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
Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to...Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and Output) are often not so deterministic, therefore they usually are imprecise data such as interval data, hence the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). In this paper the context-dependent DEA models for DMUs with interval data is extended. First, we consider each DMU (which has interval data) as two DMUs (which have exact data) and then, by solving some DEA models, we can find intervals for attractiveness degree of those DMUs. Finally, some numerical experiment is used to illustrate the proposed approach at the end of paper.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
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
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
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
Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial fo...Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial for scene understanding. However, a challenge many semantic learning models face is the lack of data. Existing video datasets are limited to short, low-resolution videos that are not representative of real-world examples. Thus, one of our key contributions is a customized semantic segmentation version of the Walking Tours Dataset that features hour-long, high-resolution, real-world data from tours of different cities. Additionally, we evaluate the performance of open-vocabulary, semantic model OpenSeeD on our own custom dataset and discuss future implications.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
The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational re...The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.展开更多
This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the u...This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the underlying causes,and proposes regulatory countermeasures and recommendations for registrants,regulatory authorities,and social organizations.The objective is to offer practical insights and regulatory guidance to support the enhancement of cosmetic registration and regulatory standards.展开更多
Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance p...Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.展开更多
Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibil...Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibility through a singular policy.But in addition to the accessibility of a place or an activity,to inform about what is accessible is very important as well,and has not really taken off.Indeed,for disabled people,the difficulty lies not only with access to places and the use of resources,but also with the visibility of these resources.This means that information concerning accessibility has to be disclosed and provided effectively to disabled people,those involved with them and the relevant institutions.In different countries all over the world,many labels and pictograms have been created for this purpose and give information relating to accessibility.Using a socio-historical approach,we will present and analyze the different types of icons,symbols,pictograms and labels that have been put in place around the world and in France:what are they used for and for whom are they made?We will show that they are pointers which firstly reflect the diversity and range within the target group concerned by accessibility,and secondly the evolution of accessibility as a dynamic and ecological principle.展开更多
This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properti...This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properties of symbol labeling within a phase shift keying(PSK)modula-tion.These properties reduce the candidate labeling search space.Based on this search space,we take DBICM capacity as the cost function and propose a general method for optimizing symbol labeling by em-ploying the differential evolution algorithm.Numeri-cal results show that our labeling obtains a signal-to-noise ratio(SNR)gain up to 0.45 dB with respect to Gray labeling.展开更多
Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,n...Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,noise inherent in the samples can substantially degrade the detection accuracy of these algorithms.To overcome these challenges,we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation(LP-CRI).The dataset is randomly partitioned into multiple subsets,each of which undergoes clustering followed by label propagation and evaluation.The rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state,initiating a subsequent round of propagation to ensure stable and effective training.Experimental results demonstrate that the proposed method is both computationally efficient and easy to train,significantly enhancing detector performance and outperforming traditional immune detection algorithms.展开更多
Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are pr...Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are primarily identified through teachers’observations and postclass questionnaires or interviews.The expression intensity of students,which is extremely important for fine-grained emotion analysis,is not considered.Hence,a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented.This method aims to recognize the positive emotion levels of a student in an end-to-end framework.First,the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity.Then,a deep-asymmetric convolutional neural network learns the expression model through dual neural networks,to enhance the stability of the network model and avoid the extreme attention region learned.Further,dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information.Thus,diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives.Finally,comparative experiments for the convergence speed,model-training parameters,confusion matrix,and classification probability are performed.The proposed method was applied to a real classroom scene to analyze the emotions of students.Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.展开更多
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.
文摘Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and Output) are often not so deterministic, therefore they usually are imprecise data such as interval data, hence the DEA models becomes a nonlinear programming problem and is called imprecise DEA (IDEA). In this paper the context-dependent DEA models for DMUs with interval data is extended. First, we consider each DMU (which has interval data) as two DMUs (which have exact data) and then, by solving some DEA models, we can find intervals for attractiveness degree of those DMUs. Finally, some numerical experiment is used to illustrate the proposed approach at the end of paper.
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 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.
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.
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Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial for scene understanding. However, a challenge many semantic learning models face is the lack of data. Existing video datasets are limited to short, low-resolution videos that are not representative of real-world examples. Thus, one of our key contributions is a customized semantic segmentation version of the Walking Tours Dataset that features hour-long, high-resolution, real-world data from tours of different cities. Additionally, we evaluate the performance of open-vocabulary, semantic model OpenSeeD on our own custom dataset and discuss future implications.
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.
基金supported by National Natural Science Foundation of China(22161142018,21991081,22177056,and 22174074)the Ministry of Science and Technology of China(2021YFA1600304).
文摘The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.
文摘This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the underlying causes,and proposes regulatory countermeasures and recommendations for registrants,regulatory authorities,and social organizations.The objective is to offer practical insights and regulatory guidance to support the enhancement of cosmetic registration and regulatory standards.
基金funded by the Ongoing Research Funding program(ORF-2025-867),King Saud University,Riyadh,Saudi Arabia.
文摘Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.
文摘Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibility through a singular policy.But in addition to the accessibility of a place or an activity,to inform about what is accessible is very important as well,and has not really taken off.Indeed,for disabled people,the difficulty lies not only with access to places and the use of resources,but also with the visibility of these resources.This means that information concerning accessibility has to be disclosed and provided effectively to disabled people,those involved with them and the relevant institutions.In different countries all over the world,many labels and pictograms have been created for this purpose and give information relating to accessibility.Using a socio-historical approach,we will present and analyze the different types of icons,symbols,pictograms and labels that have been put in place around the world and in France:what are they used for and for whom are they made?We will show that they are pointers which firstly reflect the diversity and range within the target group concerned by accessibility,and secondly the evolution of accessibility as a dynamic and ecological principle.
文摘This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properties of symbol labeling within a phase shift keying(PSK)modula-tion.These properties reduce the candidate labeling search space.Based on this search space,we take DBICM capacity as the cost function and propose a general method for optimizing symbol labeling by em-ploying the differential evolution algorithm.Numeri-cal results show that our labeling obtains a signal-to-noise ratio(SNR)gain up to 0.45 dB with respect to Gray labeling.
基金granted by Key Project of Beijing Municipal Social Science Foundation(No.15ZHA004)Key Project of Beijing Municipal Social Science Foundation and Beijing Municipal Education Commission Social Science Program(No.SZ20231123202).
文摘Many existing immune detection algorithms rely on a large volume of labeled self-training samples,which are often difficult to obtain in practical scenarios,thus limiting the training of detection models.Furthermore,noise inherent in the samples can substantially degrade the detection accuracy of these algorithms.To overcome these challenges,we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation(LP-CRI).The dataset is randomly partitioned into multiple subsets,each of which undergoes clustering followed by label propagation and evaluation.The rebound mechanism assesses the model’s performance after propagation and determines whether to revert to its previous state,initiating a subsequent round of propagation to ensure stable and effective training.Experimental results demonstrate that the proposed method is both computationally efficient and easy to train,significantly enhancing detector performance and outperforming traditional immune detection algorithms.
基金supported in part by the National Natural Science Foundation of China(62067003,61967010,62262030)Key Project of Science and Technology Research of Education Department of Jiangxi Province(GJJ210309)Jiangxi Provincial Natural Science Foundation Youth Foundation(2024BAB20047).
文摘Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are primarily identified through teachers’observations and postclass questionnaires or interviews.The expression intensity of students,which is extremely important for fine-grained emotion analysis,is not considered.Hence,a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented.This method aims to recognize the positive emotion levels of a student in an end-to-end framework.First,the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity.Then,a deep-asymmetric convolutional neural network learns the expression model through dual neural networks,to enhance the stability of the network model and avoid the extreme attention region learned.Further,dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information.Thus,diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives.Finally,comparative experiments for the convergence speed,model-training parameters,confusion matrix,and classification probability are performed.The proposed method was applied to a real classroom scene to analyze the emotions of students.Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.
