Recognition Mechanism Research Articles

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Intelligent Detection of Surface Defects in High-Speed Railway Ballastless Track Based on Self-Attention and Transfer Learning

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways.

High-sensitivity THz-ATR imaging of cerebral ischemia in a rat model

The fast label-free detection of the extent and degree of cerebral ischemia has been the difficulty and hotspot for precise and accurate neurosurgery. We experimentally demonstrated that the fresh cerebral tissues at different ischemic stages within 24 hours can be well distinguished from the normal tissues using terahertz (THz) attenuated total reflection (ATR) imaging system. It was indicated that the total reflectivity of THz wave for ischemic cerebral tissues was lower than that for normal tissues. Especially, compared to the images stained with 2,3,5-triphenyl tetrazolium chloride (TTC), the ischemic tissues can be detected using THz wave with high sensitivity as early as the ischemic time of 2.5 hours, where THz images showed the ischemic areas became larger and diffused as the ischemic time increasing. Furthermore, the THz spectroscopy of cerebral ischemic tissues at different ischemic times was obtained in the range of 0.5-2.0 THz. The absorption coefficient of ischemic tissue increased with the increase of ischemic time, whereas the refractive index decreased with prolonging the ischemic time. Additionally, it was found from hematoxylin and eosin (H&E) staining microscopic images that, with the ischemic time increasing, the cell size and cell density of the ischemic tissues decreased, whereas the intercellular substance of the ischemic tissues increased. The result showed that THz recognition mechanism of the ischemia is mainly based on the increase of intercellular substance, especially water content, which has a stronger impact on absorption of THz wave than that of cell density. Thus, THz imaging has great potential for recognition of cerebral ischemia and it may become a new method for intraoperative real-time guidance, recognition in situ, and precise excision.

Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic Sensing

Silent Speech Interfaces (SSI) on mobile devices offer a privacy-friendly alternative to conventional voice input methods. Previous research has primarily focused on smartphones. In this paper, we introduce Lipwatch, a novel system that utilizes acoustic sensing techniques to enable SSI on smartwatches. Lipwatch leverages the inaudible waves emitted by the watch's speaker to capture lip movements and then analyzes the echo to enable SSI. In contrast to acoustic sensing-based SSI on smartphones, our development of Lipwatch takes into full consideration the specific scenarios and requirements associated with smartwatches. Firstly, we elaborate a wake-up-free mechanism, allowing users to interact without the need for a wake-up phrase or button presses. The mechanism utilizes the inertial sensors on the smartwatch to detect gestures, in combination with acoustic signals that detecting lip movements to determine whether SSI should be activated. Secondly, we design a flexible silent speech recognition mechanism that explores limited vocabulary recognition to comprehend a broader range of user commands, even those not present in the training dataset, relieving users from strict adherence to predefined commands. We evaluate Lipwatch on 15 individuals using a set of the 80 most common interaction commands on smartwatches. The system achieves a Word Error Rate (WER) of 13.7% in user-independent test. Even when users utter commands containing words absent in the training set, Lipwatch still demonstrates a remarkable 88.7% top-3 accuracy. We implement a real-time version of Lipwatch on a commercial smartwatch. The user study shows that Lipwatch can be a practical and promising option to enable SSI on smartwatches.

CRISPR-Cas12a-mediated colorimetric aptasensor of netilmicin based on enzymes-assisted signal amplification and nanozyme employing a rationally truncated aptamer

Netilmicin (NET) is a typical veterinary antibiotic used in aquaculture and animal husbandry, but its excessive accumulation poses a serious threat to the ecological environment and human health. Therefore, a novel and highly sensitive Crispr-Cas12a-mediated colorimetric aptasensor was developed by combining the enzyme-assisted signal amplification strategy and magnetic NMOF-Pt nanozyme composite material (NPSM) for the visual detection of veterinary antibiotic residues in real samples. A high-affinity truncated aptamer (N-20) targeting NET was evolved from a parental aptamer sequence with the help of comprehensive recognition mechanism studies. Subsequently, N-20 was elaborately designed into the isothermal enzyme-assisted strand displacement amplification reaction. The amplified products (Act-DNAs) activated the trans-cleavage activity in the Crispr Cas12a system. The NMOF-Pt nanozyme was released from NPSM after cleaving the single chain of DNA (ssDNA). The quantity of released nanozyme was positively correlated with the amount of NET to quantitatively achieve the detection with the linear range and limits of detection (LOD) of about 0.003–30 ng/mL and 1.15 pg/mL, respectively. This method was successfully applied to detect NET in Lake water, milk and pork samples with satisfactory recoveries ranging from 89.9 % to 104.3 %. The constructed aptasensor demonstrated superior analytical performance and exhibited enormous application prospects in food supervision, environment monitoring, and medical laboratory science.

Research progress on the detection of foodborne pathogens based on aptamer recognition.

Foodborne diseases caused by bacterial contamination are a serious threat to food safety and human health. The classical plate culture method has the problems of long detection cycle, low sensitivity and specificity, and complicated operation, which cannot meet the growing demand for rapid quantitative detection of pathogenic bacteria. The frequent outbreak of foodborne diseases has put forward higher requirements for rapid and simple detection technology of foodborne pathogens. Aptamer is a kind of oligonucleotide fragment that can recognize targets with the advantages of high affinity and good specificity. The target can be range from proteins, small molecules, cells bacteria, and even viruses. Herein, the latest advances in sensitive and rapid detection of foodborne pathogens based on aptamer recognition was reviewed. Special attention has been paid to the obtained sequences of aptamers to various foodborne pathogens, the optimization of sequences, and the mechanism of aptamer recognition. Then, the research progress of biosensors for the detection of pathogenic bacteria based on aptamer recognition were summarized. Some challenges and prospects for the detection of foodborne pathogens based on aptamer recognition were prospected. In summary, with the further deepening of aptamer research and improvement of detection technology, aptamer-based recognition can meet the needs of rapid, sensitive, and accurate detection in practical applications.

Recognition mechanism of split T-2 toxin aptamer coupled with reliable dual-mode detection in peanut and beer

T-2 toxin is a highly poisonous trichothecene and a well-known contaminant in cereal grains and cereal-based products, hence it is essential to design a simple, reliable, and precise detection mechanism for ensuring food safety and human health. In response to this imperative, we innovatively designed an unreported split T-2 toxin aptamer probe, and the cooperative binding mechanism of the split aptamer to T-2 was first investigated. Based on these, a dual-mode fluorescence (FL) and fluorescence polarization (FP) aptasensor was developed for T-2 toxin detection in peanut and beer samples. The results showed an excellent detection property of the aptasensor, indicating a linear range of 0.1–20 nM and 0.2–50 nM at a detection limit of 0.1 nM, 0.12 nM for FL and FP signals, respectively. In actual sample assays, recoveries of 101.7%–115.2%, 109.8%–121.6% for FL and FP mode, respectively, were reached in peanuts, whereas recoveries ranged from 81.0% -124.0%, 94.1%–107.8% for FL and FP mode in beer. This designed dual-mode biosensor not only showed excellent performances but also verified each other and proved the reliability of binary split aptamer molecular probes.

Multiple-component covalent organic frameworks for simultaneous extraction and determination of multitarget pollutants in sea foods

Persistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have been raising global concerns due to their toxic effects on environment and human health. The monitoring of residues of POPs in seafood is crucial for assessing the accumulation of these contaminants in the study area and mitigating potential risks to human health. However, the diversity and complexity of POPs in seafood present significant challenges for their simultaneous detection. Here, a novel multi-component fluoro-functionalized covalent organic framework (OH-F-COF) was designed as SPE adsorbent for simultaneous extraction POPs. On this basis, the recognition and adsorption mechanisms were investigated by molecular simulation. Due to multiple interactions and large specific surface area, OH-F-COF displayed satisfactory coextraction performance for PFASs, PCBs, and BPs. Under optimized conditions, the OH-F-COF sorbent was employed in a strategy of simultaneous extraction and stepwise elution (SESE), in combination with HPLC−MS/MS and GC−MS method, to effectively determined POPs in seafood collected from coastal areas of China. The method obtained low detection limits for BPs (0.0037 −0.0089 ng/g), PFASs (0.0038 −0.0207 ng/g), and PCBs (0.2308 −0.2499 ng/g), respectively. This approach provided new research ideas for analyzing and controlling multitarget POPs in seafood. Environmental implicationsPersistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have caused serious hazards to human health and ecosystems. Hence, there is a need to develop a quantitative method that can rapidly detect POPs in environmental and food samples. Herein, a novel multi-component fluorine-functionalized covalent organic skeletons (OH-F-COF) were prepared at room temperature, and served as adsorbent for POPs. The SESE-SPE strategy combined with chromatographic techniques was used to achieve a rapid detection of POPs in sea foods from the coastal provinces of China. This method provides a valuable tool for analyzing POPs in environmental and food samples.

Intramolecular autoinhibition regulates the selectivity of PRPF40A tandem WW domains for proline-rich motifs

PRPF40A plays an important role in the regulation of pre-mRNA splicing by mediating protein-protein interactions in the early steps of spliceosome assembly. By binding to proteins at the 5´ and 3´ splice sites, PRPF40A promotes spliceosome assembly by bridging the recognition of the splices. The PRPF40A WW domains are expected to recognize proline-rich sequences in SF1 and SF3A1 in the early spliceosome complexes E and A, respectively. Here, we combine NMR, SAXS and ITC to determine the structure of the PRPF40A tandem WW domains in solution and characterize the binding specificity and mechanism for proline-rich motifs recognition. Our structure of the PRPF40A WW tandem in complex with a high-affinity SF1 peptide reveals contributions of both WW domains, which also enables tryptophan sandwiching by two proline residues in the ligand. Unexpectedly, a proline-rich motif in the N-terminal region of PRPF40A mediates intramolecular interactions with the WW tandem. Using NMR, ITC, mutational analysis in vitro, and immunoprecipitation experiments in cells, we show that the intramolecular interaction acts as an autoinhibitory filter for proof-reading of high-affinity proline-rich motifs in bona fide PRPF40A binding partners. We propose that similar autoinhibitory mechanisms are present in most WW tandem-containing proteins to enhance binding selectivity and regulation of WW/proline-rich peptide interaction networks.

Mechanisms of ligand recognition and activation of melanin-concentrating hormone receptors

Melanin-concentrating hormone (MCH) is a cyclic neuropeptide that regulates food intake, energy balance, and other physiological functions by stimulating MCHR1 and MCHR2 receptors, both of which are class A G protein-coupled receptors. MCHR1 predominately couples to inhibitory G protein, Gi/o, and MCHR2 can only couple to Gq/11. Here we present cryo-electron microscopy structures of MCH-activated MCHR1 with Gi and MCH-activated MCHR2 with Gq at the global resolutions of 3.01 Å and 2.40 Å, respectively. These structures reveal that MCH adopts a consistent cysteine-mediated hairpin loop configuration when bound to both receptors. A central arginine from the LGRVY core motif between the two cysteines of MCH penetrates deeply into the transmembrane pocket, triggering receptor activation. Integrated with mutational and functional insights, our findings elucidate the molecular underpinnings of ligand recognition and MCH receptor activation and offer a structural foundation for targeted drug design.

Molecular mechanism for recognition of the cargo adapter Rab6GTP by the dynein adapter BicD2.

Rab6 is a key modulator of protein secretion. The dynein adapter Bicaudal D2 (BicD2) recruits the motors cytoplasmic dynein and kinesin-1 to Rab6GTP-positive vesicles for transport; however, it is unknown how BicD2 recognizes Rab6. Here, we establish a structural model for recognition of Rab6GTP by BicD2, using structure prediction and mutagenesis. The binding site of BicD2 spans two regions of Rab6 that undergo structural changes upon the transition from the GDP- to GTP-bound state, and several hydrophobic interface residues are rearranged, explaining the increased affinity of the active GTP-bound state. Mutations of Rab6GTP that abolish binding to BicD2 also result in reduced co-migration of Rab6GTP/BicD2 in cells, validating our model. These mutations also severely diminished the motility of Rab6-positive vesicles in cells, highlighting the importance of the Rab6GTP/BicD2 interaction for overall motility of the multi-motor complex that contains both kinesin-1 and dynein. Our results provide insights into trafficking of secretory and Golgi-derived vesicles and will help devise therapies for diseases caused by BicD2 mutations, which selectively affect the affinity to Rab6 and other cargoes.

Novel biocompatible N-rich AIE fluorescent probe for live cell imaging and visual onsite detection of uranium.

A sensitive and biocompatible N-rich probe for rapid visualuranium detectionwas constructed by grafting two trianiline groups to 2,6-bis(aminomethyl)pyridine. Possessing excellent aggregation-induced emission (AIE) property and the advantages to form multidentate chelate with U selectively, the probe has been applied successfully to visualize uranium in complex environmental water samples and living cells, demonstrating outstanding anti-interference ability against large equivalent of different ions over a wide effective pH range. A large linear range (1.0 × 10-7-9.0 × 10-7mol/L) and low detection limit (72.6nmol/L, 17.28ppb) were achieved for the visual determination of uranium. The recognition mechanism, photophysical properties, analytical performance and cytotoxicity were systematically investigated, demonstrating highpotential for fast risk assessment of uranium pollution in field and in vivo.

Structural basis of chorismate isomerization by Arabidopsis ISOCHORISMATE SYNTHASE1.

Salicylic acid (SA) plays a crucial role in plant defense against biotrophic and semi-biotrophic pathogens. In Arabidopsis (Arabidopsis thaliana), isochorismate synthase 1 (AtICS1) is a key enzyme for the pathogen-induced biosynthesis of SA via catalytic conversion of chorismate into isochorismate, an essential precursor for SA synthesis. Despite the extensive knowledge of ICS1-related menaquinone, siderophore, tryptophan (MST) enzymes in bacteria, the structural mechanisms for substrate binding and catalysis in plant isochorismate synthase (ICS) enzymes are unknown. This study reveals that plant ICS enzymes catalyze the isomerization of chorismate through a magnesium-dependent mechanism, with AtICS1 exhibiting the most substantial catalytic activity. Additionally, we present high-resolution crystal structures of apo AtICS1 and its complex with chorismate, offering detailed insights into the mechanisms of substrate recognition and catalysis. Importantly, our investigation indicates the existence of a potential substrate entrance channel and a gating mechanism regulating substrate into the catalytic site. Structural comparisons of AtICS1 with MST enzymes suggest a shared structural framework with conserved gating and catalytic mechanisms. This work provides valuable insights into the structural and regulatory mechanisms governing substrate delivery and catalysis in AtICS1, as well as other plant ICS enzymes.

Explainable deep learning for sEMG-based similar gesture recognition: A Shapley-value-based solution

Surface electromyography (sEMG) based gesture recognition shows promise in enhancing human-robot interaction. However, accurately recognizing similar gestures is a challenging task, and the underlying mechanisms of gesture recognition are not well understood. To address these issues, we developed a new solution called the Shapley-value-based similar gesture recognition (SV-SGR) method. Our solution combines deep learning and game theory to achieve high recognition accuracy and interpretability. First, we devised a data preprocessing method that converts sEMG signals into sEMG color images, which can be more effectively utilized by deep learning techniques. Next, we established a deep-neural-network-based model for gesture recognition using the processed sEMG color images. Then, we designed a global explanation approach based on Shapley values to quantify the contribution of each channel to recognizing similar gestures. Finally, we carried out an explanation analysis, which provides feedback on the recognition model to enhance the precision of gesture recognition. Extensive comparisons and interpretable analyses have been conducted on real-world datasets, and the results demonstrate that the SV-SGR method outperforms other baselines under various experimental conditions. The interpretable analysis method based on Shapley values effectively enhances the performance of recognizing similar gestures and provides valuable insights into the decision-making process of recognition models.

Functionalized DNA Origami-Enabled Detection of Biomarkers.

Biomarkers are crucial physiological and pathological indicators in the host. Over the years, numerous detection methods have been developed for biomarkers, given their significant potential in various biological and biomedical applications. Among these, the detection system based on functionalized DNA origami has emerged as a promising approach due to its precise control over sensing modules, enabling sensitive, specific, and programmable biomarker detection. We summarize the advancements in biomarker detection using functionalized DNA origami, focusing on strategies for DNA origami functionalization, mechanisms of biomarker recognition, and applications in disease diagnosis and monitoring. These applications are organized into sections based on the type of biomarkers-nucleic acids, proteins, small molecules, and ions-and concludes with a discussion on the advantages and challenges associated with using functionalized DNA origami systems for biomarker detection.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors.

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

The RNA binding protein EHD6 recruits the m6A reader YTH07 and sequesters OsCOL4 mRNA into phase-separated ribonucleoprotein condensates to promote rice flowering

N6-methyladenosine (m6A) is one of the most abundant modifications in eukaryotic mRNA, but the comprehensive biological functionality continues to be a subject for exploration. In this study, we identified and characterized a new flowering-promoting gene EARLY HEADING DATE6 (EHD6) in rice. EHD6 encodes an RNA recognition motif (RRM)-containing RNA binding protein that is localized in the non-membranous cytoplasm ribonucleoprotein (RNP) granules and can bind both m6A-modified RNA and unmodified RNA indiscriminately. We found that EHD6 can physically interact with YTH07, a YTH (YT521-B homology) domain containing m6A reader, and their interaction enhances the binding of m6A-modified RNA and triggers relocation of a part of YTH07 from the cytoplasm into RNP granules through phase-separated condensation. Within these condensates, the mRNA of a rice flowering repressor, CONSTANS-like 4 (OsCOL4), becomes sequestered, leading to a reduction in its protein abundance and thus affect flowering through the Early heading date 1 pathway. Our results not only shed new light on the molecular mechanism of efficient m6A recognition by the collaboration between the RNA binding protein and YTH family m6A reader, but also uncovers a potential m6A mediated translation regulation through phase-separated ribonucleoprotein condensation in rice.

Neural mechanisms of Chinese character recognition, updating, and maintenance in the N-back task

Using the N-back task, we investigated how memory load influences the neural activity of the Chinese character cognitive subprocess (recognition, updating, and maintenance) in Mainland Chinese speakers. Twenty-seven participants completed the Chinese character N-back paradigm while having their event-related potentials recorded. The study employed time and frequency domain analyses of EEG data. Results showed that accuracy decreased and response times increased with larger N values. For ERPs, N2pc and P300 amplitudes decreased and SW amplitude increased with larger N values. For time frequency analyses, the desynchronization of alpha oscillations decreased after stimulus onset, but the synchronization of alpha oscillations increased during the maintenance phase. The results suggest that greater memory load is related to a decrease in cognitive resources during updating and an increase in cognitive resources during information maintenance. The results of a behavioral-ERP data structural equation model analysis showed that the ERP indicators in the maintenance phase predicted behavioral performance.

Exploring the molecular mechanisms of Lrp/AsnC-type transcription regulator DecR, an L-cysteine-responsive feast/famine regulatory protein

The Lrp/AsnC family of transcriptional regulators is commonly found in prokaryotes and is associated with the regulation of amino acid metabolism. However, it remains unclear how the L-cysteine-responsive Lrp/AsnC family regulator perceives and responds to L-cysteine. Here, we try to elucidate the molecular mechanism of the L-cysteine-responsive transcriptional regulator. Through 5′RACE and EMSA, we discovered a 15 bp incompletely complementary pair palindromic sequence essential for DecR binding, which differed slightly from the binding sequence of other Lrp/AsnC transcription regulators. Using alanine scanning, we identified the L-cysteine binding site on DecR and found that different Lrp/AsnC regulators adjust their binding pocket's side-chain residues to accommodate their specific effector. MD simulations were then conducted to explore how ligand binding influences the allosteric behavior of the protein. PCA and in silico docking revealed that ligand binding induced perturbations in the linker region, triggering conformational alterations and leading to the relocalization of the DNA-binding domains, enabling the embedding of the DNA-binding region of DecR into the DNA molecule, thereby enhancing DNA-binding affinity. Our findings can broaden the understanding of the recognition and regulatory mechanisms of the Lrp/AsnC-type transcription factors, providing a theoretical basis for further investigating the molecular mechanisms of other transcription factors.

Three-dimensional reconstruction of wear particle surfaces using multi-focus image sequence

Wear particles hold morphological traits that are linked to wear rate and mechanisms. Wear particle analysis is vital in the context of wear monitoring. Current methods can be used in three-dimensional (3D) reconstruction for particle type recognition and wear mechanism identification. A 3D reconstruction method based on microscopic focusing and image processing is here proposed. A fixed-focus quasi-planar strategy is built using the defocusing principle. The stationary wavelet transform is employed to assess the degree of focus for the purpose of depth mapping, facilitating the construction of 3D particle models. The model is then optimized for surface morphology to address noise and unevenness. The reconstruction results of the proposed approach were compared with that of laser scanning confocal microscope with average errors of arithmetic mean height (Sa), root-mean-square height (Sq), and kurtosis (Sku) at 2.81%, 2.96%, and 10.06%, respectively. The algorithm is non-contact and adaptable, offering value across various fields.