Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physica...Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physical properties can provide useful information on their origin,evolution,and hazard to human beings.However,it remains challenging to investigate small,newly discovered,near-Earth objects because of our limited observational window.This investigation seeks to determine the visible colors of near-Earth asteroids(NEAs),perform an initial taxonomic classification based on visible colors and analyze possible correlations between the distribution of taxonomic classification and asteroid size or orbital parameters.Observations were performed in the broadband BVRI Johnson−Cousins photometric system,applied to images from the Yaoan High Precision Telescope and the 1.88 m telescope at the Kottamia Astronomical Observatory.We present new photometric observations of 84 near-Earth asteroids,and classify 80 of them taxonomically,based on their photometric colors.We find that nearly half(46.3%)of the objects in our sample can be classified as S-complex,26.3%as C-complex,6%as D-complex,and 15.0%as X-complex;the remaining belong to the A-or V-types.Additionally,we identify three P-type NEAs in our sample,according to the Tholen scheme.The fractional abundances of the C/X-complex members with absolute magnitude H≥17.0 were more than twice as large as those with H<17.0.However,the fractions of C-and S-complex members with diameters≤1 km and>1 km are nearly equal,while X-complex members tend to have sub-kilometer diameters.In our sample,the C/D-complex objects are predominant among those with a Jovian Tisserand parameter of T_(J)<3.1.These bodies could have a cometary origin.C-and S-complex members account for a considerable proportion of the asteroids that are potentially hazardous.展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
AIM:To propose a new endoscopic classification of achalasia for selecting patients appropriate for undergoing peroral endoscopic myotomy(POEM).METHODS:We screened out the data of patients with achalasia examined from ...AIM:To propose a new endoscopic classification of achalasia for selecting patients appropriate for undergoing peroral endoscopic myotomy(POEM).METHODS:We screened out the data of patients with achalasia examined from October 2000 to September 2011 at our Digestive Endoscopic Center with endoscopic pictures clear enough to reveal the morphology of middle and lower esophagus.After analyzing the correlation between the endoscopic morphology of the esophageal lumen and POEM,we proposed a new endoscopic classification(Ling classification) of achalasia according to three kinds of endoscopically viewed structures:multi-ring structure,crescent-like structure and diverticulum structure.There were three types based on the criteria of Ling classification:type Ⅰ,smooth without multi-ring,crescent-like structure or diverticulum structure;type Ⅱ,with multi-ring or crescent-like structure but without diverticulum structure;and type Ⅲ,with diverticulum structure.Type Ⅱ was classified into three subtypes:Ling Ⅱa,Ling Ⅱb and Ling Ⅱc;and type Ⅲ also had three subtypes:Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr.Two endoscopists made a final decision upon mutual agreement through discussion if their separately recorded characteristics were different.RESULTS:Among the 976 screened patients with achalasia,636 patients with qualified endoscopic pictures were selected for the analysis,including 405 males and 231 females.The average age was 42.7 years,ranging from 6 to 93 years.Type Ⅰ was the most commonly observed type of achalasia,accounting for 64.5%(410/636),and type Ⅲ was the least commonly observed type of achalasia,accounting for 2.8%(18/636).And type Ⅱ accounted for 32.7%(208/636) and subtype of Ling Ⅱa,Ling Ⅱb and Ling Ⅱc accounted for 14.6%(93/636),9.9%(63/636) and 8.2%(52/636),respectively.And subtype of Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr accounted for 0.8%(5/636),0.3%(2/636) and 1.7%(11/636),respectively.CONCLUSION:A new endoscopic classification of achalasia is proposed that might help in determining the proper candidates for POEM.展开更多
BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the para...BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.展开更多
Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interd...Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.展开更多
Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical sign...Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.展开更多
The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring ef...The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Background: Vulvovaginal candidiasis (VVC) is a common cause of significant morbidity, affecting millions of women worldwide. It is estimated that approximately 75%of women of childbearing age will have at least one e...Background: Vulvovaginal candidiasis (VVC) is a common cause of significant morbidity, affecting millions of women worldwide. It is estimated that approximately 75%of women of childbearing age will have at least one episode of candidiasis in their lifetime. In the last decades, resistance to azoles has become a public health problem. Although studies on vulvovaginitis have been done, there is lack of VVC studies in our area. The aim of this study was to describe the etiological and resistance profiles of vulvovaginal candidiasis to standard antifungus at the Saint Camille Hospital of Ouagadougou (HOSCO), Burkina Faso. Methods: We conducted a prospective study from January 2018 to December 2022. From vulvovaginal swabs, Candida species were identified using the ChromID® Candida Agar medium and the API® Candida gallery. Antifungal susceptibility testing was performed using Kirby-Bauer agar disk diffusion. Results: A total of 4789 women were sampled. The average age of sexually active women was 27.80+/−6.77 years, with extremes ranging from 15 to 64 years. Vaginal Candida infections accounted for 74.16% of the cases. The 20 - 29 age group was the most affected by vulvovaginal candidiasis. Pregnant women accounted for 28.76% of our study population. Women in the second (2nd) trimester of pregnancy had more Candida infections. Candida albicans was the most isolated species (55.12%), followed by Candida glabrata (27.64%), Candida tropicalis (6.91%), Candida famata (6.67%), Candida krusei (2.56%). All the Candida species isolated showed very high of resistance to Fluconazole (45.2%), Miconazole (23.7%) and Clotrimazole (45.7%). Conclusion: Species-specific antifungal results should always be considered to avoid antifungal resistance associated with vulvovaginal candidiasis. Identifying the causative species using vaginal fungal cultures can help guide therapy and improve outcomes for these patients.展开更多
With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming a...With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming and may not guide immediate clinical decisions.This paper aims to introduce a novel classification system for GPs based on their potential risk of malignant transformation,categorizing them as"good","bad",and"ugly".A review of the literature and clinical case analysis were conducted to explore the clinical implications,management strategies,and the system's application in endoscopic practice.Good polyps,mainly including fundic gland polyps and inflammatory fibrous polyps,have a low risk of malignancy and typically require minimal or no intervention.Bad polyps,mainly including hyperplastic polyps and adenomas,pose an intermediate risk of malignancy,necessitating closer monitoring or removal.Ugly polyps,mainly including type 3 neuroendocrine tumors and early gastric cancer,indicate a high potential for malignancy and require urgent and comprehensive treatment.The new classification system provides a simplified and practical framework for diagnosing and managing GPs,improving diagnostic accuracy,guiding individualized treatment,and promoting advancements in endoscopic techniques.Despite some challenges,such as the risk of misclassification due to similar endoscopic appearances,this system is essential for the standardized management of GPs.It also lays the foundation for future research into biomarkers and the development of personalized medicine.展开更多
In Candida species,the endoplasmic reticulum(ER)stress response—regulated by the unfolded protein response(UPR)—serves as a critical adaptive mechanism affecting both pathogenicity and antifungal resistance.This rev...In Candida species,the endoplasmic reticulum(ER)stress response—regulated by the unfolded protein response(UPR)—serves as a critical adaptive mechanism affecting both pathogenicity and antifungal resistance.This review aims to synthesize current knowledge on ER stress pathways in Candida glabrata and Candida albicans,highlighting their species-specific adaptations and therapeutic implications.We systematically analyzed peer-reviewed literature on ER stress mechanisms in Candida,focusing on comparative studies of UPR signaling.Emphasis was placed on C.glabrata’s inositol-requiring enzyme 1(IRE1)-dependent Regulated IRE1-Dependent Decay(RIDD)pathway and C.albicans’IRE1/HAC1 and calcium-mediated pathways.Connections to virulence and drug resistance were evaluated through genetic,transcriptomic,and phenotypic evidence.Candida species employ divergent UPR strategies:C.glabrata mitigates ER stress primarily via RIDD-mediated mRNA decay to reduce protein load,while C.albicans enhances folding capacity through HAC1 splicing and calcium homeostasis.These adaptations promote survival in hostile host environments(e.g.,oxidative stress,immune attacks)and are linked to resistance against azoles and echinocandins.Pharmacological disruption of UPR components(e.g.,IRE1 inhibitors)sensitizes Candida to antifungals in experimental models.ER stress response pathways are promising targets for antifungal drug development.Understanding species-specific UPR mechanisms in Candida could guide novel therapies to overcome resistance and improve treatment outcomes.展开更多
There are various types of natural gas resources in coal measures,making them major targets for natural gas exploration and development in China.In view of the particularity of the whole petroleum system of coal measu...There are various types of natural gas resources in coal measures,making them major targets for natural gas exploration and development in China.In view of the particularity of the whole petroleum system of coal measures and the reservoir-forming evolution of natural gas in coal,this study reveals the formation,enrichment characteristics and distribution laws of coal-rock gas by systematically reviewing the main types and geological characteristics of natural gas in the whole petroleum system of coal measures.First,natural gas in the whole petroleum system of coal measures is divided into two types,conventional gas and unconventional gas,according to its occurrence characteristics and accumulation mechanism,and into six types,distal detrital rock gas,special rock gas,distal/proximal tight sandstone gas,inner-source tight sandstone gas,shale gas,and coal-rock gas,according to its source and reservoir lithology.The natural gas present in coal-rock reservoirs is collectively referred to as coal-rock gas.Existing data indicate significant differences in the geological characteristics of coal-rock gas exploration and development between shallow and deep layers in the same area,with the transition depth boundary generally 1500-2000 m.Based on the current understanding of coal-rock gas and respecting the historical usage conventions of coalbed methane terminology,coal-rock gas can be divided into deep coal-rock gas and shallow coalbed methane according to burial depth.Second,according to the research concept of“full-process reservoir formation”in the theory of the whole petroleum system of coal measures,based on the formation and evolution of typical coal-rock gas reservoirs,coal-rock gas is further divided into four types:primary coal-rock gas,regenerated coal-rock gas,residual coal-rock gas,and bio coal-rock gas.The first two belong to deep coal-rock gas,while the latter two belong to shallow coal-rock gas.Third,research on the coal-rock gas reservoir formation and evolution shows that shallow coal-rock gas is mainly residual coal-rock gas or bio coal-rock gas formed after geological transformation of primary coal-rock gas,with the reservoir characteristics such as low reservoir pressure,low gas saturation,adsorbed gas in dominance,and gas production by drainage and depressurization,while deep coal-rock gas is mainly primary coal-rock gas and regenerated coal-rock gas,with the reservoir characteristics such as high reservoir pressure,high gas saturation,abundant free gas,and no or little water.In particular,the primary coal-rock gas is wide in distribution,large in resource quantity,and good in reservoir quality,making it the most favorable type of coal-rock gas for exploration and development.展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
Objective To provide a theoretical basis for the adjustment of the registration classification of China’s biological products,and to establish a continuously improved registration classification system.Methods Based ...Objective To provide a theoretical basis for the adjustment of the registration classification of China’s biological products,and to establish a continuously improved registration classification system.Methods Based on literature research,the specific classification methods,classification principles and considerations of biological registration in China,the United States and the European Union were studied to form a complete comparative analysis.Results and Conclusion It is recommended that the division between therapeutic and preventive use should be removed from the registration classification of biologics.The therapeutic,preventive and diagnostic use of the product should be limited as part of the product specification,and the registration should be classified according to the development of biotechnology,innovation,modification and bio-similar drugs.In addition,the supervision of registration of advanced therapeutic products should be different from that of traditional biologics.展开更多
This study examined the EAG(Electroantennogram)responses of Stilpnotia candida to a mix of host plant volatiles and to provide a foundation for the development of plant-derived attractants.During the peak period of ad...This study examined the EAG(Electroantennogram)responses of Stilpnotia candida to a mix of host plant volatiles and to provide a foundation for the development of plant-derived attractants.During the peak period of adult eclosion,gas chromatography–mass spectrometry analyzed and identified the volatiles emitted by Populus×beijingensis found in Xizang.Based on the preliminary EAG experiments and the GC‒MS results,a blending scheme was developed.EAG and Y-tube-olfactometry were employed to measure the electrophysiological and behavioural responses of unmated males and females 24 h after eclosion 10 blends of volatiles derived from five host plants.The GC–MS analysis revealed 22 volatile compounds from Populus×beijingensis leaves,composed of esters,hydrocarbons,terpenoids,alcohols,phenols,and ether.The results indicated that all 10 blending schemes produced EAG responses in mature S.candida.The concentration thresholds were between 1 and 10μg·μL^(-1),above the optimal concentration,and a corresponding decrease in EAG was observed.According to intergroup comparisons,mature S.candida had more pronounced EAG responses.Under different concentrations,there were significant differences in the EAG from male and female S.candida to each blending scheme.Behavioural response tests indicated that schemes 2,7,and 8 exhibited significantly greater attractiveness to adult S.candida.The combined results from the EAG and behavioral response experiments demonstrated that unmated male and female adult S.candida have varying degrees of sensitivity to the volatile compounds from the 10 blending schemes specific to Xizangan Populus×beijingensis.Schemes 2,7,and 8 showed robust EAG responses and attractive behavioural responses to both male and female adult S.candida.展开更多
In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and ...In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.展开更多
基金funded by the China National Space Administration(KJSP2023020105)supported by the National Key R&D Program of China(Grant No.2023YFA1608100)+2 种基金the NSFC(Grant No.62227901)the Minor Planet Foundationsupported by the Egyptian Science,Technology&Innovation Funding Authority(STDF)under Grant No.48102.
文摘Near-Earth objects are important not only in studying the early formation of the Solar System,but also because they pose a serious hazard to humanity when they make close approaches to the Earth.Study of their physical properties can provide useful information on their origin,evolution,and hazard to human beings.However,it remains challenging to investigate small,newly discovered,near-Earth objects because of our limited observational window.This investigation seeks to determine the visible colors of near-Earth asteroids(NEAs),perform an initial taxonomic classification based on visible colors and analyze possible correlations between the distribution of taxonomic classification and asteroid size or orbital parameters.Observations were performed in the broadband BVRI Johnson−Cousins photometric system,applied to images from the Yaoan High Precision Telescope and the 1.88 m telescope at the Kottamia Astronomical Observatory.We present new photometric observations of 84 near-Earth asteroids,and classify 80 of them taxonomically,based on their photometric colors.We find that nearly half(46.3%)of the objects in our sample can be classified as S-complex,26.3%as C-complex,6%as D-complex,and 15.0%as X-complex;the remaining belong to the A-or V-types.Additionally,we identify three P-type NEAs in our sample,according to the Tholen scheme.The fractional abundances of the C/X-complex members with absolute magnitude H≥17.0 were more than twice as large as those with H<17.0.However,the fractions of C-and S-complex members with diameters≤1 km and>1 km are nearly equal,while X-complex members tend to have sub-kilometer diameters.In our sample,the C/D-complex objects are predominant among those with a Jovian Tisserand parameter of T_(J)<3.1.These bodies could have a cometary origin.C-and S-complex members account for a considerable proportion of the asteroids that are potentially hazardous.
文摘This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
文摘AIM:To propose a new endoscopic classification of achalasia for selecting patients appropriate for undergoing peroral endoscopic myotomy(POEM).METHODS:We screened out the data of patients with achalasia examined from October 2000 to September 2011 at our Digestive Endoscopic Center with endoscopic pictures clear enough to reveal the morphology of middle and lower esophagus.After analyzing the correlation between the endoscopic morphology of the esophageal lumen and POEM,we proposed a new endoscopic classification(Ling classification) of achalasia according to three kinds of endoscopically viewed structures:multi-ring structure,crescent-like structure and diverticulum structure.There were three types based on the criteria of Ling classification:type Ⅰ,smooth without multi-ring,crescent-like structure or diverticulum structure;type Ⅱ,with multi-ring or crescent-like structure but without diverticulum structure;and type Ⅲ,with diverticulum structure.Type Ⅱ was classified into three subtypes:Ling Ⅱa,Ling Ⅱb and Ling Ⅱc;and type Ⅲ also had three subtypes:Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr.Two endoscopists made a final decision upon mutual agreement through discussion if their separately recorded characteristics were different.RESULTS:Among the 976 screened patients with achalasia,636 patients with qualified endoscopic pictures were selected for the analysis,including 405 males and 231 females.The average age was 42.7 years,ranging from 6 to 93 years.Type Ⅰ was the most commonly observed type of achalasia,accounting for 64.5%(410/636),and type Ⅲ was the least commonly observed type of achalasia,accounting for 2.8%(18/636).And type Ⅱ accounted for 32.7%(208/636) and subtype of Ling Ⅱa,Ling Ⅱb and Ling Ⅱc accounted for 14.6%(93/636),9.9%(63/636) and 8.2%(52/636),respectively.And subtype of Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr accounted for 0.8%(5/636),0.3%(2/636) and 1.7%(11/636),respectively.CONCLUSION:A new endoscopic classification of achalasia is proposed that might help in determining the proper candidates for POEM.
基金Supported by Gansu Province Joint Fund General Program,No.24JRRA878Gansu Provincial Science and Technology Program Project,No.24JRRA1020+2 种基金Gansu Province Key Talent Program,No.2025RCXM006Teaching Research and Reform Program for Postgraduate Education at Gansu University of Traditional Chinese Medicine(GUSTCM),No.YBXM-202406Special Fund for Mentors of“Qihuang Talents”in the First-Level Discipline of Chinese Medicine,No.ZYXKBD-202415。
文摘BACKGROUND Emerging evidence implicates Candida albicans(C.albicans)in human oncogenesis.Notably,studies have supported its involvement in regulating outcomes in colorectal cancer(CRC).This study investigated the paradoxical role of C.albicans in CRC,aiming to determine whether it promotes or suppresses tumor development,with a focus on the mechanistic basis linked to its metabolic profile.AIM To investigate the dual role of C.albicans in the development and progression of CRC through metabolite profiling and to establish a prognostic model that integrates the microbial and metabolic interactions in CRC,providing insights into potential therapeutic strategies and clinical outcomes.METHODSA prognostic model integrating C. albicans with CRC was developed, incorporating enrichment analysis, immuneinfiltration profiling, survival analysis, Mendelian randomization, single-cell sequencing, and spatial transcriptomics.The effects of the C. albicans metabolite mixture on CRC cells were subsequently validated in vitro. Theprimary metabolite composition was characterized using liquid chromatography-mass spectrometry.RESULTSA prognostic model based on five specific mRNA markers, EHD4, LIME1, GADD45B, TIMP1, and FDFT1, wasestablished. The C. albicans metabolite mixture significantly reduced CRC cell viability. Post-treatment analysisrevealed a significant decrease in gene expression in HT29 cells, while the expression levels of TIMP1, EHD4, andGADD45B were significantly elevated in HCT116 cells. Conversely, LIME1 expression and that of other CRC celllines showed reductions. In normal colonic epithelial cells (NCM460), GADD45B, TIMP1, and FDFT1 expressionlevels were significantly increased, while LIME1 and EHD4 levels were markedly reduced. Following metabolitetreatment, the invasive and migratory capabilities of NCM460, HT29, and HCT116 cells were reduced. Quantitativeanalysis of extracellular ATP post-treatment showed a significant elevation (P < 0.01). The C. albicans metabolitemixture had no effect on reactive oxygen species accumulation in CRC cells but led to a reduction in mitochondrialmembrane potential, increased intracellular lipid peroxidation, and induced apoptosis. Metabolomic profilingrevealed significant alterations, with 516 metabolites upregulated and 531 downregulated.CONCLUSIONThis study introduced a novel prognostic model for CRC risk assessment. The findings suggested that the C.albicans metabolite mixture exerted an inhibitory effect on CRC initiation.
文摘Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
基金funded by the National Natural Science Foundation of China (Grant Nos. 42241103 and 62227901)the Key Research Program of the Institute of Geology and Geophysics, Chinese Academy of Sciences (Grant Nos. IGGCAS-202101 and IGGCAS-202401)
文摘Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071315 and 62271336).
文摘The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘Background: Vulvovaginal candidiasis (VVC) is a common cause of significant morbidity, affecting millions of women worldwide. It is estimated that approximately 75%of women of childbearing age will have at least one episode of candidiasis in their lifetime. In the last decades, resistance to azoles has become a public health problem. Although studies on vulvovaginitis have been done, there is lack of VVC studies in our area. The aim of this study was to describe the etiological and resistance profiles of vulvovaginal candidiasis to standard antifungus at the Saint Camille Hospital of Ouagadougou (HOSCO), Burkina Faso. Methods: We conducted a prospective study from January 2018 to December 2022. From vulvovaginal swabs, Candida species were identified using the ChromID® Candida Agar medium and the API® Candida gallery. Antifungal susceptibility testing was performed using Kirby-Bauer agar disk diffusion. Results: A total of 4789 women were sampled. The average age of sexually active women was 27.80+/−6.77 years, with extremes ranging from 15 to 64 years. Vaginal Candida infections accounted for 74.16% of the cases. The 20 - 29 age group was the most affected by vulvovaginal candidiasis. Pregnant women accounted for 28.76% of our study population. Women in the second (2nd) trimester of pregnancy had more Candida infections. Candida albicans was the most isolated species (55.12%), followed by Candida glabrata (27.64%), Candida tropicalis (6.91%), Candida famata (6.67%), Candida krusei (2.56%). All the Candida species isolated showed very high of resistance to Fluconazole (45.2%), Miconazole (23.7%) and Clotrimazole (45.7%). Conclusion: Species-specific antifungal results should always be considered to avoid antifungal resistance associated with vulvovaginal candidiasis. Identifying the causative species using vaginal fungal cultures can help guide therapy and improve outcomes for these patients.
文摘With the widespread use of upper gastrointestinal endoscopy,more and more gastric polyps(GPs)are being detected.Traditional management strategies often rely on histopathologic examination,which can be time-consuming and may not guide immediate clinical decisions.This paper aims to introduce a novel classification system for GPs based on their potential risk of malignant transformation,categorizing them as"good","bad",and"ugly".A review of the literature and clinical case analysis were conducted to explore the clinical implications,management strategies,and the system's application in endoscopic practice.Good polyps,mainly including fundic gland polyps and inflammatory fibrous polyps,have a low risk of malignancy and typically require minimal or no intervention.Bad polyps,mainly including hyperplastic polyps and adenomas,pose an intermediate risk of malignancy,necessitating closer monitoring or removal.Ugly polyps,mainly including type 3 neuroendocrine tumors and early gastric cancer,indicate a high potential for malignancy and require urgent and comprehensive treatment.The new classification system provides a simplified and practical framework for diagnosing and managing GPs,improving diagnostic accuracy,guiding individualized treatment,and promoting advancements in endoscopic techniques.Despite some challenges,such as the risk of misclassification due to similar endoscopic appearances,this system is essential for the standardized management of GPs.It also lays the foundation for future research into biomarkers and the development of personalized medicine.
文摘In Candida species,the endoplasmic reticulum(ER)stress response—regulated by the unfolded protein response(UPR)—serves as a critical adaptive mechanism affecting both pathogenicity and antifungal resistance.This review aims to synthesize current knowledge on ER stress pathways in Candida glabrata and Candida albicans,highlighting their species-specific adaptations and therapeutic implications.We systematically analyzed peer-reviewed literature on ER stress mechanisms in Candida,focusing on comparative studies of UPR signaling.Emphasis was placed on C.glabrata’s inositol-requiring enzyme 1(IRE1)-dependent Regulated IRE1-Dependent Decay(RIDD)pathway and C.albicans’IRE1/HAC1 and calcium-mediated pathways.Connections to virulence and drug resistance were evaluated through genetic,transcriptomic,and phenotypic evidence.Candida species employ divergent UPR strategies:C.glabrata mitigates ER stress primarily via RIDD-mediated mRNA decay to reduce protein load,while C.albicans enhances folding capacity through HAC1 splicing and calcium homeostasis.These adaptations promote survival in hostile host environments(e.g.,oxidative stress,immune attacks)and are linked to resistance against azoles and echinocandins.Pharmacological disruption of UPR components(e.g.,IRE1 inhibitors)sensitizes Candida to antifungals in experimental models.ER stress response pathways are promising targets for antifungal drug development.Understanding species-specific UPR mechanisms in Candida could guide novel therapies to overcome resistance and improve treatment outcomes.
基金Supported by the National Science and Technology Major Project for New Oil and Gas Exploration and Development(2025ZD1404200)Forward-looking and Fundamental Project of PetroChina Company Limited(2024DJ23)Scientific Research and Technology Development Project of PetroChina Research Institute of Petroleum Exploration&Development(2024vzz).
文摘There are various types of natural gas resources in coal measures,making them major targets for natural gas exploration and development in China.In view of the particularity of the whole petroleum system of coal measures and the reservoir-forming evolution of natural gas in coal,this study reveals the formation,enrichment characteristics and distribution laws of coal-rock gas by systematically reviewing the main types and geological characteristics of natural gas in the whole petroleum system of coal measures.First,natural gas in the whole petroleum system of coal measures is divided into two types,conventional gas and unconventional gas,according to its occurrence characteristics and accumulation mechanism,and into six types,distal detrital rock gas,special rock gas,distal/proximal tight sandstone gas,inner-source tight sandstone gas,shale gas,and coal-rock gas,according to its source and reservoir lithology.The natural gas present in coal-rock reservoirs is collectively referred to as coal-rock gas.Existing data indicate significant differences in the geological characteristics of coal-rock gas exploration and development between shallow and deep layers in the same area,with the transition depth boundary generally 1500-2000 m.Based on the current understanding of coal-rock gas and respecting the historical usage conventions of coalbed methane terminology,coal-rock gas can be divided into deep coal-rock gas and shallow coalbed methane according to burial depth.Second,according to the research concept of“full-process reservoir formation”in the theory of the whole petroleum system of coal measures,based on the formation and evolution of typical coal-rock gas reservoirs,coal-rock gas is further divided into four types:primary coal-rock gas,regenerated coal-rock gas,residual coal-rock gas,and bio coal-rock gas.The first two belong to deep coal-rock gas,while the latter two belong to shallow coal-rock gas.Third,research on the coal-rock gas reservoir formation and evolution shows that shallow coal-rock gas is mainly residual coal-rock gas or bio coal-rock gas formed after geological transformation of primary coal-rock gas,with the reservoir characteristics such as low reservoir pressure,low gas saturation,adsorbed gas in dominance,and gas production by drainage and depressurization,while deep coal-rock gas is mainly primary coal-rock gas and regenerated coal-rock gas,with the reservoir characteristics such as high reservoir pressure,high gas saturation,abundant free gas,and no or little water.In particular,the primary coal-rock gas is wide in distribution,large in resource quantity,and good in reservoir quality,making it the most favorable type of coal-rock gas for exploration and development.
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
文摘Objective To provide a theoretical basis for the adjustment of the registration classification of China’s biological products,and to establish a continuously improved registration classification system.Methods Based on literature research,the specific classification methods,classification principles and considerations of biological registration in China,the United States and the European Union were studied to form a complete comparative analysis.Results and Conclusion It is recommended that the division between therapeutic and preventive use should be removed from the registration classification of biologics.The therapeutic,preventive and diagnostic use of the product should be limited as part of the product specification,and the registration should be classified according to the development of biotechnology,innovation,modification and bio-similar drugs.In addition,the supervision of registration of advanced therapeutic products should be different from that of traditional biologics.
基金supported by the Research and Development of Ecological Control Mechanisms and Application Techniques for Forest Pest and Disease in the North and South Mountains of Lhasa(No.XZ202301ZY0019N)Construction and Comprehensive Service Cap ability Enhancement of Plateau Characteristic Agricultural and Animal Husbandry Science and Technology Small Courtyard(XK202403)。
文摘This study examined the EAG(Electroantennogram)responses of Stilpnotia candida to a mix of host plant volatiles and to provide a foundation for the development of plant-derived attractants.During the peak period of adult eclosion,gas chromatography–mass spectrometry analyzed and identified the volatiles emitted by Populus×beijingensis found in Xizang.Based on the preliminary EAG experiments and the GC‒MS results,a blending scheme was developed.EAG and Y-tube-olfactometry were employed to measure the electrophysiological and behavioural responses of unmated males and females 24 h after eclosion 10 blends of volatiles derived from five host plants.The GC–MS analysis revealed 22 volatile compounds from Populus×beijingensis leaves,composed of esters,hydrocarbons,terpenoids,alcohols,phenols,and ether.The results indicated that all 10 blending schemes produced EAG responses in mature S.candida.The concentration thresholds were between 1 and 10μg·μL^(-1),above the optimal concentration,and a corresponding decrease in EAG was observed.According to intergroup comparisons,mature S.candida had more pronounced EAG responses.Under different concentrations,there were significant differences in the EAG from male and female S.candida to each blending scheme.Behavioural response tests indicated that schemes 2,7,and 8 exhibited significantly greater attractiveness to adult S.candida.The combined results from the EAG and behavioral response experiments demonstrated that unmated male and female adult S.candida have varying degrees of sensitivity to the volatile compounds from the 10 blending schemes specific to Xizangan Populus×beijingensis.Schemes 2,7,and 8 showed robust EAG responses and attractive behavioural responses to both male and female adult S.candida.
基金supported by:the 2023 Basic Public Welfare Research Project of the Wenzhou Science and Technology Bureau“Research on Multi-Source Data Classification and Grading Standards and Intelligent Algorithms for Higher Education Institutions”(Project No.G2023094)Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions(Grant/Award Number:2024QN061)2023 Basic Public Welfare Research Project of Wenzhou(No.:S2023014).
文摘In the context of the rapid development of digital education,the security of educational data has become an increasing concern.This paper explores strategies for the classification and grading of educational data,and constructs a higher educational data security management and control model centered on the integration of medical and educational data.By implementing a multi-dimensional strategy of dynamic classification,real-time authorization,and secure execution through educational data security levels,dynamic access control is applied to effectively enhance the security and controllability of educational data,providing a secure foundation for data sharing and openness.
