Golgi apparatus(GA)-associated secretome runs through the endomembrane system and is critical for inter-and intracellular communication networks.However,achieving in situ dissection of the GA-associated secretome rema...Golgi apparatus(GA)-associated secretome runs through the endomembrane system and is critical for inter-and intracellular communication networks.However,achieving in situ dissection of the GA-associated secretome remains challenging owing to the scarcity of specific labeling methods.This work develops an aggregation-induced emission(AIE)luminogen-mediated photocatalytic proximity labeling strategy that allows profiling of the GA-associated secretome with high spatiotemporal precision.Leveraging an AIE luminogen-derived GA-targeting photocatalyst,this strategy achieves efficient labeling of proteins in minutes within the Golgi lumen upon light activation,which enables spatiotemporally resolved modification of histidine and tyrosine residues.We succeed in profiling secretome in both living HeLa cells and hard-to-transfect macrophage HMC3 cells,and a significant subset of GA-associated secretome with 80%specificity is determined,linking the distinct GA-associated secretory profiles to cellular characteristics.The method is further applied to proteome mapping of brain and bone metastatic lung cancer cells,which reveals the underlying roles the GA-associated secretome plays in extracellular matrix organization during metastasis.This work delivers a robust tool to break the dilemma of chemical labeling of GA-associated secretome in living cells,and provides mechanistic insights into secretion regulation at the subcellular level.展开更多
Dear Editor,The long-term use of copper(Cu)fungicides to prevent downy mildew of vine led to the accumulation of Cu in vineyard topsoils(Komárek et al.,2010;Droz et al.,2021),which may alter the functioning and s...Dear Editor,The long-term use of copper(Cu)fungicides to prevent downy mildew of vine led to the accumulation of Cu in vineyard topsoils(Komárek et al.,2010;Droz et al.,2021),which may alter the functioning and sustainability of vineyard ecosystems(Cornu et al.,2022).展开更多
The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-inf...The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.展开更多
The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid ...The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid syntheses of aromatic scaffolds that contains deuterium remain an important yet elusive task.State-of-the-art approaches mainly relied on the transition metal-catalyzed C-H deuteration via the assistance of directing groups(DGs),which often suffered from over-deuteration and lengthy step counts required for installation and/or removal of DG.Herein,we report a generalizable synthetic linchpin strategy for the facile preparation of the ortho-deuterated aromatic core.Through capture of aryne-derived 1,3-zwitterion with heavy water,we synthesized an array of ortho-deuterated aryl sulfonium salts.These novel linchpins not only participated the transition metal catalyzed cross-coupling reaction as nucleophiles,but also served as aryl radical reservoirs under photochemical or electrochemical conditions,enabling facile and precise access to structurally diverse deuterated aromatics.Moreover,we have disclosed a novel EDA complex enabled direct arylation of phosphines under visible-light irradiation,further expanding the utility of our platform approach.展开更多
Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.S...Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.展开更多
Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total ...Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.展开更多
Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exh...Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases.Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders.To investigate this heterogeneity,we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions.This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein(GfaABC1D)promoter.The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions.The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images.Furthermore,the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation.This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases.The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions.展开更多
Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-...Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-box models that lacks interpretability as well as they fuse features by simply stacking them,overlooking the discrepancies in the importance of different features,which reduces the credibility and diagnosis accuracy of the models.2)They ignore the effects of potentially mistaken labels in the training datasets disrupting the ability of the models to learn the true data distribution,which degrades the generalization performance of intelligent diagnosis models,especially when the training samples are limited.To address the above items,an interpretable few-shot framework for fault diagnosis with noisy labels is proposed for train transmission systems.In the proposed framework,a feature extractor is constructed by stacked frequency band focus modules,which can capture signal features in different frequency bands and further adaptively concentrate on the features corresponding to the potential fault characteristic frequency.Then,according to prototypical network,a novel metric-based classifier is developed that is tolerant to mislabeled support samples in the case of limited samples.Besides,a new loss function is designed to decrease the impact of label mistakes in query datasets.Finally,fault simulation experiments of subway train transmission systems are designed and conducted,and the effectiveness as well as superiority of the proposed method are proved by ablation experiments and comparison with the existing methods.展开更多
This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-sta...This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.展开更多
The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set inco...The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address ...Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.展开更多
Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)G...Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)Ga,and^(89)Zr.Among the radiotracers,the radioactive^(18)F-labelled chemical agent as PET probes plays a predominant role in monitoring,detecting,treating,and predicting tumours due to its perfect half-life.In this paper,the^(18)F-labelled chemical materials as PET probes are systematically summarized.First,we introduce various radionuclides of PET and elaborate on the mechanism of PET imaging.It highlights the^(18)F-labelled chemical agents used as PET probes,including[^(18)F]-2-deoxy-2-[^(18)F]fluoro-D-glucose([^(18)F]-FDG),^(18)F-labelled amino acids,^(18)F-labelled nucleic acids,^(18)F-labelled receptors,^(18)F-labelled reporter genes,and^(18)F-labelled hypoxia agents.In addition,some PET probes with metal as a supplementary element are introduced briefly.Meanwhile,the^(18)F-labelled nanoparticles for the PET probe and the multi-modality imaging probe are summarized in detail.The approach and strategies for the fabrication of^(18)F-labelled PET probes are also described briefly.The future development of the PET probe is also prospected.The development and application of^(18)F-labelled PET probes will expand our knowledge and shed light on the diagnosis and theranostics of tumours.展开更多
Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting...Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting their structures and biochemical activities.Over the last few years,a variety of promising strategies have been devised that address these challenges including those that involve introduction of small-size peptide tags or unnatural amino acids(UAAs),chemical labeling of specific protein residues,and affinity-driven labeling.This review summarizes recent developments made in the area of site-specific protein labeling utilizing genetically encoding-and chemical-based methods,and discusses future issues that need to be addressed by researchers in this field.展开更多
The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicit...The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicity Week,which ran from June 23 to 29.展开更多
In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is c...In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is called a k-product cordial labeling if | vf(i)−vf(j) |≤1, and | ef(i)−ef(j) |≤1, i,j∈{ 0,1,⋯,k−1 }, where vf(x)and ef(x)denote the number of vertices and edges respectively labeled with x (x=0,1,⋯,k−1). Motivated by this concept, we further studied and established that several families of graphs admit k-product cordial labeling. In this paper, we show that the path graphs Pnadmit k-product cordial labeling.展开更多
基金support from the National Key R&D Program of China(2023YFA0913902)the National Natural Science Foundation of China(32088101,22074140,and 22474136)the Dalian Institute of Chemical Physics,Chinese Academy of Sciences(DICP-I202316).
文摘Golgi apparatus(GA)-associated secretome runs through the endomembrane system and is critical for inter-and intracellular communication networks.However,achieving in situ dissection of the GA-associated secretome remains challenging owing to the scarcity of specific labeling methods.This work develops an aggregation-induced emission(AIE)luminogen-mediated photocatalytic proximity labeling strategy that allows profiling of the GA-associated secretome with high spatiotemporal precision.Leveraging an AIE luminogen-derived GA-targeting photocatalyst,this strategy achieves efficient labeling of proteins in minutes within the Golgi lumen upon light activation,which enables spatiotemporally resolved modification of histidine and tyrosine residues.We succeed in profiling secretome in both living HeLa cells and hard-to-transfect macrophage HMC3 cells,and a significant subset of GA-associated secretome with 80%specificity is determined,linking the distinct GA-associated secretory profiles to cellular characteristics.The method is further applied to proteome mapping of brain and bone metastatic lung cancer cells,which reveals the underlying roles the GA-associated secretome plays in extracellular matrix organization during metastasis.This work delivers a robust tool to break the dilemma of chemical labeling of GA-associated secretome in living cells,and provides mechanistic insights into secretion regulation at the subcellular level.
基金financially supported by the Bordeaux Wine Interprofessional Council(French acronym CIVB)in the framework of the EXTRACUIVRE projectby the French National Research Institute for Agriculture,Food and Environment(INRAE)in the framework of the COPOFTEA projectpartially supported by the TSU Program Priority 2030,Russia。
文摘Dear Editor,The long-term use of copper(Cu)fungicides to prevent downy mildew of vine led to the accumulation of Cu in vineyard topsoils(Komárek et al.,2010;Droz et al.,2021),which may alter the functioning and sustainability of vineyard ecosystems(Cornu et al.,2022).
基金supported by the National Natural Science Foundation of China (No.22007008)the LiaoNing Revitalization Talents Program (No.XLYC1907021)the Fundamental Research Funds for the Central Universities (Nos.DUT23YG120,DUT19RC(3)009)。
文摘The study of target proteins is crucial for understanding molecular interactions and developing analytical platforms,therapeutic agents and functional tools.Herein,we present a novel nanoplatform activated by near-infrared(NIR) light for triple-modal proteins study,which enabling target protein labeling,enrichment and visualization.Azido-naphthalimide-coated upconversion nanoparticles(UCNPs) serve as NIR light-responsive nanoplatforms,showing promising applications in studying interactions between various bioactive molecules and proteins in living systems.Under NIR light irradiation,azido-naphthalimides are activated by ultraviolet(UV) and blue light emitted from UCNPs and the resulting amino-naphthalimides intermediate not only crosslink nearby target proteins but also enable imaging performance.We demonstrate that this nanoplatform is capable of selective protein labeling and imaging in complex protein environments,achieving specific labeling and imaging of both intracellular and extracellular proteins in mammalian cells as well as bacteria.Furthermore,in vivo protein labeling has been achieved using this novel NIR light-activatable nanoplatform.This technique will open new avenues for discoveries and mechanistic interrogation in chemical biology.
基金supported by the National Natural Science Foundation of China (Nos.22271010 and 21702013)。
文摘The deuterium labeling has garnered significant interest in drug discovery due to its critical role on improving pharmacokinetic and metabolic properties.However,despite its pharmaceutical value,the general and rapid syntheses of aromatic scaffolds that contains deuterium remain an important yet elusive task.State-of-the-art approaches mainly relied on the transition metal-catalyzed C-H deuteration via the assistance of directing groups(DGs),which often suffered from over-deuteration and lengthy step counts required for installation and/or removal of DG.Herein,we report a generalizable synthetic linchpin strategy for the facile preparation of the ortho-deuterated aromatic core.Through capture of aryne-derived 1,3-zwitterion with heavy water,we synthesized an array of ortho-deuterated aryl sulfonium salts.These novel linchpins not only participated the transition metal catalyzed cross-coupling reaction as nucleophiles,but also served as aryl radical reservoirs under photochemical or electrochemical conditions,enabling facile and precise access to structurally diverse deuterated aromatics.Moreover,we have disclosed a novel EDA complex enabled direct arylation of phosphines under visible-light irradiation,further expanding the utility of our platform approach.
文摘Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels.
基金financial supports from the National Science Foundation of China(42277062,41977149 and 42230714).
文摘Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.
基金National Natural Science Foundation of China,No.32271148(to JW)the National Key Research and the Development Program of China,No.2023M740625(to ML)+1 种基金the Natural Science Foundation of Guangdong Province,Nos.2021B1515120050(to HW)and 2023A1515110782(to ML)and Key R&D Program of Ningxia Hui Autonomous Region,No.2024BEG02027(to JW).
文摘Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases.Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders.To investigate this heterogeneity,we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions.This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein(GfaABC1D)promoter.The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions.The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images.Furthermore,the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation.This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases.The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions.
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金supported in part by the National Key R&D Program of China under Grant 2022YFB4300601in part by the State Key Laboratory of Advanced Rail Autonomous Operation under Grant RAO2023ZZ003.
文摘Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety,stability,and efficiency of railway operations.However,existing studies have the following limitations.1)They are typical black-box models that lacks interpretability as well as they fuse features by simply stacking them,overlooking the discrepancies in the importance of different features,which reduces the credibility and diagnosis accuracy of the models.2)They ignore the effects of potentially mistaken labels in the training datasets disrupting the ability of the models to learn the true data distribution,which degrades the generalization performance of intelligent diagnosis models,especially when the training samples are limited.To address the above items,an interpretable few-shot framework for fault diagnosis with noisy labels is proposed for train transmission systems.In the proposed framework,a feature extractor is constructed by stacked frequency band focus modules,which can capture signal features in different frequency bands and further adaptively concentrate on the features corresponding to the potential fault characteristic frequency.Then,according to prototypical network,a novel metric-based classifier is developed that is tolerant to mislabeled support samples in the case of limited samples.Besides,a new loss function is designed to decrease the impact of label mistakes in query datasets.Finally,fault simulation experiments of subway train transmission systems are designed and conducted,and the effectiveness as well as superiority of the proposed method are proved by ablation experiments and comparison with the existing methods.
基金supported by the Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province(2022A0505030025)the Science and Technology Fund,FDCT,Macao SAR(0064/2021/A2)
文摘This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.
基金supported by the National Natural Science Foundation of China(No.U21B2062).
文摘The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
文摘Target occlusion poses a significant challenge in computer vision,particularly in agricultural applications,where occlusion of crops can obscure key features and impair the model’s recognition performance.To address this challenge,a mushroom recognition method was proposed based on an erase module integrated into the EL-DenseNet model.EL-DenseNet,an extension of DenseNet,incorporated an erase attention module designed to enhance sensitivity to visible features.The erase module helped eliminate complex backgrounds and irrelevant information,allowing the mushroom body to be preserved and increasing recognition accuracy in cluttered environments.Considering the difficulty in distinguishing similar mushroom species,label smoothing regularization was employed to mitigate mislabeling errors that commonly arose from human observers.This strategy converted hard labels into soft labels during training,reducing the model’s overreliance on noisy labels and improving its generalization ability.Experimental results showed that the proposed EL-DenseNet,when combined with transfer learning,achieved a recognition accuracy of 96.7%for mushrooms in occluded and complex backgrounds.Compared with the original DenseNet and other classic models,this approach demonstrated superior accuracy and robustness,providing a promising solution for intelligent mushroom recognition.
文摘Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)Ga,and^(89)Zr.Among the radiotracers,the radioactive^(18)F-labelled chemical agent as PET probes plays a predominant role in monitoring,detecting,treating,and predicting tumours due to its perfect half-life.In this paper,the^(18)F-labelled chemical materials as PET probes are systematically summarized.First,we introduce various radionuclides of PET and elaborate on the mechanism of PET imaging.It highlights the^(18)F-labelled chemical agents used as PET probes,including[^(18)F]-2-deoxy-2-[^(18)F]fluoro-D-glucose([^(18)F]-FDG),^(18)F-labelled amino acids,^(18)F-labelled nucleic acids,^(18)F-labelled receptors,^(18)F-labelled reporter genes,and^(18)F-labelled hypoxia agents.In addition,some PET probes with metal as a supplementary element are introduced briefly.Meanwhile,the^(18)F-labelled nanoparticles for the PET probe and the multi-modality imaging probe are summarized in detail.The approach and strategies for the fabrication of^(18)F-labelled PET probes are also described briefly.The future development of the PET probe is also prospected.The development and application of^(18)F-labelled PET probes will expand our knowledge and shed light on the diagnosis and theranostics of tumours.
基金supported by the National Key R&D Program of China(No.2021YFC2103600)the National Natural Science Foundation of China(Nos.22278224,22478191)+1 种基金the Project of Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the State Key Laboratory of Materials Oriented Chemical Engineering(No.KL21-08)。
文摘Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting their structures and biochemical activities.Over the last few years,a variety of promising strategies have been devised that address these challenges including those that involve introduction of small-size peptide tags or unnatural amino acids(UAAs),chemical labeling of specific protein residues,and affinity-driven labeling.This review summarizes recent developments made in the area of site-specific protein labeling utilizing genetically encoding-and chemical-based methods,and discusses future issues that need to be addressed by researchers in this field.
文摘The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicity Week,which ran from June 23 to 29.
文摘In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is called a k-product cordial labeling if | vf(i)−vf(j) |≤1, and | ef(i)−ef(j) |≤1, i,j∈{ 0,1,⋯,k−1 }, where vf(x)and ef(x)denote the number of vertices and edges respectively labeled with x (x=0,1,⋯,k−1). Motivated by this concept, we further studied and established that several families of graphs admit k-product cordial labeling. In this paper, we show that the path graphs Pnadmit k-product cordial labeling.
