Recognition Step Research Articles

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

AbstractFruit images collected by picking robots in natural environments have problems such as uneven lighting, complex backgrounds, occlusion of branches and leaves, and overlapping fruits and vegetables, which greatly increases the difficulty for picking robots to accurately identify target fruits and vegetables. Meanwhile, most fruits and vegetables have similar‐color backgrounds. Compared with the ones with different colors in the background, fruits and vegetables in similar‐color backgrounds are similar to the colors of their leaves and surrounding weeds, which increases the difficulty of identification. Therefore, realizing the accurate identification of fruits and vegetables in similar‐color backgrounds is vital to realize the dynamic monitoring of the growth of fruits and vegetables and intelligent picking, which has important value and application prospects for optimizing plantation management and automated harvesting operations. The work summarized the specific characteristics of fruits and vegetables with the close‐color background and the different processing procedures in the identification process. The following methods have been summarized, for example, image acquisition, image preprocessing, feature extraction, image segmentation, and fruit and vegetable detection in the process, and some methods were compared. Finally, the current research was discussed, and a solution was proposed for the different processing steps of fruit and vegetable recognition in similar‐color backgrounds. Deep learning methods were combined with some traditional methods to identify fruits and vegetables with close‐color background, which provides references for further research in various aspects.

Nucleotide excision repair: a versatile and smart toolkit.

Nucleotide excision repair (NER) is a major pathway to deal with bulky adducts induced by various environmental toxins in all cellular organisms. The two sub-pathways of NER, global genome repair (GGR) and transcription-coupled repair (TCR), differ in the damage recognition modes. In this review, we describe the molecular mechanism of NER in mammalian cells, especially the details of damage recognition steps in both sub-pathways. We also introduce new sequencing methods for genome-wide mapping of NER, as well as recent advances about NER in chromatin by these methods. Finally, the roles of NER factors in repairing oxidative damages and resolving R-loops are discussed.

Conformational rearrangements upon start codon recognition in human 48S translation initiation complex.

Selection of the translation start codon is a key step during protein synthesis in human cells. We obtained cryo-EM structures of human 48S initiation complexes and characterized the intermediates of codon recognition by kinetic methods using eIF1A as a reporter. Both approaches capture two distinct ribosome populations formed on an mRNA with a cognate AUG codon in the presence of eIF1, eIF1A, eIF2–GTP–Met-tRNAiMet and eIF3. The ‘open’ 40S subunit conformation differs from the human 48S scanning complex and represents an intermediate preceding the codon recognition step. The ‘closed’ form is similar to reported structures of complexes from yeast and mammals formed upon codon recognition, except for the orientation of eIF1A, which is unique in our structure. Kinetic experiments show how various initiation factors mediate the population distribution of open and closed conformations until 60S subunit docking. Our results provide insights into the timing and structure of human translation initiation intermediates and suggest the differences in the mechanisms of start codon selection between mammals and yeast.

USP14-regulated allostery of the human proteasome by time-resolved cryo-EM

Proteasomal degradation of ubiquitylated proteins is tightly regulated at multiple levels1–3. A primary regulatory checkpoint is the removal of ubiquitin chains from substrates by the deubiquitylating enzyme ubiquitin-specific protease 14 (USP14), which reversibly binds the proteasome and confers the ability to edit and reject substrates. How USP14 is activated and regulates proteasome function remain unknown4–7. Here we present high-resolution cryo-electron microscopy structures of human USP14 in complex with the 26S proteasome in 13 distinct conformational states captured during degradation of polyubiquitylated proteins. Time-resolved cryo-electron microscopy analysis of the conformational continuum revealed two parallel pathways of proteasome state transitions induced by USP14, and captured transient conversion of substrate-engaged intermediates into substrate-inhibited intermediates. On the substrate-engaged pathway, ubiquitin-dependent activation of USP14 allosterically reprograms the conformational landscape of the AAA-ATPase motor and stimulates opening of the core particle gate8–10, enabling observation of a near-complete cycle of asymmetric ATP hydrolysis around the ATPase ring during processive substrate unfolding. Dynamic USP14–ATPase interactions decouple the ATPase activity from RPN11-catalysed deubiquitylation11–13 and kinetically introduce three regulatory checkpoints on the proteasome, at the steps of ubiquitin recognition, substrate translocation initiation and ubiquitin chain recycling. These findings provide insights into the complete functional cycle of the USP14-regulated proteasome and establish mechanistic foundations for the discovery of USP14-targeted therapies.

A Survey for Emotion Recognition Based on Speech Signal

There are many characteristics of human beings, such as fingerprints, DNA, and retinal pigmentation that are essential. A person's voice is unique to each individual. Humans use speech to communicate their thoughts and feelings. The process of determining one's mental state involves expressing one's basic emotions in words. A person's emotions play a significant part in his or her daily existence. In order to convey one's thoughts and feelings to others, it is essential to use this method. Emotions can be discerned from speech information because humans have a built-in ability to do so. The selection of an emotion recognition body (speech database), the identification of numerous variables connected to speech, and the selection of a suitable classification model are the main hurdles for emotion recognition. To identify emotions, an emotion identification system analyzes auditory structural elements of speech. The analysis is based on multiple research papers and includes an in-depth examination of the methodology and data sets. The study discovered that emotion detection is accomplished using four distinct methodologies: physiological signal recognition, facial expression recognition, a variety of speech signals, and text semantics in both objective and subjective databases such as JAFFE, CK+, Berlin emotional database, and SAVEE. In general, these techniques enable the identification of seven basic emotions. To determine the emotion, the audio expression for eight emotions (happy, angry, sad, depressed, bored, anxious, afraid, and apprehensive), all published research maintain an average level of accuracy. The major goal of this survey is to compare and contrast numerous previous survey methodologies, which are backed up by empirical evidence. This study covered signal collection processing, feature extraction, and signal classification, as well as the pros and downsides of each approach. It also goes over a number of strategies that may need to be tweaked at each step of speech emotion recognition.

Construction of a Smart Library Subject Precise Service Platform Based on User Needs

Under the development and support of digital, networked and intelligent technologies (IT), university wisdom libraries adopt superb IT, sophisticated big data resource allocation, fine supervised wisdom RFD management system and exquisite supervised wisdom service system to provide a wisdom environment to meet users’ personalized needs and promote the transformation of libraries to a new form of wisdom. University intelligent libraries are still in their infancy and development stages in today’s world; the infrastructure has not been fully developed, the number of intelligent devices, the types of intelligent services, and the level of intelligence have all been developed but are still in their infancy and development stages, and the level of intelligence has not yet reached a significant level, and the level of intelligence has not yet reached a significant level. This paper develops the construction of a precise service platform for wisdom library disciplines based on users’ needs. In this paper, we propose a hybrid continuous ant colony optimization (BACO) method, and further propose a BACO-based multidoor image segmentation bun type for facial image segmentation. The experiments show that the proposed method has good performance and can lay the foundation for the next step of facial recognition and even the construction of smart library to ensure the accurate service of university smart library.

HMG-Like DSP1 Mediates Immune Responses of the Western Flower Thrips (Frankliniella occidentalis) Against Beauveria bassiana, a Fungal Pathogen.

Western flower thrips, Frankliella occidentalis, is a serious pest by directly infesting host crops. It can also give indirect damage to host crops by transmitting a plant virus called tomato spotted wilt virus. A fungal pathogen, Beauveria bassiana, can infect thrips. It has been used as a biopesticide. However, little is known on the defense of thrips against this fungal pathogen. This study assessed the defense of thrips against the fungal infection with respect to immunity by analyzing immune-associated genes of F. occidentalis in both larvae and adults. Immunity-associated genes of western flower thrips were selected from three immunity steps: nonself recognition, mediation, and immune responses. For the pathogen recognition step, dorsal switch protein 1 (DSP1) was chosen. For the immune mediation step, phospholipase A2 (PLA2) and prostaglandin E2 synthase were also selected. For the step of immune responses, two phenoloxidases (PO) genes and four proPO-activating peptidase genes involved in melanization against pathogens were chosen. Dual oxidase gene involved in the production of reactive oxygen species and four antimicrobial peptide genes for executing humoral immune responses were selected. All immunity-associated genes were inducible to the fungal infection. Their expression levels were induced higher in adults than in larvae by the fungal infections. However, inhibitor treatments specific to DSP1 or PLA2 significantly suppressed the inducible expression of these immune-associated genes, leading to significant enhancement of fungal pathogenicity. These results suggest that immunity is essential for thrips to defend against B. bassiana, in which DSP1 and eicosanoids play a crucial role in eliciting immune responses.

PIFS Scheme for HEad Pose Estimation Aimed at Faster Face Recognition

In the context of 2D face biometrics, pose represents one challenging intra-class variation that, over the years, has been approached in a number of different ways, such as through inherently pose-invariant features, or by means of specifically trained deep networks, or even by frontalization techniques aimed to restore a canonical pose. To this regard, the idea behind this paper is to perform head pose estimation (HPE) previous to the face recognition step, so that the probe’s resulting yaw, pitch and roll values can be used to possibly find the pose-wise closest elements in the gallery, thus reducing one-to-many matching time. We exploit a common approach to both pose estimation and feature matching, where fractal encoding is used to extract a feature vector and to compare it to a reference template through a metric distance for both HPE and the facial recognition. Experiments conducted on the Biwi Kinect Head Pose database and the AFLW2000 dataset show state-of-the art level of precision for head pose estimation along with a 10x efficiency boost in the overall face recognition task, compared to popular deep learning approaches, whilst achieving also an edge in terms of recognition accuracy.

A two-stage adaptive thresholding segmentation for noisy low-contrast images

Image recognition is the process of recognizing and classifying objects with machine learning algorithms. Image binarization is the first and most challenging step in image recognition, in which foreground objects are separated from their background. When foreground objects have complex morphological structure and background noise is strong, foreground objects are often being fractured into subcomponents. To address the over-segmentation issue of organisms with complex structures, we propose a 2-stage adaptive binarization approach based on Sauvola's binarization algorithm. We tested the effectiveness of the new approach on a set of underwater images with jellyfish collected in nearshore waters using a shadowgraph underwater plankton imaging system, PlanktonScope, because jellyfish have relatively complex structure and are often over-segemented. The results showed that the 2-stage approach improved the integrity of extracted jellyfish compared to traditional binarization methods, including Sauvola's algorithm. The analysis of local entropy values showed that the first stage effectively suppresses redundant information in the image and reduces the number of Region of Interests (ROIs), and the second stage preserves relatively weak and low-intensity signals to ensure the integrity of the extracted targets. The 2-stage approach improves hardware resource utilization and computational efficiency. It is robust for images acquired in sub-optimal conditions and enhances the accuracy of analytical results in the study of marine organisms using imaging systems.

Structural insights into recognition of chemokine receptors by Staphylococcus aureus leukotoxins.

Staphylococcus aureus (SA) leukocidin ED (LukED) belongs to a family of bicomponent pore forming toxins that play important roles in SA immune evasion and nutrient acquisition. LukED targets specific G protein-coupled chemokine receptors to lyse human erythrocytes (red blood cells) and leukocytes (white blood cells). The first recognition step of receptors is critical for specific cell targeting and lysis. The structural and molecular bases for this mechanism are not well understood but could constitute essential information to guide antibiotic development. Here, we characterized the interaction of LukE with chemokine receptors ACKR1, CCR2, and CCR5 using a combination of structural, pharmacological, and computational approaches. First, crystal structures of LukE in complex with a small molecule mimicking sulfotyrosine side chain (p-cresyl sulfate) and with peptides containing sulfotyrosines issued from receptor sequences revealed the location of receptor sulfotyrosine binding sites in the toxins. Then, by combining previous and novel experimental data with protein docking, classical and accelerated weight histogram (AWH) molecular dynamics we propose models of the ACKR1-LukE and CCR5-LukE complexes. This work provides novel insights into chemokine receptor recognition by leukotoxins and suggests that the conserved sulfotyrosine binding pocket could be a target of choice for future drug development.

The first step is recognizing there is a problem: a methodology for adjusting for variability in disease severity when estimating clinician performance

BackgroundAdoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others.MethodsWe propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions.ResultsOur metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (β = 0.63, 95% confidence interval 0.46–0.80, p < 7 × 10− 5). Non-PCCM physicians recognized ARDS cases less frequently and expressed greater satisfaction with the ability to get the information needed for making an ARDS diagnosis (p < 5 × 10− 4), suggesting that lower performing clinicians may be less aware of institutional barriers.ConclusionsWe present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.

Biophysical characterization of the interaction of Atg8 with a disordered region of Nup159 involved in selective autophagy of the nuclear pore complex

A functional proteome in the cell is maintained by coordinate regulation of biogenesis, folding, and degradation of cellular proteins. Although the degradation pathways have been extensively characterized for various substrates, it remains elusive how large multiprotein complexes are selectively degraded. Recent investigations have discovered selective autophagic degradation of the yeast Nuclear Pore Complex (NPC) consisting of ∼500 proteins and mediating selective nucleocytoplasmic transport. To understand the underlying molecular mechanism of NPC-phagy, we performed biophysical characterization of the interaction between Atg8 and an intrinsically disordered region (IDR) of Nup159 involved in the initial recognition step. In particular, from the systematic isothermal titration calorimetry (ITC) experiments, we determined the thermodynamic parameters and discovered a significant negative heat capacity change (ΔCp°) for the interaction. Furthermore, the heat capacity change becomes more negative at higher temperatures, yielding a negative curvature in the observed enthalpy change (ΔH°) with respect to temperature. This thermodynamic feature was analyzed in terms of coupling between binding and conformational equilibria of Atg8 and/or Nup159 IDR. We interpret the coupled conformational equilibria as disorder-to-order transitions or local stabilizations of Nup159 IDR and/or partially unfolded Atg8 upon binding. A potential impact of the proposed coupling in the initial step of NPC-phagy is discussed. In a broader view, our study demonstrates that a negative curvature of ΔH° can be used as a probe for conformational selection processes in the interactions of IDRs with their target proteins.

Active DNA damage eviction by HLTF stimulates nucleotide excision repair

Nucleotide excision repair (NER) counteracts the onset of cancer and aging by removing helix-distorting DNA lesions via a "cut-and-patch"-type reaction. The regulatory mechanisms that drive NER through its successive damage recognition, verification, incision, and gap restoration reaction steps remain elusive. Here, we show that the RAD5-related translocase HLTF facilitates repair through active eviction of incised damaged DNA together with associated repair proteins. Our data show a dual-incision-dependent recruitment of HLTF to the NER incision complex, which is mediated by HLTF's HIRAN domain that binds 3'-OH single-stranded DNA ends. HLTF's translocase motor subsequently promotes the dissociation of the stably damage-bound incision complex together with the incised oligonucleotide, allowing for an efficient PCNA loading and initiation of repair synthesis. Our findings uncover HLTF as an important NER factor that actively evicts DNA damage, thereby providing additional quality control by coordinating the transition between the excision and DNA synthesis steps to safeguard genome integrity.

An improved low-complexity DenseUnet for high-accuracy iris segmentation network

Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms.

Intelligence System for Multi-Language Recognition

Language classification systems are used to classify spoken language from a particular phoneme sample and are usually the first step of many spoken language processing tasks, such as automatic speech recognition (ASR) systems Without automatic language detection, spoken speech cannot be properly analyzed and grammar rules cannot be applied, causing failures Subsequent speech recognition steps. We propose a language classification system that solves the problem in the image field, rather than the sound field. This research identified and implemented several low-level features using Mel Frequency Cepstral Coefficients, which extract traits from speech files of four languages (Arabic, English, French, Kurdish) from the database (M2L_Dataset) as the data source used in this research. A Convolutional Neuron Network is used to operate on spectrogram images of the available audio snippets. In extensive experiments, we showed that our model is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages, while maintaining classification accuracy. We released our own code and extensive training package for language classification systems for the community. CNN algorithm was applied in this research to classify and the result was perfect, as the classification accuracy reached 97% between two languages if the sample length was only one second, but if the sample length was two seconds, the classification accuracy reached 98%. While the classification among three languages, the classification accuracy reached 95% if the sample length was only one second, but if the sample length was two seconds, the classification accuracy reached 96%.

Detection of Mammogram Using Improved Watershed Segmentation Algorithm and Classifying with Feed Forward Neural Network (FNN)

Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all circumstances. Though this is very perceptive to tiny fluctuations in the size of the image, large numbers of areas are produced unacceptably, and the boundaries after segmentations are also quite hard. The second level set is an effective method of segmenting all types of medical images because; it easily flows with, cavities, folds, splits, and merges. To make the recognition step easier and more accurate, the result of segmentation is considered the beginning position of the curve, and the same will be used at the next step of the level set. This produces a closed, smooth, and accurately placed contour or surface. As a result, the present research uses watershed segmentation to isolate tumor regions and performs classification using Feed Forward Neural Network (FNN) to extract features for classification. Experimental results are evaluated based on performance and quality analysis. In the classification process, the study obtained an accuracy rate of 91.2% in the learning model and 71.8% in a testing model.

Evaluating the Impact of Voice Activity Detection on Speech Emotion Recognition for Autistic Children

Individuals with autism are known to face challenges with emotion regulation, and express their affective states in a variety of ways. With this in mind, an increasing amount of research on automatic affect recognition from speech and other modalities has recently been presented to assist and provide support, as well as to improve understanding of autistic individuals' behaviours. As well as the emotion expressed from the voice, for autistic children the dynamics of verbal speech can be inconsistent and vary greatly amongst individuals. The current contribution outlines a voice activity detection (VAD) system specifically adapted to autistic children's vocalisations. The presented VAD system is a recurrent neural network (RNN) with long short-term memory (LSTM) cells. It is trained on 130 acoustic Low-Level Descriptors (LLDs) extracted from more than 17 h of audio recordings, which were richly annotated by experts in terms of perceived emotion as well as occurrence and type of vocalisations. The data consist of 25 English-speaking autistic children undertaking a structured, partly robot-assisted emotion-training activity and was collected as part of the DE-ENIGMA project. The VAD system is further utilised as a preprocessing step for a continuous speech emotion recognition (SER) task aiming to minimise the effects of potential confounding information, such as noise, silence, or non-child vocalisation. Its impact on the SER performance is compared to the impact of other VAD systems, including a general VAD system trained from the same data set, an out-of-the-box Web Real-Time Communication (WebRTC) VAD system, as well as the expert annotations. Our experiments show that the child VAD system achieves a lower performance than our general VAD system, trained under identical conditions, as we obtain receiver operating characteristic area under the curve (ROC-AUC) metrics of 0.662 and 0.850, respectively. The SER results show varying performances across valence and arousal depending on the utilised VAD system with a maximum concordance correlation coefficient (CCC) of 0.263 and a minimum root mean square error (RMSE) of 0.107. Although the performance of the SER models is generally low, the child VAD system can lead to slightly improved results compared to other VAD systems and in particular the VAD-less baseline, supporting the hypothesised importance of child VAD systems in the discussed context.

Improved Binary Adaptive Wind Driven Optimization Algorithm-Based Dimensionality Reduction for Supervised Classification

Typically, Feature Selection (FS) is adopted as a critical preprocessing step in most pattern recognition and data mining tasks. It helps to avoid the acute impact of irrelevant and redundant features on the performance of the classification model under consideration. To tackle this problem, researchers have proposed different methods for selecting the most significant features to improve the overall classification accuracy for a given dataset by extracting relevant information. Realistically, for a given dataset with a large number of features, conventional methods usually struggle to find good solutions. Therefore, in this study, a meta-heuristic algorithm called the Wind Driven Optimization (WDO) is enhanced and then cloned into a binary variant, the improved Binary Adaptive WDO (iBAWDO). The proposed iBAWDO would manage to select the most relevant (near-optimal) features while reducing the computational cost and enhancing (or even maintaining) the final classification accuracy. An evolutionary crossover technique as well as the Simulated Annealing algorithm (SA) are incorporated into the original WDO algorithm to enhance its search ability for feasible regions as well as exploitation within these regions, respectively. To assess the relevance of the features selected, two popular classifiers, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM), are adopted as fitness evaluators. The proposed iBAWDO algorithm was validated on 18 multi-scale benchmark datasets against binary versions of 11 well-known meta-heuristic approaches: Binary version of the original WDO (BWDO), Binary Particle Swarm Optimization (BPSO), Binary Bat Algorithm (BBA), Binary Grey Wolf Optimization (BGWO), Binary Whale Optimization Algorithm (BWOA), Binary Grasshopper Optimization Algorithm (BGOA), Binary Sailfish Optimizer (BSFO), Binary Harris Hawks optimization (BHHO), Binary Bird Swarm Algorithm (BBSA), Binary Atom Search Optimization (BASO), and Binary Henry Gas Solubility Optimization (BHGSO). A Wilcoxon’s rank-sum non-parametric test was conducted at a 5% significance level to statistically affirm the high competitiveness of the proposed method. Overall, the experimental results revealed that the proposed method is significantly effective on both small- and large-dimensional datasets.

Deep Learning Based Data Fusion Methods for Multimodal Emotion Recognition

Multimodal emotion recognition is a robust and reliable method as it utilizes multimodal data for more comprehensive representation of emotions. Data fusion is a key step in multimodal emotion recognition, because the accuracy of the recognition model mostly depends on how the different modalities are combined. The goal of this paper is to compare the performances of deep learning (DL) based models for the task of data fusion and multimodal emotion recognition. The contributions of this paper are two folds: 1) We introduce three DL models for multimodal fusion and classification: early fusion, hybrid fusion, and multi-task learning. 2) We systematically compare the performance of these models on three multimodal datasets. Our experimental results demonstrate that multi-task learning achieves the best results across all modalities; 75.41%, 68.33%, and 78.75% for classification of three emotional states using the combinations of audio-visual, EEG-audio, and EEG-visual data, respectively.

Histidine detection and signal amplification based on magnetic molecularly imprinted particle and Cu2+ coordination

Magnetic molecularly imprinted (MMIP) particle-based electrochemical sensors are promising for wide detection applications. However, current sensing strategies mainly rely on electrochemical active analytes because of the lack of signal conversion and amplification methods. Herein, a novel histidine (His) sensor was designed based on MMIP particle and metal coordination of Cu2+ with His and cysteine (Cys). The whole determination processes were separated into recognition, coordination and detection steps. The target His in complicated samples can be selectively captured by the MMIP particles to ensure the specificity, then coordinated with Cu2+ and detected at magnetic electrodes under a specific condition to avoid contamination. Highly chemically stable MMIP particles can be prepared by the sol-gel method in large quantities at a very low cost. Additionally, the signal of Cu2+ can be significantly amplified using Cys modified Au nanoparticles. The optimized detection conditions were investigated by voltammetry. The sensor allowed His determination in a wide concentration range over 5-orders of magnitude with a low detection limit of 0.01 μM, which also expressed a promising performance in spiked water and serum samples. Accordingly, the proposed sensing strategy is beneficial for the design and preparation of novel MMIP particle-based electrochemical sensors.