critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr...critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
The two-dimensional(2D)layered material molybdenum disulfide(MoS_(2))exhibits a special Mo-S-Mo sandwich structure with a rather large spacing,making it a promising candidate as an anode material for sodium storage ap...The two-dimensional(2D)layered material molybdenum disulfide(MoS_(2))exhibits a special Mo-S-Mo sandwich structure with a rather large spacing,making it a promising candidate as an anode material for sodium storage applications.Unfortunately,the practical applications are limited by their intrinsically low electrical conductivity,significant volume alteration and severe particle agglomeration.In this study,we designed a new two-step solvothermal strategy to synthesize ultrathin nanosheetassembled MoS_(2)hollow nanospheres strongly located onlignite-based carbon(MoS_(2)/C)without any template.The ultrathin nanosheets assembled into hollow structures mitigated the volume changes of MoS_(2)during the(dis)-charge cycles,facilitated Na+diffusion,and reduced the migration energy barrier within MoS_(2).Lignite-based C enhances the electrical conductivity of MoS_(2),prevents its aggregation,and alleviates mechanical stress during repeated(dis)charging.The resultant hollow spherical MoS_(2)/C composite exhibits outstanding cyclability and rate performance when used as an anode in sodium-ion batteries,as it delivers a high specific capacity of 515.8 mAh g^(-1)after 1000 cycles at 1.0 A g^(-1),with a 94.34%capacity retention rate.Even at a high current density of 20 Ag^(-1),a capacity of 431 mAh g^(-1)can still be obtained after 2000cycles.In particular,the initial Coulombic efficiency of the MoS_(2)anode is markedly enhanced by the incorporation of lignite-based C.展开更多
End Polio Pakistan program still has to overcome many hurdles;unfortunately on 8th February2016 first polio case of the year has surfaced in Karachi.It seems that battle against polio demands little bit more convictio...End Polio Pakistan program still has to overcome many hurdles;unfortunately on 8th February2016 first polio case of the year has surfaced in Karachi.It seems that battle against polio demands little bit more conviction and motivation.WHO has set a goal of polio eradication in Pakistan till 2018,in order to evaluate the success of this target;polio eradication campaign in Pakistan has been analyzed in different perspectives.Our analysis indicated that major obstacles in eradication are low literacy rate,poor health infrastructure,lack of planning,natural disaster,economic crisis,counter insurgencies and almost no protection for polio health workers.WHO has allocated new funds to tackle this problem,now there is a need to spend this money more effectively with proper planning and honest deployment of funds.展开更多
Transcriptomic changes at the cessation of sugar accumulation in the pericarp of Vitis vinifera were addressed on single berries re-synchronised according to their individual growth patterns.The net rates of water,sug...Transcriptomic changes at the cessation of sugar accumulation in the pericarp of Vitis vinifera were addressed on single berries re-synchronised according to their individual growth patterns.The net rates of water,sugars and K+accumulation inferred from individual growth and solute concentration confirmed that these inflows stopped simultaneously in the ripe berry,while the small amount of malic acid remaining at this stage was still being oxidised at low rate.Re-synchronised individual berries displayed negligible variations in gene expression among triplicates.RNA-seq studies revealed sharp reprogramming of cell-wall enzymes and structural proteins at the stop of phloem unloading,associated with an 80%repression of multiple sugar transporters and aquaporins on the plasma or tonoplast membranes,with the noticeable exception of H^(+)/sugar symporters,which were rather weakly and constitutively expressed.This was verified in three genotypes placed in contrasted thermo-hydric conditions.The prevalence of SWEET suggests that electrogenic transporters would play a minor role on the plasma membranes of SE/CC complex and the one of the flesh,while sucrose/H+exchangers dominate on its tonoplast.Cis-regulatory elements present in their promoters allowed to sort these transporters in different groups,also including specific TIPs and PIPs paralogs,and cohorts of cell wall-related genes.Together with simple thermodynamic considerations,these results lead to propose that H^(+)/sugar exchangers at the tonoplast,associated with a considerably acidic vacuolar pH,may exhaust cytosolic sugars in the flesh and alleviate the need for supplementary energisation of sugar transport at the plasma membrane.展开更多
Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that ...Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis （PCA） and cluster analysis （CA） using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component （PC） with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base （BL）, shape of leaf apex （SA）, ratio of lamina width to lamina length （R）, dots color at the lower midrib （DLM）, color of young syconium （CYS）, color of mature syconium （CMS） and the number of syconium on trees （DST）. This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised.展开更多
Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get informat...Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.展开更多
Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ...Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.展开更多
基金funded by the Ministry of Higher Education(MoHE)Malaysia through the Fundamental Research Grant Scheme—Early Career Researcher(FRGS-EC),grant number FRGSEC/1/2024/ICT02/UNIMAP/02/8.
文摘critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
基金financially supported by the National Natural Science Foundation of China(Nos.51962027 and 21968022)the Major Science and Technology Project of Inner Mongolia Autonomous Region(No.2021ZD0016)+5 种基金the National Key R&D Program of China(No.2020YFC1909105)the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(No.NJYT24002)the Central Guidance Fund for Local Scientific and Technological Development(No.2024ZY0012)the Key Project of Tianjin Natural Science Foundation(No.23JCZDJC00570)the Special Funding of China Postdoctoral Science Foundation(No.2023T160268)the China Postdoctoral Science Foundation(No.2023M741362)
文摘The two-dimensional(2D)layered material molybdenum disulfide(MoS_(2))exhibits a special Mo-S-Mo sandwich structure with a rather large spacing,making it a promising candidate as an anode material for sodium storage applications.Unfortunately,the practical applications are limited by their intrinsically low electrical conductivity,significant volume alteration and severe particle agglomeration.In this study,we designed a new two-step solvothermal strategy to synthesize ultrathin nanosheetassembled MoS_(2)hollow nanospheres strongly located onlignite-based carbon(MoS_(2)/C)without any template.The ultrathin nanosheets assembled into hollow structures mitigated the volume changes of MoS_(2)during the(dis)-charge cycles,facilitated Na+diffusion,and reduced the migration energy barrier within MoS_(2).Lignite-based C enhances the electrical conductivity of MoS_(2),prevents its aggregation,and alleviates mechanical stress during repeated(dis)charging.The resultant hollow spherical MoS_(2)/C composite exhibits outstanding cyclability and rate performance when used as an anode in sodium-ion batteries,as it delivers a high specific capacity of 515.8 mAh g^(-1)after 1000 cycles at 1.0 A g^(-1),with a 94.34%capacity retention rate.Even at a high current density of 20 Ag^(-1),a capacity of 431 mAh g^(-1)can still be obtained after 2000cycles.In particular,the initial Coulombic efficiency of the MoS_(2)anode is markedly enhanced by the incorporation of lignite-based C.
文摘End Polio Pakistan program still has to overcome many hurdles;unfortunately on 8th February2016 first polio case of the year has surfaced in Karachi.It seems that battle against polio demands little bit more conviction and motivation.WHO has set a goal of polio eradication in Pakistan till 2018,in order to evaluate the success of this target;polio eradication campaign in Pakistan has been analyzed in different perspectives.Our analysis indicated that major obstacles in eradication are low literacy rate,poor health infrastructure,lack of planning,natural disaster,economic crisis,counter insurgencies and almost no protection for polio health workers.WHO has allocated new funds to tackle this problem,now there is a need to spend this money more effectively with proper planning and honest deployment of funds.
基金We would like to thank the Poupelain Foundation,the Comite Interprofessionnel des Vins de Bordeaux(CIVB)the Agence Nationale de la Recherche(G2WAS project,ANR-19-CE20-0024)for providing the financial support of this study.
文摘Transcriptomic changes at the cessation of sugar accumulation in the pericarp of Vitis vinifera were addressed on single berries re-synchronised according to their individual growth patterns.The net rates of water,sugars and K+accumulation inferred from individual growth and solute concentration confirmed that these inflows stopped simultaneously in the ripe berry,while the small amount of malic acid remaining at this stage was still being oxidised at low rate.Re-synchronised individual berries displayed negligible variations in gene expression among triplicates.RNA-seq studies revealed sharp reprogramming of cell-wall enzymes and structural proteins at the stop of phloem unloading,associated with an 80%repression of multiple sugar transporters and aquaporins on the plasma or tonoplast membranes,with the noticeable exception of H^(+)/sugar symporters,which were rather weakly and constitutively expressed.This was verified in three genotypes placed in contrasted thermo-hydric conditions.The prevalence of SWEET suggests that electrogenic transporters would play a minor role on the plasma membranes of SE/CC complex and the one of the flesh,while sucrose/H+exchangers dominate on its tonoplast.Cis-regulatory elements present in their promoters allowed to sort these transporters in different groups,also including specific TIPs and PIPs paralogs,and cohorts of cell wall-related genes.Together with simple thermodynamic considerations,these results lead to propose that H^(+)/sugar exchangers at the tonoplast,associated with a considerably acidic vacuolar pH,may exhaust cytosolic sugars in the flesh and alleviate the need for supplementary energisation of sugar transport at the plasma membrane.
文摘Ficus deltoidea Jack （Moraceae） or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis （PCA） and cluster analysis （CA） using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component （PC） with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base （BL）, shape of leaf apex （SA）, ratio of lamina width to lamina length （R）, dots color at the lower midrib （DLM）, color of young syconium （CYS）, color of mature syconium （CMS） and the number of syconium on trees （DST）. This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised.
文摘Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
