Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learni...Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.展开更多
Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasi...Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage resources.Moreover,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity relationships.To address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial relationships.The spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel dimension.We conduct quantitative and qualitative experiments on two different commonly used RSCIR datasets.Compared with traditional methods,our approach achieves an improvement of 3%-11% in sumR metric.Compared with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.展开更多
During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 202...During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making ...Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.展开更多
Bocapavovirus,a member of the genus Bocaparvovirus within the subfamily Parvovirinae and the family Parvoviridae,is a small,non-enveloped,single-stranded DNA virus.This pathogen poses health risks to both humans and a...Bocapavovirus,a member of the genus Bocaparvovirus within the subfamily Parvovirinae and the family Parvoviridae,is a small,non-enveloped,single-stranded DNA virus.This pathogen poses health risks to both humans and animals.The Bocaparvovirus genome.展开更多
This paper presents the morphologic,chemical and other typomorphic characteristics of native gold from four placer deposits(basins of the Lev.Nora,Skalistaya and Golysheva rivers,and Loginova brook),four placer occurr...This paper presents the morphologic,chemical and other typomorphic characteristics of native gold from four placer deposits(basins of the Lev.Nora,Skalistaya and Golysheva rivers,and Loginova brook),four placer occurrences(basins of the Lagernaya,Nizh.Litke and Prokhodimaya rivers,and Tikhiy brook),and the alluvial deposit of cape Mordovin on Bolshevik island of the Severnaya Zemlya archipelago(Russia).Optical microscopy,scanning electron microscopy and electron-probe microanalysis were used in this study.Placer gold from the Lagernaya,Golysheva,Nizh.Litke and Skalistaya rivers,Tikhiy brook and cape Mordovin is characterized by a very high fineness(>988‰)in the rims and a lower fineness(860‰–970‰)in the center.Gold particles from the placers of the Lev.Nora and Prokhodimaya rivers and Loginova brook are low fineness and widely vary in the center(from 647‰to 920‰)and are high fineness(950‰–980‰)in the rims.In some gold particles from the placers of the Lev.Nora and Skalistaya rivers,zones with Cu up to 1.2 wt.%and Hg up to 2.6 wt.%are observed.Titanite,monazite,cobaltite,ulmannite,brannerite,rutile,zircon,Y-xenotime,bismuthite,native bismuth and bismuthinite,garnet(almandine),Cu-or Ni-pyrrhotite were found in the native gold from the Skalistaya and Lev.Nora placers.Native gold from the Skalistaya river placer contains mineral micro-inclusions of cobaltite,Cu,Cd-bearing sphalerite and Fe,Cu-ullmannite.Native gold from the Lev.Nora river placer differs in the presence of brannerite and bismuth minerals.On the basis of the obtained results,available metallogenic characteristics of Bolshevik island and literature data,the following types of primary sources are predicted for these locations:(1)Lev.Nora river deposits of gold-copper rare metal and porphyry gold-copper formations;(2)Skalistaya river deposits of porphyry gold-copper and gold-quartz formation;(3)all the other locations:deposits of gold-quartz and gold-sulfide-quartz formations(hosted in terrigenous carbonaceous complexes).The presence of intermediate reservoirs near some of these locations is probable.展开更多
During the intense debates between fixism and mobilism,Chinese geologist J.S.Lee published“The Fundamental Cause of Evolution of the Earth’s Surface Features”in 1926 in the Bulletin of the Geological Society of Chi...During the intense debates between fixism and mobilism,Chinese geologist J.S.Lee published“The Fundamental Cause of Evolution of the Earth’s Surface Features”in 1926 in the Bulletin of the Geological Society of China,supporting mobilism.He attributed continental movements to the variations of the Earth’s rotation speed:when the rotation speed increases,it generates forces that compel the continents and seawater to move horizontally from the poles toward the equator and vice versa.It was on this idea that his Geomechanics was established in the following decades.展开更多
Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included p...Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.展开更多
The study by Luo et al published in the World Journal of Gastrointestinal Oncology presents a thorough and scientific methodology.Pancreatic cancer is the most challenging malignancy in the digestive system,exhibiting...The study by Luo et al published in the World Journal of Gastrointestinal Oncology presents a thorough and scientific methodology.Pancreatic cancer is the most challenging malignancy in the digestive system,exhibiting one of the highest mortality rates associated with cancer globally.The delayed onset of symptoms and diagnosis often results in metastasis or local progression of the cancer,thereby constraining treatment options and outcomes.For these patients,prompt tumour identification and treatment strategising are crucial.The present objective of pancreatic cancer research is to examine the correlation between various pathological types and imaging data to facilitate therapeutic decision-making.This study aims to clarify the correlation between diverse pathological markers and imaging in pancreatic cancer patients,with prospective longitudinal studies potentially providing novel insights into the diagnosis and treatment of pancreatic cancer.展开更多
BACKGROUND Colorectal cancer(CRC)is a malignant tumor with high morbidity and mortality rates worldwide.With the development of medical imaging technology,imaging features are playing an increasingly important role in...BACKGROUND Colorectal cancer(CRC)is a malignant tumor with high morbidity and mortality rates worldwide.With the development of medical imaging technology,imaging features are playing an increasingly important role in the prognostic evaluation of CRC.Laparoscopic radical resection is a common surgical approach for treating CRC.However,research on the link between preoperative imaging and short-term prognosis in this context is limited.We hypothesized that specific preope-rative imaging features can predict the short-term prognosis in patients under-going laparoscopic CRC resection.AIM To investigate the imaging features of CRC and analyze their correlation with the short-term prognosis of laparoscopic radical resection.METHODS This retrospective study conducted at the Affiliated Cancer Hospital of Shandong First Medical University included 122 patients diagnosed with CRC who under-went laparoscopic radical resection between January 2021 and February 2024.All patients underwent magnetic resonance imaging(MRI)and were diagnosed with CRC through pathological examination.MRI data and prognostic indicators were collected 30 days post-surgery.Logistic regression analysis identified imaging fea-tures linked to short-term prognosis,and a receiver operating characteristic(ROC)curve was used to evaluate the predictive value.RESULTS Among 122 patients,22 had irregular,low-intensity tumors with adjacent high signals.In 55,tumors were surrounded by alternating signals in the muscle layer.In 32,tumors extended through the muscular layer and blurred boundaries with perienteric adipose tissue.Tumor signals appeared in the adjacent tissues in 13 patients with blurred gaps.Logistic regression revealed differences in longitudinal tumor length,axial tumor length,volume transfer constant,plasma volume fraction,and apparent diffusion coefficient among patients with varying prognostic results.ROC analysis indicated that the areas under the curve for these parameters were 0.648,0.927,0.821,0.809,and 0.831,respectively.Sensitivity values were 0.643,0.893,0.607,0.714,and 0.714,and specificity 0.702,0.904,0.883,0.968,and 0.894(P<0.05).CONCLUSION The imaging features of CRC correlate with the short-term prognosis following laparoscopic radical resection.These findings provide valuable insights for clinical decision-making.展开更多
BACKGROUND Autoimmune gastritis(AIG)is frequently associated with one or more comorbid conditions,among which type I gastric neuroendocrine tumors(gNETs)warrant significant clinical concern.However,risk factors for th...BACKGROUND Autoimmune gastritis(AIG)is frequently associated with one or more comorbid conditions,among which type I gastric neuroendocrine tumors(gNETs)warrant significant clinical concern.However,risk factors for the development of gNETs in AIG populations remain poorly defined.AIM To characterize the clinical and endoscopic profiles of AIG and identify potential risk factors for gNETs development.METHODS In this single-center cross-sectional study carried out at a tertiary hospital,303 patients with AIG over an 8-year period were retrospectively categorized into gNETs(n=116)and non-gNETs(n=187)groups.Endoscopic and clinical pa-rameters were analyzed.Endoscopic features were systematically reevaluated according to the 2023 Japanese diagnostic criteria for AIG.Feature selection was performed using the Boruta algorithm,and the model discriminative ability was evaluated via receiver operating characteristic curve analysis.RESULTS Among the 303 patients with AIG,116 had gNETs and 187 did not.Compared with the non-gNETs group,patients in the gNETs group were younger(54.3 years vs 60.6 years,P<0.001),had higher rate of vitamin B12 deficiency(77.2%vs 55.8%,P<0.001),lower pepsinogen I(4.3 ng/mL vs 7.4 ng/mL,P<0.001)and pepsinogen I/II ratios(0.7 vs.1.1,P<0.001),and lower prior Helicobacter pylori infection rate(3.4%vs 21.4%,P<0.001).Endoscopically,the gNETs group showed a lower incidence of oxyntic mucosal remnants,hyperplastic polyps,and patchy antral redness.The predictive model incorporating age,prior Helicobacter pylori infection,vitamin B12 level,gastric hy-perplastic polyps,and patchy antral redness showed an area under the curve of 0.830.CONCLUSION Patients with AIG or gNETs exhibit specific clinical and endoscopic features.The predictive model demonstrated favorable discriminative ability and may facilitate risk stratification of gNETs in patients with AIG.展开更多
This study aims to examine the factors influencing the use of public parks among the elderly in Bangkok,Thai-land,amidst shifting demographics.As of mid-2024,seniors aged 60 and older accounted for 20.70%of Thailand’...This study aims to examine the factors influencing the use of public parks among the elderly in Bangkok,Thai-land,amidst shifting demographics.As of mid-2024,seniors aged 60 and older accounted for 20.70%of Thailand’s total population.With an annual growth rate of 4.89%,the country is steadily advancing toward becoming a super-aged society.The elderly population increasingly seeks not only senior-friendly housing but also accessible and inclusive public parks or spaces.Parks play a vital role in promoting the health,well-being,and social engagement of elderly individuals.This research explores the relationship between public park use and various independent variables,including public use behavior characteristics and factors associated with the use of public spaces.These factors are categorized into ac-cessibility attributes,diversity attributes,seating arrangement attributes,temperature and weather attributes,aesthetic attributes,safety attributes,and social interaction attributes.Data were collected from 299 respondents,who were asked to rate 25 questions on a 5-point Likert scale,addressing aspects related to their preferences and choices for using public parks.A binary logistic regression analysis was employed to identify the factors impacting elderly individuals’use of public parks in Bangkok.The results indicate that public park use behavior characteristics,along with accessibility,diversity,seating arrangements,aesthetics,safety,and social interaction attributes,significantly influence the use of public parks by elderly individuals.These findings provide valuable insights for public policymakers and park designers,offering recommendations on how to design and develop public parks that better cater to the needs of Bangkok’s aging popu-lation.展开更多
BACKGROUND Colorectal cancer(CRC)is one of the most common malignant gastrointestinal tumors worldwide,with high incidence and mortality rates.AIM To investigate the expression significance of the chromatin-remodeling...BACKGROUND Colorectal cancer(CRC)is one of the most common malignant gastrointestinal tumors worldwide,with high incidence and mortality rates.AIM To investigate the expression significance of the chromatin-remodeling protein MORC family CW-type zinc finger 4(MORC4)as a biomarker in CRC patients,and to explore its relationship with pathological features and prognosis.METHODS A total of 143 CRC specimens and 57 adjacent tissue specimens,surgically removed from our hospital between January 2020 and January 2021,were collected.MORC4 protein expression was assessed using immunohistochemistry after paraffin embedding.The relationship between MORC4 protein expression and clinicopathological characteristics of patients was analyzed.Kaplan-Meier survival curves were plotted to analyze the relationship between MORC4 protein expression and prognosis in CRC patients.RESULTS Compared with adjacent tissues,the expression rate of MORC4 protein in CRC tissues was significantly higher(P<0.05).No significant difference was observed in the high expression rate of MORC4 protein in CRC tissues among patients of different gender,age,tumor location,tumor diameter,and primary tumor status(P>0.05).However,significant differences were found in the high expression rate of MORC4 protein in patients with different degrees of differentiation,lymph node metastasis,distant metastasis,tumor-lymph node-metastasis stage,and serum carcinoembryonic antigen levels(P<0.05).Compared with patients with low MORC4 expression,patients with high MORC4 expression had a worse prognosis(P<0.05).CONCLUSION The upregulation of MORC4 expression in CRC patients is closely related to disease severity and prognosis,suggesting its potential as an evaluation biomarker,which warrants further investigation.展开更多
Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic information.However,traditional models still rely on static visual features that do ...Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic information.However,traditional models still rely on static visual features that do not evolve with the changing linguistic context,which can hinder the ability to form meaningful connections between the image and the generated captions.This limitation often leads to captions that are less accurate or descriptive.In this paper,we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context.Our model strengthens the alignment between visual and linguistic elements,resulting in more coherent and contextually appropriate captions.Specifically,we introduce two innovative modules:the Visual Weighting Module(VWM)and the Enhanced Features Attention Module(EFAM).The VWM adjusts visual features using partial attention,enabling dynamic reweighting of the visual inputs,while the EFAM further refines these features to improve their relevance to the generated caption.By continuously adjusting visual features in response to the linguistic context,our model bridges the gap between static visual features and dynamic language generation.We demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset,where our method outperforms state-of-the-art techniques in terms of caption quality and contextual relevance.Our results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.展开更多
Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-...Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.展开更多
One reference in the original manuscript contained incorrect bibliographic information and cited a non-existent publication:Traczyk A(1999)Pleistocene debris cover beds and block-debris tongues in the north-western pa...One reference in the original manuscript contained incorrect bibliographic information and cited a non-existent publication:Traczyk A(1999)Pleistocene debris cover beds and block-debris tongues in the north-western part of theŚlęża Massif(Poland)and their formation under permafrost conditions.Geographia Polonica 81(1).This erroneous reference has now been removed from the references list.展开更多
Hereditary angioedema (HAE) is a rare,autosomal dominant inherited disorder with an incidence of approximately 1 in 50,000.Among its various tapes,HAE with normal C1 inhibitor levels (HAE-nC1-INH)is exceptionally rare...Hereditary angioedema (HAE) is a rare,autosomal dominant inherited disorder with an incidence of approximately 1 in 50,000.Among its various tapes,HAE with normal C1 inhibitor levels (HAE-nC1-INH)is exceptionally rare.^([1]) HAE symptoms include recurrent episodes of skin and mucosal edema that can occur anywhere in the body.^([1-4]) Laryngeal edema is life-threatening,as it can lead to airway obstruction and potentially fatal suffocation.^([1-3])Edema of the gastrointestinal mucosa may cause abdominal pain,vomiting,and symptoms that are often misdiagnosed as acute abdomen.^([1-4]) This study included four patients,including one with HAE-nC1-INH (genetic testing revealed a heterozygous mutation in the KNG1 gene (c.1404G>C:p.Q468H)) and three with HAE due to C1 inhibitor deficiency (HAE-C1-INH).This case series aims to increase knowledge of HAE by illustrating its diverse clinical presentations and emphasizing features that may prompt clinical suspicion and facilitate timely diagnosis.展开更多
文摘Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.
基金supported by the National Key R&D Program of China(No.2022ZD0118402)。
文摘Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention recently.However,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage resources.Moreover,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity relationships.To address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial relationships.The spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel dimension.We conduct quantitative and qualitative experiments on two different commonly used RSCIR datasets.Compared with traditional methods,our approach achieves an improvement of 3%-11% in sumR metric.Compared with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.
文摘During Donald Trump’s first term,the“Trump Shock”brought world politics into an era of uncertainties and pulled the transatlantic alliance down to its lowest point in history.The Trump 2.0 tsunami brewed by the 2024 presidential election of the United States has plunged the U.S.-Europe relations into more gloomy waters,ushering in a more complex and turbulent period of adjustment.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
文摘Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.
基金supported by the Natural Science Foundation of Sichuan Province,China(2024NSFSC1272)the Innovation Team Development Funds for Sichuan Mutton Goat&Sheep,China(SCCXTD-2024-14)Scientific and Technological Innovation Team for Qinghai-Tibetan Plateau Research in Southwest Minzu University,China(2024CXTD08)。
文摘Bocapavovirus,a member of the genus Bocaparvovirus within the subfamily Parvovirinae and the family Parvoviridae,is a small,non-enveloped,single-stranded DNA virus.This pathogen poses health risks to both humans and animals.The Bocaparvovirus genome.
基金The work was carried out within the framework the State assignment of the Federal Agency for Subsoil Use of 27.12.2023№049-00003-24-00a State Assignment of the Sobolev Institute of Geology and Mineralogy,Russian Academy of Sciences(project no.122041400237-8).
文摘This paper presents the morphologic,chemical and other typomorphic characteristics of native gold from four placer deposits(basins of the Lev.Nora,Skalistaya and Golysheva rivers,and Loginova brook),four placer occurrences(basins of the Lagernaya,Nizh.Litke and Prokhodimaya rivers,and Tikhiy brook),and the alluvial deposit of cape Mordovin on Bolshevik island of the Severnaya Zemlya archipelago(Russia).Optical microscopy,scanning electron microscopy and electron-probe microanalysis were used in this study.Placer gold from the Lagernaya,Golysheva,Nizh.Litke and Skalistaya rivers,Tikhiy brook and cape Mordovin is characterized by a very high fineness(>988‰)in the rims and a lower fineness(860‰–970‰)in the center.Gold particles from the placers of the Lev.Nora and Prokhodimaya rivers and Loginova brook are low fineness and widely vary in the center(from 647‰to 920‰)and are high fineness(950‰–980‰)in the rims.In some gold particles from the placers of the Lev.Nora and Skalistaya rivers,zones with Cu up to 1.2 wt.%and Hg up to 2.6 wt.%are observed.Titanite,monazite,cobaltite,ulmannite,brannerite,rutile,zircon,Y-xenotime,bismuthite,native bismuth and bismuthinite,garnet(almandine),Cu-or Ni-pyrrhotite were found in the native gold from the Skalistaya and Lev.Nora placers.Native gold from the Skalistaya river placer contains mineral micro-inclusions of cobaltite,Cu,Cd-bearing sphalerite and Fe,Cu-ullmannite.Native gold from the Lev.Nora river placer differs in the presence of brannerite and bismuth minerals.On the basis of the obtained results,available metallogenic characteristics of Bolshevik island and literature data,the following types of primary sources are predicted for these locations:(1)Lev.Nora river deposits of gold-copper rare metal and porphyry gold-copper formations;(2)Skalistaya river deposits of porphyry gold-copper and gold-quartz formation;(3)all the other locations:deposits of gold-quartz and gold-sulfide-quartz formations(hosted in terrigenous carbonaceous complexes).The presence of intermediate reservoirs near some of these locations is probable.
文摘During the intense debates between fixism and mobilism,Chinese geologist J.S.Lee published“The Fundamental Cause of Evolution of the Earth’s Surface Features”in 1926 in the Bulletin of the Geological Society of China,supporting mobilism.He attributed continental movements to the variations of the Earth’s rotation speed:when the rotation speed increases,it generates forces that compel the continents and seawater to move horizontally from the poles toward the equator and vice versa.It was on this idea that his Geomechanics was established in the following decades.
基金National Natural Science Foundation of China(82305090)Shanghai Municipal Health Commission(20234Y0168)National Key Research and Development Program of China (2017YFC1703301)。
文摘Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.
基金Supported by the National Health Commission’s Key Laboratory of Gastrointestinal Tumor Diagnosis and Treatment for The Year 2022,National Health Commission’s Master’s and Doctoral/Postdoctoral Fund Project,No.NHCDP2022001Gansu Provincial People’s Hospital Doctoral Supervisor Training Project,No.22GSSYA-3.
文摘The study by Luo et al published in the World Journal of Gastrointestinal Oncology presents a thorough and scientific methodology.Pancreatic cancer is the most challenging malignancy in the digestive system,exhibiting one of the highest mortality rates associated with cancer globally.The delayed onset of symptoms and diagnosis often results in metastasis or local progression of the cancer,thereby constraining treatment options and outcomes.For these patients,prompt tumour identification and treatment strategising are crucial.The present objective of pancreatic cancer research is to examine the correlation between various pathological types and imaging data to facilitate therapeutic decision-making.This study aims to clarify the correlation between diverse pathological markers and imaging in pancreatic cancer patients,with prospective longitudinal studies potentially providing novel insights into the diagnosis and treatment of pancreatic cancer.
文摘BACKGROUND Colorectal cancer(CRC)is a malignant tumor with high morbidity and mortality rates worldwide.With the development of medical imaging technology,imaging features are playing an increasingly important role in the prognostic evaluation of CRC.Laparoscopic radical resection is a common surgical approach for treating CRC.However,research on the link between preoperative imaging and short-term prognosis in this context is limited.We hypothesized that specific preope-rative imaging features can predict the short-term prognosis in patients under-going laparoscopic CRC resection.AIM To investigate the imaging features of CRC and analyze their correlation with the short-term prognosis of laparoscopic radical resection.METHODS This retrospective study conducted at the Affiliated Cancer Hospital of Shandong First Medical University included 122 patients diagnosed with CRC who under-went laparoscopic radical resection between January 2021 and February 2024.All patients underwent magnetic resonance imaging(MRI)and were diagnosed with CRC through pathological examination.MRI data and prognostic indicators were collected 30 days post-surgery.Logistic regression analysis identified imaging fea-tures linked to short-term prognosis,and a receiver operating characteristic(ROC)curve was used to evaluate the predictive value.RESULTS Among 122 patients,22 had irregular,low-intensity tumors with adjacent high signals.In 55,tumors were surrounded by alternating signals in the muscle layer.In 32,tumors extended through the muscular layer and blurred boundaries with perienteric adipose tissue.Tumor signals appeared in the adjacent tissues in 13 patients with blurred gaps.Logistic regression revealed differences in longitudinal tumor length,axial tumor length,volume transfer constant,plasma volume fraction,and apparent diffusion coefficient among patients with varying prognostic results.ROC analysis indicated that the areas under the curve for these parameters were 0.648,0.927,0.821,0.809,and 0.831,respectively.Sensitivity values were 0.643,0.893,0.607,0.714,and 0.714,and specificity 0.702,0.904,0.883,0.968,and 0.894(P<0.05).CONCLUSION The imaging features of CRC correlate with the short-term prognosis following laparoscopic radical resection.These findings provide valuable insights for clinical decision-making.
文摘BACKGROUND Autoimmune gastritis(AIG)is frequently associated with one or more comorbid conditions,among which type I gastric neuroendocrine tumors(gNETs)warrant significant clinical concern.However,risk factors for the development of gNETs in AIG populations remain poorly defined.AIM To characterize the clinical and endoscopic profiles of AIG and identify potential risk factors for gNETs development.METHODS In this single-center cross-sectional study carried out at a tertiary hospital,303 patients with AIG over an 8-year period were retrospectively categorized into gNETs(n=116)and non-gNETs(n=187)groups.Endoscopic and clinical pa-rameters were analyzed.Endoscopic features were systematically reevaluated according to the 2023 Japanese diagnostic criteria for AIG.Feature selection was performed using the Boruta algorithm,and the model discriminative ability was evaluated via receiver operating characteristic curve analysis.RESULTS Among the 303 patients with AIG,116 had gNETs and 187 did not.Compared with the non-gNETs group,patients in the gNETs group were younger(54.3 years vs 60.6 years,P<0.001),had higher rate of vitamin B12 deficiency(77.2%vs 55.8%,P<0.001),lower pepsinogen I(4.3 ng/mL vs 7.4 ng/mL,P<0.001)and pepsinogen I/II ratios(0.7 vs.1.1,P<0.001),and lower prior Helicobacter pylori infection rate(3.4%vs 21.4%,P<0.001).Endoscopically,the gNETs group showed a lower incidence of oxyntic mucosal remnants,hyperplastic polyps,and patchy antral redness.The predictive model incorporating age,prior Helicobacter pylori infection,vitamin B12 level,gastric hy-perplastic polyps,and patchy antral redness showed an area under the curve of 0.830.CONCLUSION Patients with AIG or gNETs exhibit specific clinical and endoscopic features.The predictive model demonstrated favorable discriminative ability and may facilitate risk stratification of gNETs in patients with AIG.
文摘This study aims to examine the factors influencing the use of public parks among the elderly in Bangkok,Thai-land,amidst shifting demographics.As of mid-2024,seniors aged 60 and older accounted for 20.70%of Thailand’s total population.With an annual growth rate of 4.89%,the country is steadily advancing toward becoming a super-aged society.The elderly population increasingly seeks not only senior-friendly housing but also accessible and inclusive public parks or spaces.Parks play a vital role in promoting the health,well-being,and social engagement of elderly individuals.This research explores the relationship between public park use and various independent variables,including public use behavior characteristics and factors associated with the use of public spaces.These factors are categorized into ac-cessibility attributes,diversity attributes,seating arrangement attributes,temperature and weather attributes,aesthetic attributes,safety attributes,and social interaction attributes.Data were collected from 299 respondents,who were asked to rate 25 questions on a 5-point Likert scale,addressing aspects related to their preferences and choices for using public parks.A binary logistic regression analysis was employed to identify the factors impacting elderly individuals’use of public parks in Bangkok.The results indicate that public park use behavior characteristics,along with accessibility,diversity,seating arrangements,aesthetics,safety,and social interaction attributes,significantly influence the use of public parks by elderly individuals.These findings provide valuable insights for public policymakers and park designers,offering recommendations on how to design and develop public parks that better cater to the needs of Bangkok’s aging popu-lation.
基金was approved by the Ethics Committee of Cangzhou Central Hospital,No.29795793.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most common malignant gastrointestinal tumors worldwide,with high incidence and mortality rates.AIM To investigate the expression significance of the chromatin-remodeling protein MORC family CW-type zinc finger 4(MORC4)as a biomarker in CRC patients,and to explore its relationship with pathological features and prognosis.METHODS A total of 143 CRC specimens and 57 adjacent tissue specimens,surgically removed from our hospital between January 2020 and January 2021,were collected.MORC4 protein expression was assessed using immunohistochemistry after paraffin embedding.The relationship between MORC4 protein expression and clinicopathological characteristics of patients was analyzed.Kaplan-Meier survival curves were plotted to analyze the relationship between MORC4 protein expression and prognosis in CRC patients.RESULTS Compared with adjacent tissues,the expression rate of MORC4 protein in CRC tissues was significantly higher(P<0.05).No significant difference was observed in the high expression rate of MORC4 protein in CRC tissues among patients of different gender,age,tumor location,tumor diameter,and primary tumor status(P>0.05).However,significant differences were found in the high expression rate of MORC4 protein in patients with different degrees of differentiation,lymph node metastasis,distant metastasis,tumor-lymph node-metastasis stage,and serum carcinoembryonic antigen levels(P<0.05).Compared with patients with low MORC4 expression,patients with high MORC4 expression had a worse prognosis(P<0.05).CONCLUSION The upregulation of MORC4 expression in CRC patients is closely related to disease severity and prognosis,suggesting its potential as an evaluation biomarker,which warrants further investigation.
基金supported by the National Natural Science Foundation of China(Nos.U22A2034,62177047)High Caliber Foreign Experts Introduction Plan funded by MOST,and Central South University Research Programme of Advanced Interdisciplinary Studies(No.2023QYJC020).
文摘Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic information.However,traditional models still rely on static visual features that do not evolve with the changing linguistic context,which can hinder the ability to form meaningful connections between the image and the generated captions.This limitation often leads to captions that are less accurate or descriptive.In this paper,we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context.Our model strengthens the alignment between visual and linguistic elements,resulting in more coherent and contextually appropriate captions.Specifically,we introduce two innovative modules:the Visual Weighting Module(VWM)and the Enhanced Features Attention Module(EFAM).The VWM adjusts visual features using partial attention,enabling dynamic reweighting of the visual inputs,while the EFAM further refines these features to improve their relevance to the generated caption.By continuously adjusting visual features in response to the linguistic context,our model bridges the gap between static visual features and dynamic language generation.We demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset,where our method outperforms state-of-the-art techniques in terms of caption quality and contextual relevance.Our results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.
基金The National Key Research and Development Program of China under contract No.2023YFC3008204the National Natural Science Foundation of China under contract Nos 41977302 and 42476217.
文摘Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.
文摘One reference in the original manuscript contained incorrect bibliographic information and cited a non-existent publication:Traczyk A(1999)Pleistocene debris cover beds and block-debris tongues in the north-western part of theŚlęża Massif(Poland)and their formation under permafrost conditions.Geographia Polonica 81(1).This erroneous reference has now been removed from the references list.
基金supported by the National Social Science Fund of China (19VJX168)。
文摘Hereditary angioedema (HAE) is a rare,autosomal dominant inherited disorder with an incidence of approximately 1 in 50,000.Among its various tapes,HAE with normal C1 inhibitor levels (HAE-nC1-INH)is exceptionally rare.^([1]) HAE symptoms include recurrent episodes of skin and mucosal edema that can occur anywhere in the body.^([1-4]) Laryngeal edema is life-threatening,as it can lead to airway obstruction and potentially fatal suffocation.^([1-3])Edema of the gastrointestinal mucosa may cause abdominal pain,vomiting,and symptoms that are often misdiagnosed as acute abdomen.^([1-4]) This study included four patients,including one with HAE-nC1-INH (genetic testing revealed a heterozygous mutation in the KNG1 gene (c.1404G>C:p.Q468H)) and three with HAE due to C1 inhibitor deficiency (HAE-C1-INH).This case series aims to increase knowledge of HAE by illustrating its diverse clinical presentations and emphasizing features that may prompt clinical suspicion and facilitate timely diagnosis.
