysis.ConclusionsIn this study, patients with alcohol abuse are at higher odds of angiographic vasospasm and DCI, which are related to poor prognosis following aSAH. These findings are important for the prevention and clinical management of aSAH.

Background: Hong Qu glutinous rice wine (HQGRW) is brewed under non-aseptic fermentation conditions, so it usually has a relatively high total acid content. The aim of this study was to investigate the dynamics of the bacterial communities and total acid during the fermentation of HQGRW and elucidate the correlation between total acid and bacterial communities. Results: The results showed that the period of rapid acid increase during fermentation occurred at the early stage of fermentation. There was a negative response between total acid increase and the rate of increase in alcohol during the early fermentation stage. Bacterial community analysis using high-throughput sequencing technology was found that the dominant bacterial communities changed during the traditional fermentation of HQGRW. Both principal component analysis (PCA) and hierarchical clustering analysis revealed that there was a great difference between the bacterial communities of Hong Qu starter and those identified during the fermentation process. Furthermore, the key bacteria likely to be associated with total acid were identified by Spearman's correlation analysis. Lactobacillus, unclassified Lactobacillaceae, and Pediococcus were found, which can make significant contributions to the total acid development (|r| > 0.6 with FDR adjusted P

Background. Diaphanous related formin 1 (DIAPH1) is a novel component of advanced glycation end product (AGE) signal transduction that was recently found to participate in diabetes-related disorders, obesity, and androgen hormones. We investigated whether plasma DIAPH1 levels were a potential prognostic predictor for polycystic ovary syndrome (PCOS). Methods. The levels of circulating plasma DIAPH1 and indicators of glucose, insulin, lipid metabolism, liver enzymes, kidney function, sex hormones, and inflammation were measured in 75 patients with PCOS and 77 healthy participants. All of the participants were divided into normal-weight (NW) and overweight/obese (OW) subgroups. Statistical analyses were performed with R studio. Results. PCOS patients manifested hyperandrogenism, increased luteinizing hormone/follicle-stimulating hormone (LH/FSH), and accumulated body fat and insulin resistance. Plasma DIAPH1 levels were significantly decreased in women with PCOS compared to control participants, and DIAPH1 levels were distinctly reduced in OW PCOS compared to OW control subjects (P

Novel polymers applied in economic membrane technologies are a perennial hot topic in the fields of natural gas purification and O2 enrichment. Herein, novel hypercrosslinked polymers (HCPs) incorporating 6FDA-based polyimide (PI) MMMs were prepared via a casting method for enhancing transport of different gases (CO2, CH4, O2, and N2). Intact HCPs/PI MMMs could be obtained due to good compatibility between the HCPs and PI. Pure gas permeation experiments showed that compared with pure PI film, the addition of HCPs effectively promotes gas transport, increases gas permeability, and maintains ideal selectivity. The permeabilities of HCPs/PI MMMs toward CO2 and O2 were as high as 105.85 Barrer and 24.03 Barrer, respectively, and the ideal selectivities of CO2/CH4 and O2/N2 were 15.67 and 3.00, respectively. Molecular simulations further verified that adding HCPs was beneficial to gas transport. Thus, HCPs have potential utility in fabrication of MMMs for facilitating gas transport in the fields of natural gas purification and O2 enrichment.

In recent years, there have been frequent water pollution emergencies, which seriously threaten the environment of water supply sources and affect the safety and quality of the water supply. These emergencies have aroused concern from the public and the government and highlight the necessity of plans for the emergency treatment of the affected water sources. In this paper, a sudden pollution of a river drinking water source is used as the research object. A mathematical simulation method was used to investigate the water quality near and downstream of a wharf in the state of a sudden oil spill. The wharf is located 1.34 km upstream of the water intake position of this river water source. Based on our investigation, we have established a risk assessment method for an oil spill emergency pollution event. Our aim was to provide a basis for the assessment of the water intake quality and water safety status of a river-based water plant and to provide technical support for developing an effective treatment plan in the event of a sudden environmental pollution accident.

Background: Traumatic brain injury-induced coagulopathy (TBI-IC), is a disease with poor prognosis and increased mortality rate.Objectives: Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of coagulopathy in this population.Methods: ML models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Candidate predictors, including demographics, family history, comorbidities, vital signs, laboratory findings, injury type, therapy strategy and scoring system were included. Models were compared on area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis (DCA) curve.Results: Of 999 patients in MIMIC-IV included in the final cohort, a total of 493 (49.35%) patients developed coagulopathy following TBI. Recursive feature elimination (RFE) selected 15 variables, including international normalized ratio (INR), prothrombin time (PT), sepsis related organ failure assessment (SOFA), activated partial thromboplastin time (APTT), platelet (PLT), hematocrit (HCT), red blood cell (RBC), hemoglobin (HGB), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), creatinine (CRE), congestive heart failure, myocardial infarction, sodium, and blood transfusion. The external validation in eICU-CRD demonstrated that adapting boosting (Ada) model had the highest AUC of 0.924 (95% CI: 0.902–0.943). Furthermore, in the DCA curve, the Ada model and the extreme Gradient Boosting (XGB) model had relatively higher net benefits (ie, the correct classification of coagulopathy considering a trade-off between false- negatives and false-positives)—over other models across a range of threshold probability values.Conclusions: The ML models, as indicated by our study, can be used to predict the incidence of TBI-IC in the intensive care unit (ICU).

The Taipu River is an important cross-border river in the Yangtze River Delta region and a direct channel connecting Taihu Lake and Huangpu River. Along the main stream of the Taipu River are many sources of water, such as the Wujiang, Dingzha, and Liantang Rivers. Many boats traverse these rivers transporting a wide variety of goods, including large quantities of oil, chemicals, and other dangerous goods. In the event of accidents on these vessels, spilled cargo will directly threaten the drinking water safety of people in the region. Aiming at simulating and assessing the environmental risks of sudden oil spills in rivers in the Yangtze River Delta region, this paper established a two-dimensional oil spill model of the typical transboundary Taipu River based on the MIKE21 water environment numerical simulation software developed by the Danish Institute of Water Conservancy. The established model will improve emergency response and treatment plans as well as our understanding of river oil spill progressions.