The intrinsic physical properties of the noble metal nanoparticles,which are highly sensitive to the nature of their local molecular environment,make such systems ideal for the detection of molecular recognition event...The intrinsic physical properties of the noble metal nanoparticles,which are highly sensitive to the nature of their local molecular environment,make such systems ideal for the detection of molecular recognition events.The current review describes the state of the art concerning molecular recognition of Noble metal nanoparticles.In the first part the preparation of such nanoparticles is discussed along with methods of capping and stabilization.A brief discussion of the three common methods of functionalization:Electrostatic adsorption;Chemisorption;Affinity-based coordination is given.In the second section a discussion of the optical and electrical properties of nanoparticles is given to aid the reader in understanding the use of such properties in molecular recognition.In the main section the various types of capping agents for molecular recognition;nucleic acid coatings,protein coatings and molecules from the family of supramolecular chemistry are described along with their numerous applications.Emphasis for the nucleic acids is on complementary oligonucleotide and aptamer recognition.For the proteins the recognition properties of antibodies form the core of the section.With respect to the supramolecular systems the cyclodextrins,calix[n]arenes,dendrimers,crown ethers and the cucurbitales are treated in depth.Finally a short section deals with the possible toxicity of the nanoparticles,a concern in public health.展开更多
This study involves initial Hartree-Fock and Density Functional theory calculations onthe molecular recognition of the cyclodextrins. The α-cyclodextrin-acetophenone complexationsystem was investigated with PM3, HF/3...This study involves initial Hartree-Fock and Density Functional theory calculations onthe molecular recognition of the cyclodextrins. The α-cyclodextrin-acetophenone complexationsystem was investigated with PM3, HF/3-21G* and B3LYP/3-21G* methods. The results indicatedthat the inclusion orientation in which the acetyl group of the acetophenone points towards thesecondary hydroxyls of the a-cyclodextrin was preferable in energy. The steric effect wassupposed as the physical reason of such a behavior Hence, the simple rule the anti-parallelarrangement of the dipoles of the host and guest molecules in the cyclodextrin complexqtion is notgenerally applicable.展开更多
Anion can be identified by pyromellitic imide-azacyclophane which is one of the host compounds.This article investigated the interaction between the host and organic pollution compounds.The host and other eight compou...Anion can be identified by pyromellitic imide-azacyclophane which is one of the host compounds.This article investigated the interaction between the host and organic pollution compounds.The host and other eight compounds were optimized by DFT(density functional theory) B3LYP/6-31G level and the energy of compounds was corrected using Boys-Bemardi method.On the basis of B3LYP/6-31G optimized geometries,the RDG function and sign(λ2(r))ρ(r) function values of space points were calculated,and color RDG isosurface map was drawn.3He chemical shift was calculated by the B3LYP/6-31G method.The results showed that the eight organic pollution molecules with the host one shaped stable configurations by hydrogen bonds,respectively.The stabilization energy of complexes 4 and 7 showed repulsion(steric effects) of cyclophane cage observably affecting the stability of the complexes.The location,intensity and the type of interaction in complex 1 were analyzed through color-filled RDG isosurface map.Aromaticity calculations showed that the weak interaction reduced the transverse induction ring current in the host rings,and deteriorated the aromaticity of compounds.展开更多
Theoretical study on coordinates between crown ethers and aniline as well as monosaccharides is performed by AM1, MNDO and PM3 methods. It is indicated that crown ethers possess ability to recognize polar guests espec...Theoretical study on coordinates between crown ethers and aniline as well as monosaccharides is performed by AM1, MNDO and PM3 methods. It is indicated that crown ethers possess ability to recognize polar guests especially ionic guests and monosaccharides. Electronic spectra of coordinates are computed by the INDO/SCI method. The reason of the blue-shift for UV absorption of complexes relative to that of hosts is discussed and electronic transition is theoretic- cally assigned.展开更多
Assembly of carbohydrates on nickel （Ⅱ） center by utilizing N-glycosidicbond formation with a branched amine: tris（2-aminoethyl）amine （tren）, an unprecedentedchiral inversion around the metal center （Co or Mn...Assembly of carbohydrates on nickel （Ⅱ） center by utilizing N-glycosidicbond formation with a branched amine: tris（2-aminoethyl）amine （tren）, an unprecedentedchiral inversion around the metal center （Co or Mn） induced by an interaction betweensugars and sulfate anions, peroxo-bridged dinuclear cobalt （Ⅲ） complex containing N-glycoside ligands from tren and D-glucose and its reversible dioxygen binding property,and novel trimanganese complexes with a linear Mn<sub>3</sub> （Ⅱ, Ⅲ, Ⅱ） assemblage bridged bycarbohydrates are described.展开更多
β-Cyclodextrin （β-CD） and its cross-linked polymer （ACDP） were known as the mimetic models. Metalloporphyriu had been widely used in the enzymatic method of analysis and molecular recognition. In present work, i...β-Cyclodextrin （β-CD） and its cross-linked polymer （ACDP） were known as the mimetic models. Metalloporphyriu had been widely used in the enzymatic method of analysis and molecular recognition. In present work, it was investigation that supramolecular recognition for halogenated phenols, three cresols, three nitrophenols and three aminophenols, served respectively as the substrate of the mimetic receptor, iron-5, 10, 15, 20-tetrakis （sulforphenyl）-21H, 23H-porphine （FeTPPS） or FeTPPS-ACDP. Supramolecular complex, FeTPPS-β-CDP with tunction of mult i-recognition and induced-fit, was a advanced kind of mimetic peroxidase; Methyl phenol or polyphenol was the substitute of chlorophenic acid, while aminophenols and other phenols were suggested not to be utilized to enzymatic assay of H2O2. Being a mimetic enzyme mimicking the space structure of overall proteinase 9 beaimed by immobilized mimetic enzyme with a large number of GCD interior cavities, chlorophenol was identified optimal substrate in the system tested.展开更多
In the beginning everything was explained in Biochemistry in terms of hydrogen-bonds (HB). Then, the devastating blow, known as the HB-inventory argument came;hydrogen bonding with water molecules compete with intramo...In the beginning everything was explained in Biochemistry in terms of hydrogen-bonds (HB). Then, the devastating blow, known as the HB-inventory argument came;hydrogen bonding with water molecules compete with intramolecular hydrogen-bonds. As a result, the HBs paradigm fell from grace. The void created was immediately filled by Kauzmann’s idea of hydrophobic (HφO) effect which reigned supreme in biochemical literature for over 50 years (1960-2010). Cracks in the HB-inventory argument on one hand, and doubts about the adequacy of Kauzmann’s model for the HφO effect, have led to a comeback of the HBs, along with a host of new hydrophilic (HφI) effects. The HφO effects lost much of its power - which it never really had - in explaining protein folding and protein-protein association. Instead, the more powerful and richer repertoire of HφI effects took over the reins. The interactions also offered simple and straightforward answers to the problems of protein folding, and protein-protein association.展开更多
A novel synthetic N-(9-fluorenyl methoxy carbonyl)-L-Cysteine (Fmoc-Cys(SH)-OH) receptor was pre- pared by co-polymerizing (9-fluorenyl methoxy carbonyl)-S-(1-propene-2-thiol)-L-Cysteine (Fmoc-Cys(SCH2CHCH2)-OH) and a...A novel synthetic N-(9-fluorenyl methoxy carbonyl)-L-Cysteine (Fmoc-Cys(SH)-OH) receptor was pre- pared by co-polymerizing (9-fluorenyl methoxy carbonyl)-S-(1-propene-2-thiol)-L-Cysteine (Fmoc-Cys(SCH2CHCH2)-OH) and a non-imprinted polymer prepared from 1-propene-1-thiol photo-chemically 15 h at room temperature and additional 3 h thermally at 80℃. Subsequently, disulfides were reduced with lithium aluminum hydride (LiAlH4) from imprinted polymers. The imprinted polymers selectively recognized Fmoc-Cys(SH)-OH with high binding constants in aqueous and protic solvents by thiol-disulfide exchange reactions. In order to estimate the covalent rebinding, particles were further extracted and disulfides reduced were estimated with the non-covalent recognized and covalently bounded analytes. From rebinding studies that were conducted, we observed that proved polymer particles could be reproducible and contain constant binding strengths and recognition properties. Furthermore, we proved that short incubation periods resulted in fast and efficient thiol-disulfide interchange reactions.展开更多
AIM: To generate DNA-aptamers binding to Methicillinresistant Staphylococcus aureus(MRSA).METHODS: The Cell-Systematic Evolution of Ligands by Exponential Enrichment(SELEX) technology was used to run the selection aga...AIM: To generate DNA-aptamers binding to Methicillinresistant Staphylococcus aureus(MRSA).METHODS: The Cell-Systematic Evolution of Ligands by Exponential Enrichment(SELEX) technology was used to run the selection against MRSA bacteria and develop target-specific aptamers. MRSA bacteria were targeted while Enterococcus faecalis bacteria were used for counter selection during that process. Binding assays to determine the right aptamer candidates as well as binding assays on clinical samples were performed through flow cytometry and analyzed using the FlowJ o software. The characterization of the aptamers was done by determination of their Kd values and determined by analysis of flow data at different aptamer concentration using Sigma Plot. Finally, the recognitionof the complex Gold-nanoparticle-aptamer to the bacteria cells was observed using transmission electron microscopy(TEM).RESULTS: During the cell-SELEX selection process, 17 rounds were necessary to generate enrichment of the pool. While the selection was run using fixed cells, it was shown that the binding of the pools with live cells was giving similar results. After sequencing and analysis of the two last pools, four sequences were identified to be aptamer candidates. The characterization of those aptamers showed that based on their Kd values, DTMRSA4 presented the best binding with a Kd value of 94.61 ± 18.82 nmol/L. A total of ten clinical samples of MRSA, S. aureus and Enterococcus faecalis were obtained to test those aptamers and determine their binding on a panel of samples. DTMRSA1 and DTMRSA3 showed the best results regarding their specificity to MRSA, DTMRSA1 being the most specific of all. Finally, those aptamers were coupled with gold-nanoparticle and their binding to MRSA cells was visualized through TEM showing that adduction of nanoparticles on the aptamers did not change their binding property.CONCLUSION: A total of four aptamers that bind to MRSA were obtained with Kd values ranking from 94 to 200 nmol/L.展开更多
Selective molecular recognition in water is routine for bioreceptors,but remains challenging for synthetic hosts.This is principally because noncovalent interactions are usually less efficient in aqueous environments....Selective molecular recognition in water is routine for bioreceptors,but remains challenging for synthetic hosts.This is principally because noncovalent interactions are usually less efficient in aqueous environments.By mimicking the cavity feature of bioreceptors,Prof.Wei Jiang proposed and clarified the concept of“endo-functionalized cavity”.Through situating polar binding sites into a deep hydrophobic cavity,we designed and synthesized several macrocyclic hosts,among which amide naphthotubes are the most representative.The hosts can selectively recognize various polar molecules including organic micropollutants,drug molecules,and chiral molecules in water by employing the hydrophobic effect and shielded hydrogen bonding.In addition,these biomimetic hosts have been applied in spectroscopic analysis,adsorptive separation and self-assembly.In this review,we provide an overview of recent advances on amide naphthotubes with special emphasis on the efforts of Jiang's group.We are convinced that these biomimetic macrocycles will make further contributions to supramolecular chemistry and beyond.展开更多
Infected bone fractures remain a major clinical challenge for orthopedic surgeons.From a tissue regeneration perspective,biomaterial scaffolds with antibacterial and osteoinductive activities are highly desired,while ...Infected bone fractures remain a major clinical challenge for orthopedic surgeons.From a tissue regeneration perspective,biomaterial scaffolds with antibacterial and osteoinductive activities are highly desired,while advanced materials capable of mimicking the pathological microenvironment during the healing process of infected tissues remain an area deserving more research.Hematoma,the gel-like blood coagulum,plays an essential role in bone fracture repair because of its ability to serve as a dynamic and temporary scaffold with cytokines for both pathogen elimination and tissue healing.In light of this,we designed a dynamic hydrogel with hematoma-like antimicrobial or reparative performance for infected bone fracture repair in this study.The proposed dynamic hydrogel network was based on the reversible recognition of a natural glycopeptide antibiotic vancomycin(Van)and its target dipeptide D-Ala-D-Ala(AA),which could serve as a hematoma-like scaffold for obliterating bacteria in the fracture region and promoting bone repair by introducing an endogenous osteogenic peptide(OGP).In vivo experiments demonstrated that the hydrogel could rapidly eradicate bacteria,improve bone regeneration and restore the local inflammatory microenvironment.Together,findings from this study imply that the use of hematoma-like dynamic hydrogel could lead to a biomimetic revolution in surgical strategies against susceptible bone fractures.展开更多
Despite rapid advances in fluorescence detectors over the past decade,the development of a highly stable,sensitive,and selective fluorescence platform for molecular recognition remains a considerable challenge.Here we...Despite rapid advances in fluorescence detectors over the past decade,the development of a highly stable,sensitive,and selective fluorescence platform for molecular recognition remains a considerable challenge.Here we report a stable carbazole-based sp2 carbon fluorescence covalent organic framework(COF)nanosheet,termed a JUC-557 nanosheet.Owing to the synergistic effect of aggregation-induced emission-and aggregation-caused quenching-based chromophores,the architecture of the JUC-577 shows high absolute quantum yields(up to 23.0%)in the solid state and when dispersed in various solvents as well as excellent sensing performance toward specific analytes,such as iodine(Ka:2.10×10^(5)M−1 and LOD:302 ppb),2,4,6-trinitrotoluene(Ka:4.38×10^(5)M−1 and LOD:129 ppb),and especially nitrobenzene(Ka:6.18×10^(6)M−1 and LOD:5 ppb)that is superior to that of fluorescence detection materials reported so far.Furthermore,the JUC-557 nanosheet preserves strong luminescence and sensitive recognition,even under harsh conditions,and allows trace detection of various analytes via a handheld UV lamp.These findings pave the way for developing stable ultrathin COF nanomaterials for highly sensitive and selective molecular detection.展开更多
Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automa...Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.展开更多
This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distri...This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.展开更多
Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seaml...Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.展开更多
This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognit...This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.展开更多
RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still...RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.展开更多
In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requ...In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.展开更多
文摘The intrinsic physical properties of the noble metal nanoparticles,which are highly sensitive to the nature of their local molecular environment,make such systems ideal for the detection of molecular recognition events.The current review describes the state of the art concerning molecular recognition of Noble metal nanoparticles.In the first part the preparation of such nanoparticles is discussed along with methods of capping and stabilization.A brief discussion of the three common methods of functionalization:Electrostatic adsorption;Chemisorption;Affinity-based coordination is given.In the second section a discussion of the optical and electrical properties of nanoparticles is given to aid the reader in understanding the use of such properties in molecular recognition.In the main section the various types of capping agents for molecular recognition;nucleic acid coatings,protein coatings and molecules from the family of supramolecular chemistry are described along with their numerous applications.Emphasis for the nucleic acids is on complementary oligonucleotide and aptamer recognition.For the proteins the recognition properties of antibodies form the core of the section.With respect to the supramolecular systems the cyclodextrins,calix[n]arenes,dendrimers,crown ethers and the cucurbitales are treated in depth.Finally a short section deals with the possible toxicity of the nanoparticles,a concern in public health.
文摘This study involves initial Hartree-Fock and Density Functional theory calculations onthe molecular recognition of the cyclodextrins. The α-cyclodextrin-acetophenone complexationsystem was investigated with PM3, HF/3-21G* and B3LYP/3-21G* methods. The results indicatedthat the inclusion orientation in which the acetyl group of the acetophenone points towards thesecondary hydroxyls of the a-cyclodextrin was preferable in energy. The steric effect wassupposed as the physical reason of such a behavior Hence, the simple rule the anti-parallelarrangement of the dipoles of the host and guest molecules in the cyclodextrin complexqtion is notgenerally applicable.
文摘Anion can be identified by pyromellitic imide-azacyclophane which is one of the host compounds.This article investigated the interaction between the host and organic pollution compounds.The host and other eight compounds were optimized by DFT(density functional theory) B3LYP/6-31G level and the energy of compounds was corrected using Boys-Bemardi method.On the basis of B3LYP/6-31G optimized geometries,the RDG function and sign(λ2(r))ρ(r) function values of space points were calculated,and color RDG isosurface map was drawn.3He chemical shift was calculated by the B3LYP/6-31G method.The results showed that the eight organic pollution molecules with the host one shaped stable configurations by hydrogen bonds,respectively.The stabilization energy of complexes 4 and 7 showed repulsion(steric effects) of cyclophane cage observably affecting the stability of the complexes.The location,intensity and the type of interaction in complex 1 were analyzed through color-filled RDG isosurface map.Aromaticity calculations showed that the weak interaction reduced the transverse induction ring current in the host rings,and deteriorated the aromaticity of compounds.
文摘Theoretical study on coordinates between crown ethers and aniline as well as monosaccharides is performed by AM1, MNDO and PM3 methods. It is indicated that crown ethers possess ability to recognize polar guests especially ionic guests and monosaccharides. Electronic spectra of coordinates are computed by the INDO/SCI method. The reason of the blue-shift for UV absorption of complexes relative to that of hosts is discussed and electronic transition is theoretic- cally assigned.
文摘Assembly of carbohydrates on nickel （Ⅱ） center by utilizing N-glycosidicbond formation with a branched amine: tris（2-aminoethyl）amine （tren）, an unprecedentedchiral inversion around the metal center （Co or Mn） induced by an interaction betweensugars and sulfate anions, peroxo-bridged dinuclear cobalt （Ⅲ） complex containing N-glycoside ligands from tren and D-glucose and its reversible dioxygen binding property,and novel trimanganese complexes with a linear Mn<sub>3</sub> （Ⅱ, Ⅲ, Ⅱ） assemblage bridged bycarbohydrates are described.
基金the National Natural Science Foundation of China
文摘β-Cyclodextrin （β-CD） and its cross-linked polymer （ACDP） were known as the mimetic models. Metalloporphyriu had been widely used in the enzymatic method of analysis and molecular recognition. In present work, it was investigation that supramolecular recognition for halogenated phenols, three cresols, three nitrophenols and three aminophenols, served respectively as the substrate of the mimetic receptor, iron-5, 10, 15, 20-tetrakis （sulforphenyl）-21H, 23H-porphine （FeTPPS） or FeTPPS-ACDP. Supramolecular complex, FeTPPS-β-CDP with tunction of mult i-recognition and induced-fit, was a advanced kind of mimetic peroxidase; Methyl phenol or polyphenol was the substitute of chlorophenic acid, while aminophenols and other phenols were suggested not to be utilized to enzymatic assay of H2O2. Being a mimetic enzyme mimicking the space structure of overall proteinase 9 beaimed by immobilized mimetic enzyme with a large number of GCD interior cavities, chlorophenol was identified optimal substrate in the system tested.
文摘In the beginning everything was explained in Biochemistry in terms of hydrogen-bonds (HB). Then, the devastating blow, known as the HB-inventory argument came;hydrogen bonding with water molecules compete with intramolecular hydrogen-bonds. As a result, the HBs paradigm fell from grace. The void created was immediately filled by Kauzmann’s idea of hydrophobic (HφO) effect which reigned supreme in biochemical literature for over 50 years (1960-2010). Cracks in the HB-inventory argument on one hand, and doubts about the adequacy of Kauzmann’s model for the HφO effect, have led to a comeback of the HBs, along with a host of new hydrophilic (HφI) effects. The HφO effects lost much of its power - which it never really had - in explaining protein folding and protein-protein association. Instead, the more powerful and richer repertoire of HφI effects took over the reins. The interactions also offered simple and straightforward answers to the problems of protein folding, and protein-protein association.
文摘A novel synthetic N-(9-fluorenyl methoxy carbonyl)-L-Cysteine (Fmoc-Cys(SH)-OH) receptor was pre- pared by co-polymerizing (9-fluorenyl methoxy carbonyl)-S-(1-propene-2-thiol)-L-Cysteine (Fmoc-Cys(SCH2CHCH2)-OH) and a non-imprinted polymer prepared from 1-propene-1-thiol photo-chemically 15 h at room temperature and additional 3 h thermally at 80℃. Subsequently, disulfides were reduced with lithium aluminum hydride (LiAlH4) from imprinted polymers. The imprinted polymers selectively recognized Fmoc-Cys(SH)-OH with high binding constants in aqueous and protic solvents by thiol-disulfide exchange reactions. In order to estimate the covalent rebinding, particles were further extracted and disulfides reduced were estimated with the non-covalent recognized and covalently bounded analytes. From rebinding studies that were conducted, we observed that proved polymer particles could be reproducible and contain constant binding strengths and recognition properties. Furthermore, we proved that short incubation periods resulted in fast and efficient thiol-disulfide interchange reactions.
文摘AIM: To generate DNA-aptamers binding to Methicillinresistant Staphylococcus aureus(MRSA).METHODS: The Cell-Systematic Evolution of Ligands by Exponential Enrichment(SELEX) technology was used to run the selection against MRSA bacteria and develop target-specific aptamers. MRSA bacteria were targeted while Enterococcus faecalis bacteria were used for counter selection during that process. Binding assays to determine the right aptamer candidates as well as binding assays on clinical samples were performed through flow cytometry and analyzed using the FlowJ o software. The characterization of the aptamers was done by determination of their Kd values and determined by analysis of flow data at different aptamer concentration using Sigma Plot. Finally, the recognitionof the complex Gold-nanoparticle-aptamer to the bacteria cells was observed using transmission electron microscopy(TEM).RESULTS: During the cell-SELEX selection process, 17 rounds were necessary to generate enrichment of the pool. While the selection was run using fixed cells, it was shown that the binding of the pools with live cells was giving similar results. After sequencing and analysis of the two last pools, four sequences were identified to be aptamer candidates. The characterization of those aptamers showed that based on their Kd values, DTMRSA4 presented the best binding with a Kd value of 94.61 ± 18.82 nmol/L. A total of ten clinical samples of MRSA, S. aureus and Enterococcus faecalis were obtained to test those aptamers and determine their binding on a panel of samples. DTMRSA1 and DTMRSA3 showed the best results regarding their specificity to MRSA, DTMRSA1 being the most specific of all. Finally, those aptamers were coupled with gold-nanoparticle and their binding to MRSA cells was visualized through TEM showing that adduction of nanoparticles on the aptamers did not change their binding property.CONCLUSION: A total of four aptamers that bind to MRSA were obtained with Kd values ranking from 94 to 200 nmol/L.
基金ACKNOWLEDGMENTS This work was supported by the ＂Western Light＂ Visiting Scholar Plan, the Program for New Century Excellent Talents in University （No.NCET-12-1017）, the Program for Grassland Excellent Talents of Inner Mongolia Autonomous Region, the Inner Mengolia Science Technology Plan, the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region （No.NJYT-12-B13）, the Natural Science Foundation of Inner Mongolia Autonomous Region （No.2011BS0601, China）, the Inner Mongolia Talented People Development Fund, and Yongfeng Boyuan Industry Co., Ltd. Jiangxi Province, China.
文摘铈(III ) tetraphenylporphyrin 硝酸盐 Ce (TPP ) NO3 被使用 mesotetraphenylporphyrin (TPP ) 和 Ce (NO3 ) 综合敭瑮污瀠潲散吗？
基金the National Natural Science Foundation of China(Nos.22174059 and 22201128)Hunan Provincial Natural Science Foundation of China(Nos.2022JJ40363 and 2022JJ40365)+1 种基金the Young Science and Technology Innovation Program of Hunan Province(No.2022RC1230)China Postdoctoral Science Foundation(No.2022M721542)for financial support。
文摘Selective molecular recognition in water is routine for bioreceptors,but remains challenging for synthetic hosts.This is principally because noncovalent interactions are usually less efficient in aqueous environments.By mimicking the cavity feature of bioreceptors,Prof.Wei Jiang proposed and clarified the concept of“endo-functionalized cavity”.Through situating polar binding sites into a deep hydrophobic cavity,we designed and synthesized several macrocyclic hosts,among which amide naphthotubes are the most representative.The hosts can selectively recognize various polar molecules including organic micropollutants,drug molecules,and chiral molecules in water by employing the hydrophobic effect and shielded hydrogen bonding.In addition,these biomimetic hosts have been applied in spectroscopic analysis,adsorptive separation and self-assembly.In this review,we provide an overview of recent advances on amide naphthotubes with special emphasis on the efforts of Jiang's group.We are convinced that these biomimetic macrocycles will make further contributions to supramolecular chemistry and beyond.
基金the National Natural Science Foundation of China(32222041,82102619,81925027,21875092)the Natural Science Foundation of Jiangsu Province(BK20220059)the National Key Research and Development Program of China(2019YFA0112000)。
文摘Infected bone fractures remain a major clinical challenge for orthopedic surgeons.From a tissue regeneration perspective,biomaterial scaffolds with antibacterial and osteoinductive activities are highly desired,while advanced materials capable of mimicking the pathological microenvironment during the healing process of infected tissues remain an area deserving more research.Hematoma,the gel-like blood coagulum,plays an essential role in bone fracture repair because of its ability to serve as a dynamic and temporary scaffold with cytokines for both pathogen elimination and tissue healing.In light of this,we designed a dynamic hydrogel with hematoma-like antimicrobial or reparative performance for infected bone fracture repair in this study.The proposed dynamic hydrogel network was based on the reversible recognition of a natural glycopeptide antibiotic vancomycin(Van)and its target dipeptide D-Ala-D-Ala(AA),which could serve as a hematoma-like scaffold for obliterating bacteria in the fracture region and promoting bone repair by introducing an endogenous osteogenic peptide(OGP).In vivo experiments demonstrated that the hydrogel could rapidly eradicate bacteria,improve bone regeneration and restore the local inflammatory microenvironment.Together,findings from this study imply that the use of hematoma-like dynamic hydrogel could lead to a biomimetic revolution in surgical strategies against susceptible bone fractures.
基金supported by the National Natural Science Foundation of China(grant nos.22025504,21621001,21390394,22105082,21772123,21761142011,51502173,and 21702095)the 111 Project(grant nos.BP0719036 and B17020)+3 种基金the China Postdoctoral Science Foundation(grant nos.2020TQ0118 and 2020M681034)the program for the JLU Science and Technology Innovative Research Team,Shanghai Engineering Research Center of Green Energy Chemical Engineering(grant no.18DZ2254200)the 111 Innovation and Talent Recruitment Base on Photochemical and Energy Materials(grant no.D18020),the Shanghai Government(grant nos.21010503400 and 18JC1412900)the International Joint Laboratory of Resource Chemistry(IJLRC).
文摘Despite rapid advances in fluorescence detectors over the past decade,the development of a highly stable,sensitive,and selective fluorescence platform for molecular recognition remains a considerable challenge.Here we report a stable carbazole-based sp2 carbon fluorescence covalent organic framework(COF)nanosheet,termed a JUC-557 nanosheet.Owing to the synergistic effect of aggregation-induced emission-and aggregation-caused quenching-based chromophores,the architecture of the JUC-577 shows high absolute quantum yields(up to 23.0%)in the solid state and when dispersed in various solvents as well as excellent sensing performance toward specific analytes,such as iodine(Ka:2.10×10^(5)M−1 and LOD:302 ppb),2,4,6-trinitrotoluene(Ka:4.38×10^(5)M−1 and LOD:129 ppb),and especially nitrobenzene(Ka:6.18×10^(6)M−1 and LOD:5 ppb)that is superior to that of fluorescence detection materials reported so far.Furthermore,the JUC-557 nanosheet preserves strong luminescence and sensitive recognition,even under harsh conditions,and allows trace detection of various analytes via a handheld UV lamp.These findings pave the way for developing stable ultrathin COF nanomaterials for highly sensitive and selective molecular detection.
基金supported from the National Philosophy and Social Sciences Foundation(Grant No.20BTQ065).
文摘Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.
文摘This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.
基金funded by Researchers Supporting Project Number(RSPD2024 R947),King Saud University,Riyadh,Saudi Arabia.
文摘Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.
基金funded by the Henan Provincial Science and Technology Research Project(222102210086)the Starry Sky Creative Space Innovation Space Innovation Incubation Project of Zhengzhou University of Light Industry(2023ZCKJ211).
文摘This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.
基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDC02040300)for this study.
文摘RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.
基金The authors extend their appreciation to the Arab Open University,Saudi Arabia,for funding this work through AOU research fund No.AOURG-2023-009.
文摘In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.
