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Objective: With the scenario of limited labeled dataset, this paper introduces a deep learning-based approach that leverages Diabetic Retinopathy (DR) severity recognition performance using fundus images combined with wide-field swept-source optical coherence tomography angiography (SS-OCTA). Methods: The proposed architecture comprises a backbone convolutional network associated with a Twofold Feature Augmentation mechanism, namely TFA-Net. The former includes multiple convolution blocks extracting representational features at various scales. The latter is constructed in a two-stage manner, i.e., the utilization of weight-sharing convolution kernels and the deployment of a Reverse Cross-Attention (RCA) stream. Results: The proposed model achieves a Quadratic Weighted Kappa rate of 90.2% on the small-sized internal KHUMC dataset. The robustness of the RCA stream is also evaluated by the single-modal Messidor dataset, of which the obtained mean Accuracy (94.8%) and Area Under Receiver Operating Characteristic (99.4%) outperform those of the state-of-the-arts significantly. Conclusion: Utilizing a network strongly regularized at feature space to learn the amalgamation of different modalities is of proven effectiveness. Thanks to the widespread availability of multi-modal retinal imaging for each diabetes patient nowadays, such approach can reduce the heavy reliance on large quantity of labeled visual data. Significance: Our TFA-Net is able to coordinate hybrid information of fundus photos and wide-field SS-OCTA for exhaustively exploiting DR-oriented biomarkers. Moreover, the embedded feature-wise augmentation scheme can enrich generalization ability efficiently despite learning from small-scale labeled data.

Nowadays, enhancing the living standard with smart healthcare via the Internet of Things is one of the most critical goals of smart cities, in which artificial intelligence plays as the core technology. Many smart services, deployed according to wearable sensor-based physical activity recognition, have been able to early detect unhealthy daily behaviors and further medical risks. Numerous approaches have studied shallow handcrafted features coupled with traditional machine learning (ML) techniques, which find it difficult to model real-world activities. In this work, by revealing deep features from deep convolutional neural networks (DCNNs) in fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming the raw signal value to pixel intensity value, segmentation data acquired from a multisensor system are encoded to an activity image for deep model learning. Formulated by several novel residual triple convolutional blocks, the proposed DCNN allows extracting multiscale spatiotemporal signal-level and sensor-level correlations simultaneously from the activity image. In the fusion model, the hybrid feature merged from the handcrafted and deep features is learned by a multiclass support vector machine (SVM) classifier. Based on several experiments of performance evaluation, our fusion approach for activity recognition has achieved the accuracy over 96.0% on three public benchmark data sets, including Daily and Sport Activities, Daily Life Activities, and RealWorld. Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems.

The use of metal ions to bridge the fluorescent materials to target analytes has been demonstrated to be a promising way to sensor design. Herein, the effect of rare-earth ions on the fluorescence of l-methionine-stabilized gold nanoclusters (Met-AuNCs) was investigated. It was found that europium (Eu3+) can significantly suppress the emission of Met-AuNCs, while other rare-earth ions showed a negligible impact. The mechanism on the observed fluorescence quenching of Met-AuNCs triggered by Eu3+ was systematically explored, with results revealing the dominant role of photoinduced electron transfer (PET). Eu3+ can bind to the surface of Met-AuNCs by the coordination effect and accepts the electron from the excited Met-AuNCs, which results in Met-AuNC fluorescence suppression. After introducing dipicolinic acid (DPA), an excellent biomarker for spore-forming pathogens, Eu3+ was removed from the surface of Met-AuNCs owing to the higher binding affinity between Eu3+ and DPA. Consequently, an immediate fluorescence recovery occurred when DPA was present in the system. Based on the Met-AuNC/Eu3+ ensemble, we then established a simple and sensitive fluorescence strategy for turn-on determination of biomarker DPA, with a linear range of 0.2-4 μM and a low limit of detection of 110 nM. The feasibility of the proposed method was further validated by the quantitative detection of DPA in the soil samples. We believe that this study would significantly facilitate the construction of metal-ion-mediated PET sensors for the measurement of various interested analytes by applying fluorescent AuNCs as detection probes.

As perception-related applications are of great importance in industrial production and daily life nowadays, solutions for understanding given images semantically receive numerous attention from the literature. To this end, significant accomplishments have been reached for such pixel-wise segmentation problem thanks to novel manipulations of integrating global context into local details in convolutional neural networks. However, this strategy in the existing work did not exhaustively exploit middle-level features, which carry reasonable balance between fine-grained and semantic information. Therefore, this paper introduces a Cross-Attentional Bracket-shaped Convolutional Network (CAB-Net) to leverage their contribution to the tournament of constructing pixel-wise labeled map. In concrete, fine-to-coarse feature maps of interest from the backbone network are densely combined by an efficient fusion of channel-wisely and spatially attentional schemes in crossing manner, namely Cross-Attentional Fusion, to embed semantically rich features into finer patterns. Continuously, these newly decoded outputs repeat the same procedure round-by-round until shaping a final feature map having finest resolution for complete scene understanding. Consequently, the proposed CAB-Net achieves competitive mean Intersection of Union performance on PASCAL VOC 2012 (83.6% without MS-COCO pretraining), CamVid (76.4%) and Cityscapes (78.3%) datasets.

Hybrid excited (HE) machines have the synergies of high torque density and high efficiency of permanent magnet (PM) machines and excellent flux controllability of wound-field machines. The PMs and field coils (FCs) coexist in HE machines to provide excitations simultaneously, and can be coupled either in series or parallel. The topologies of HE machines are very diverse, which bring challenges in the selection and design of HE machines. Based on the partitioned stator (PS) platform, a pair of series and parallel HE machines having similar structures and PM usage are evaluated and compared to identify their merits and demerits. With the help of finite element analysis, the flux regulation, energy conversion capability, parasitic effects and irreversible demagnetization of the two kinds of the HE machines are comprehensively investigated and the guidance to the machine design is provided. A pair of prototypes are fabricated and tested to validate the analyses.

The torque production mechanism of switched reluctance machines (SRMs) is investigated from the perspective of air-gap field modulation principle. The unipolar stator current excitations can be divided into DC and AC components and they induce the air-gap magneto-motive force (MMF) respectively. Meanwhile, thanks to the salient structure in SRMs, the air-gap MMFs induced by the two sources above can be modulated to obtain the synchronized air-gap fields for torque production. It is revealed that a multitude of field harmonics contribute to the torque together. The MMF-permeance model is built and the analytical deduction of the field modulation effect is provided, with which the orders of the field harmonics contributing to the torque output can be identified. Moreover, finite element method is employed to verify the predictions on various SRM topologies. Finally, aprototype machine is fabricated and measuredfor validation.

In this article, the flux reversal permanent magnet (FRPM) machine with toroidal windings is presented and analyzed with reference to conventional concentrated windings. First, the influence of rotor pole numbers on FRPM machines with conventional concentrated windings is investigated. It is found that although the 12/13 stator slot/rotor pole FRPM machine has a high back electromotive force, the pitch factor, and magnet flux utilization ratio of concentrated windings are very low. By adopting toroidal windings instead of concentrated windings in the 12/13 FRPM machine, the pitch factor and resulted machine performance can be effectively improved. Finite-element analysis shows that the 12/13 FRPM machine with toroidal windings can produce 79% higher torque than its counterpart with concentrated windings. Then, the critical design parameters and detailed electromagnetic performance of FRPM machines with both winding types are analyzed and compared. It is shown that the machine with toroidal windings has higher torque, efficiency, power factor, and lower torque ripple, making it particularly suitable for low-speed, high-torque, and axial length limited applications. In addition, other feasible stator slot/rotor pole combinations to use toroidal winding are identified, with the aid of equivalent pole-pair number. Finally, two 12/13 FRPM prototypes with toroidal windings and concentrated windings are tested to validate the analyses.

For person identification in non-interaction biometric systems, gait recognition has been recently encouraged in literature and industrial applications instead of face recognition. Although numerous advanced methods that learn object appearance by conventional machine learning models have been discussed in the last decade, most of them are strongly sensitive to scene background motion. In this research, we address the drawbacks of existing works by comprehensively studying gait information from 3D human skeleton data with a deep learning-based identifier. To capture the statistic gait information in the spatial dimension, we first extract the geometric gait features of joint distance and orientation. The dynamic gait information is then obtained by calculating the temporal description features with the mean and standard deviation of geometric features. Accordingly, the fully gait information of an individual is finally learned via a compact Deep Convolutional Neural Network which is explicitly designed with multiple stacks of asymmetric convolutional filters to fully gain the spatial correlation of in-frame body joints and the temporal relation of frame-wise posture at multiple scales. Based on the experimental results evaluated on four benchmark 3D gait datasets commonly used for person identification, including UPCV Gait, UPCV Gait K2, KS20 VisLab Multi-View Kinect Skeleton, and SDUGait, the proposed method presents the superior performance over that of several state-of-the-art approaches while maintaining a low computational capacity.

Recently, numerous methods have been introduced for three-dimensional (3-D) action recognition using handcrafted feature descriptors coupled traditional classifiers. However, they cannot learn high-level features of a whole skeleton sequence exhaustively. In this paper, a novel encoding technique—namely, pose feature to image (PoF2I), is introduced to transform the pose features of joint–joint distance and orientation to color pixels. By concatenating the features of all skeleton frames in a sequence, a color image is generated to depict spatial joint correlations and temporal pose dynamics of an action appearance. The strategy of end-to-end fine-tuning a pretrained deep convolutional neural network, which completely capture multiple high-level features at multiscale action representation, is implemented for learning recognition models. We further propose an efficient data augmentation mechanism for informative enrichment and overfitting prevention. The experimental results on six challenging 3-D action recognition datasets demonstrate that the proposed method outperforms state-of-the-art approaches.

This letter proposes a cost-efficient convolutional neural network (CNN) for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems. The network architecture is designed with several specific convolutional blocks to concurrently learn the spatiotemporal signal correlations via different asymmetric convolution kernels. Additionally, these blocks are associated with skip connections to preserve more initially residual information at multi-scale feature maps and prevent the vanishing gradient problem. In the experiments, MCNet reaches the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.

Nanozymes, or nanomaterials that mimic the behaviors of enzymes, are highly promising materials for biomedical applications because of their excellent chemical stability under harsh conditions, simple preparation method and lower costs compared with natural enzymes. We herein report the intrinsic oxidase-mimicking activity of molybdenum oxide nanoparticles (MoO3 NPs). MoO3 NPs catalyzed the oxidation of colorless 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) to green product. The catalytic mechanism of the oxidase-mimicking activity of the MoO3 NPs was investigated in detail using electron spin resonance and a radical inhibition method. The oxidation of ABTS stems from 1O2 generated from the interaction between MoO3 NPs and dissolved oxygen in the solution. Acid phosphatase (ACP) catalyzes the hydrolysis of the ascorbic acid 2-phosphate (AAP) substrate to produce ascorbic acid (AA). AA was found to fade the coloration process of the MoO3 NP-mediated ABTS oxidation. By combining the oxidase-mimicking property of the MoO3 NPs and the ACP-catalyzed hydrolysis of AAP, a novel and simple colorimetric method for detecting ACP was established. The linear range for ACP determination is 0.09-7.3 U/L with a detection limit of 0.011 U/L. This new colorimetric method was successfully applied to the detection of ACP in diluted human serum samples and screening of ACP inhibitors. The present study proposes MoO3 NPs as a new oxidase mimic for establishing various biosensing method.

Protein-supported nanoparticles have a great significance in scientific and nanotechnology research because of their “green” process, low cost-in-use, good biocompatibility, and some interesting pr...

Recently, skeleton-based human action recognition has received more interest from industrial and research communities for many practical applications thanks to the popularity of depth sensors. A large number of conventional approaches, which have exploited handcrafted features with traditional classifiers, cannot learn high-level spatiotemporal features to precisely recognize complex human actions. In this paper, we introduce a novel encoding technique, namely Pose-Transition Feature to Image (PoT2I), to transform skeleton information to image-based representation for deep convolutional neural networks (CNNs). The spatial joint correlations and temporal pose dynamics of an action are exhaustively depicted by an encoded color image. For learning action models, we fine-tune end-to-end a pre-trained network to thoroughly capture multiple high-level features at multi-scale action representation. The proposed method is benchmarked on several challenging 3D action recognition datasets (e.g., UTKinect-Action3D, SBU-Kinect Interaction, and NTU RGB+D) with different parameter configurations for performance analysis. Outstanding experimental results with the highest accuracy of 90.33% on the most challenging NTU RGB+D dataset demonstrate that our action recognition method with PoT2I outperforms state-of-the-art approaches.

Ongoing efforts have contributed to the exploration of peroxidase-like nanozymes while preserving their benefits in bioassays. Unfortunately, many peroxidase-like nanozymes still exhibit a low catalytic activity accompanied by oxidase-like activity with interferences of O2 and color which limit their applicability. Herein, we carried out a “green” approach to synthesize citrate-coated osmium nanozyme with a high peroxidase-like performance. Comprehensive experiments were validated in which the Os nanozyme could achieve excellent steady-state kinetic parameters and a high specific activity (393 000 Unit per g), which was better than those of most reported nanozymes. More significantly, the Os nanozyme possesses a higher peroxidase-like activity and negligible oxidase-like activity compared with those of common noble metal-based nanozymes (Pt and Au), freeing the corresponding assay system from the interference of O2 and color. In addition, the Os nanozyme presents robustness against complex environments and some metal ions that usually act as noble metal-based nanozymes. As a proof-of-concept, the Os nanozyme has been successfully employed to fabricate colorimetric assays for glucose and pyruvic acid detections with the associated color gradients. These findings collectively prove that the Os nanozyme could be a novel and promising peroxidase mimic candidate for bioassays and beyond.

Although numerous sensors have been successfully fabricated for the detection of various heavy metal ions by employing fluorescent gold nanoclusters (AuNCs) as nanoprobes, serious cross-interference often occurs when these ions coexist in samples, which results in glaring errors in quantification. In this study, glutathione-protected AuNCs (GSH-AuNCs) were synthesized and found to respond to both Cu2+ and Hg2+ via fluorescence suppression. Intriguingly, addition of Ag+ to GSH-AuNCs could completely inhibit the quenching effect of Hg2+ while not affecting the Cu2+-mediated quenching process. Ag+ can combine with Au+ on the surface of AuNCs to form a strong Ag+-Au+ metallic bond, which disrupts the interaction between Hg2+ and Au+ and thus eliminates the corresponding quenching effect. Based on this phenomenon, a simple sensing approach for highly selective and sensitive detection of Cu2+ in aqueous solution was developed using the GSH-AuNC/Ag+ complex as a fluorescent turn-off nanoprobe. The proposed method exhibited good linearity in the concentration range 0.02–10 μM with a limit of detection of 12 nM. This assay was demonstrated to be suitable for determination of Cu2+ in real water samples even in the presence of Hg2+, showing great promise as a tool for assessment of environmental security and drinking water quality.

The development of efficient methods for the detection of hazardous and toxic elements is extremely important for environmental security and public health. In this work, we developed a facile colorimetric assaying system for Ag+ detection in aqueous solution. Chitosan-stabilized platinum nanoparticles (Ch-PtNPs) were synthesized and severed as an artificial oxidase to catalyze the oxidation of the substrate 3,3′,5,5′-tetramethylbenzidine (TMB) and generate color signal. In the presence of Ag+, due to the strong metallophilic interactions between Ag+ and Pt2+ on the surface of Ch-PtNPs, Ag+ can weaken the affinity to the substrates and inactivate the catalytic activity of Ch-PtNPs, leading to decreased absorbance signal to varying degrees depending on Ag+ amount. Combing the specific binding between Ch-PtNPs and Ag+ with signal amplification procedure based on the Ch-PtNPs-catalyzed TMB oxidation, a sensitive, selective, simple, cost-effective, and rapid detection method for Ag+ can be realized. Ag+ ions in tap and lake waters have been successfully detected. We ensured that the proposed method can be a potential alternative for Ag+ determination in environmental samples.

Few-atom gold nanoclusters (AuNCs) have been fabricated and used for various fields owing to their remarkable optical and photophysical features. However, the rational design for the antibody-mediated synthesis of fluorescent AuNCs for direct antigen-antibody reactions remains unexplored. In this work, immunoglobulin G (IgG)-functionalized AuNCs (IgG-AuNCs) were successfully prepared via a facile and fast biomineralization process. The generated IgG-AuNCs can emit intense red fluorescence with a high photoluminescence quantum yield. Besides strong emission, the bioactivity of IgG on the IgG-AuNCs can be retained. Surface plasmon resonance measurements suggested that IgG-AuNCs can bind to goat anti-human IgG with an affinity constant of 6.21 × 10-8 M. A simple detection method was then developed using a dot-blot immunoassay with IgG-AuNCs as fluorescent tags. Experimental results confirmed that the IgG-AuNC-based fluorescent reporters had many advantages such as low nonspecific adsorption and good photostability, offering immense potential for the development of efficient biosensors. This work can be extended to other specific antibodies to produce multifunctional AuNCs and utilized to detect and monitor targeted analytes and biological events of interest.

This report outlines a versatile high-performance electrochemiluminescence (ECL) enzyme-linked immunosorbent assay (ELISA) platform, which combines the merits of high-quantum-yield Au nanocluster (AuNC) probe-based ECL technology, the efficient ECL-resonance energy-transfer (ECL-RET) strategy, and highly sensitive and specific ELISA technology. The ECL detection procedure was performed on a recyclable MnO2/AuNC-modified glassy carbon electrode interface by taking advantage of the ECL-RET between the AuNC probe and MnO2 nanomaterials (NMs) to quench the ECL intensity. The etching of MnO2 NMs by the product of ALP-based ELISA recovers the ECL signal. Notably, the ELISA process and the ECL detection procedure in this system are independent. Thus, the ECL-ELISA system can effectively avoid the influence of complex biological samples, and the ECL efficiency of the AuNC probe can be used readily. As demonstrated on TNF-α, because of the abovementioned characteristics, the ECL-ELISA platform presented an extremely wide dynamic range, with a detection limit of 2 orders lower than ELISA. Moreover, the system was also applicable for ultrahigh sensitive detection of various disease-related proteins and able to detect trace biomarkers in real serum samples. Therefore, this multifunctional ECL assay platform is versatile, facile, ultrasensitive, recyclable, and sufficiently straightforward for trace biomarker detection in complex biological samples. This approach not only enriches the foundational study of ECL devices but also greatly expands the potential application of ECL sensors in biological testing and clinical high-throughput diagnosis.

We present an integer program (IP) for urban design that (1) maximizes the floor area; (2) fills building volume with room templates; (3) encodes translational symmetry in urban layout; and (4) constructs economical urban routes. Regardless that integer programming is intensively studied in operational research (OR), its role in solving geometrical and topological problems in urban design was overlooked. Based on a regular grid, our 0–1 IP formulates the sunlight-gain rules, which give urban sites their shapes, especially for residential projects. With predefined plot templates, the gross floor area (volume) within a given site can be maximized under various sunlight requirements. Subsequently, the IP fills each building volume with 2D/3D room templates. Finally, an IP-based algorithm constructs routes that connect all plots and the site’s entrances to public transportation. Both the classical Steiner tree model and the latest coverage network model are extended to create reasonable routes. In addition, this work extends the concept of special ordered sets (SOS) to encode translational symmetry in urban layouts. Encoding layout symmetry can benefit from the solvers’ SOS2 mechanism in the Branch-and-Bound search algorithm. The results indicate that traditional decision making for cities could be partially automated by IP and an abundance of valid solutions are available for designers.

Hybrid permanent-magnet (PM) variable-flux memory (VFM) machines employ both high coercive force PM, i.e., the constant PM (CPM), and low coercive force PM, i.e., the variable PM (VPM). The high energy-product CPM guarantees the benefits of high torque density of the conventional PM machines, while the PM magnetization state of the VPM is flexibly regulated to match various operation requirements. In fact, the two kinds of PMs can be magnetically connected in either parallel or series, with which the machine characteristics are quite different. In this paper, based on the two-dimensional and three-dimensional finite-element analyses, a pair of VFM machines with parallel and series connections, respectively, are investigated with specific reference to identify their difference in end effects. The comparison results show that the series hybrid PM VFM machine suffers more serious end leakage flux. The prototype machines are manufactured and tested to validate the analyses and predictions.

In this study, a novel partitioned stator hybrid excited machine is proposed, in which the excitation sources including both permanent magnets (PMs) and field windings are allocated on the secondary stator that is separated from the original stator having armature windings. As a result, the available space is boosted to improve the torque density and flux regulation capability. Meanwhile, the rotor is simple without coil or PM. The PMs on the stator is placed on the slot openings between the two neighbour tooth tips, with which the uncontrolled voltage of the proposed machine is limited and it is favoured for safety-critical applications. The machine topology and operating principle are introduced in detail, and the basic electromagnetic performance is evaluated based on finite element method to validate the analysis.

Variable flux memory (VFM) permanent magnet (PM) machines exhibit an additional degree of freedom for control, i.e., PM magnetization state, and thus excellent flux controllability. Moreover, the hybrid PM topologies having variable PM (VPM) with low coercive force and constant PM (CPM) with high coercive force at the same time are employed to improve the torque density and the flux controllability. The parallel and series connections between the two different kinds of PMs are both feasible. Based on equivalent magnetic circuits, two-dimensional finite element analyses and experiments, the VFM machines with these two connection types are investigated and compared in this paper. The results reveal that the VPM with series-connected CPM is beneficial for more stable working point and higher torque density. A pair of VFM prototypes with parallel and series hybrid PMs are manufactured and tested to validate the analyses.

Lithocarpinols A (1) and B (2), a pair of tenellone diastereoisomers with novel fused skeleton were isolated from the deep-sea derived fungus Phomopsis lithocarpus FS508. Their structures were elucidated by comprehensive spectroscopic analyses, X-ray diffraction and quantum molecular calculation. Their plausible biogenetic pathway featured an intriguing carbonyl-ene cyclization. Lithocarpinol A exhibited moderate inhibitory effect against HepG-2 and A549 tumor cell lines with IC50 values of 9.4 μmol/L and 10.9 μmol/L, respectively.

Titanium dioxide has received great attention as a photocatalyst. However, due to the relatively large bandgap energy of TiO2 and fast recombination of photogenerated electron–hole pairs, various a...

Our scanning tunneling microscopy (STM) study observes, for the first time, twin domain boundary (TDB) formations on the surface of WTe2 single crystal, which is glued by solidifying indium to Si substrate. In these TDB regions, a large inhomogeneous strain field, especially a critical shear strain of about 7%, is observed by geometric phase analysis. This observation does not obey the old believe that a small mechanical stress is sufficient to drive thermally-induced TDB formations in two-dimensional materials. To resolve the contradiction, we perform density functional theory calculations combined with elasticity theory analysis, which show that TDBs on WTe2 are entirely displacement-induced, for which a critical strain is necessary to overcome the onset barrier.

The sensitive analysis of DNA is of extreme importance in the field of clinical diagnosis, targeted therapeutics, gene therapy, and a variety of biomedical studies. Even though significant achievements have been made in the detection of single-stranded DNA (ssDNA), facile strategy for the detection of double-stranded DNA (dsDNA) is still lacking. In this study, we have constructed an electrochemical DNA sensor based on homogeneous hybridization. Target dsDNA is thermal denatured and hybridized with thiolated capture probe and biotin-labeled reporter probe in solution phase. Excessive amount of the probes and precise control of hybridization temperature ensure the predominant formation of sandwich SH-dsDNA-biotin rather than reannealing product of the target dsDNA. A bovine serum albumin (BSA)-modified gold surface is designed to improve self-assembly capability of the sandwich SH-dsDNA-biotin structure in the presence of excessive SH-ssDNA probe. Thus, the target dsDNA can be successfully detected by using enzyme-linked amperometric amplification. By employing this strategy, dsDNA can be detected in a linear range from 5 pM to 1 nM with a detection limit down to 800 fM (S/N = 3). Cryptococcus neoformans in clinical real samples from cryptococcal meningitis patients can be discriminated successfully by the proposed method. Our research would make the electrochemical biosensor be an excellent candidate for pathogens detection in clinical diagnosis and prognosis.

Persistent high-risk human papillomavirus (HPV) infection is the leading cause of cervical cancer. Efficient detection of HPV16 E7 is necessary for early diagnosis and cure of the disease. Here, a novel and high-performance Au nanocluster (AuNC) probe-based split-type electrochemiluminescent (ECL) assay platform has been established to detect these oncogenes, in which the nucleic acid hybridization assay and the ECL measurements are performed independently. The proposed approach combines superior magnetic nanobead enrichment and separation technology, specific nucleic acid hybridization technology, and high-efficiency AuNC probe ECL strategy, and shows excellent advantages. First, the split-type ECL sensing platform can effectively avoid interference from biological samples and adequately uses the ECL efficiency of the AuNC probe. Furthermore, the ultrahigh sensitivity assay of HPV DNA can be achieved without any complex nucleic acid amplification technique. Taking advantage of the above merits of split-type detection, the ECL DNA sensor achieved ideal low detection of 6.8 aM and a wide dynamic range bridging 10 orders of magnitude HPV16 E7. Furthermore, together with its favorable and powerful specificity, high sensitivity, and good selectivity, this strategy could detect HPV16 E7 DNA in human samples, which showed great consistency with the FDA-approved approach (Hybrid capture 2, HC2). Therefore, this work proposes a facile and reliable split-type ECL platform for HPV diagnosis and shows great potential for the early diagnosis of other diseases.

In this decade, although numerous conventional methods have been introduced for three-dimensional (3D) skeleton-based human action recognition, they have posed a primary limitation of learning a vulnerable recognition model from low-level handcrafted features. This paper proposes an effective deep convolutional neural network (CNN) with a dual-stream architecture to simultaneously learn the geometric-based static pose and dynamic motion features for high-performance action recognition. Each stream consists of several advanced blocks of regular and grouped convolutional layers, wherein various kernel sizes are configured to enrich representational features. Remarkably, the blocks in each stream are associated via skip-connection scheme to overcome the vanishing gradient problem, meanwhile, the blocks of two stream are jointly connected via a customized layer to partly share high-relevant knowledge gained during the model training process. In the experiments, the action recognition method is intensively evaluated on the NTU RGB+D dataset and its upgraded version with up to 120 action classes, where the proposed CNN achieves a competitive performance in terms of accuracy and complexity compared to several other deep models.

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.

With a rapid advancement of nanotechnology and the close integration of disciplines, research on nanozymes (nanomaterials with enzyme-like activities), is becoming an expeditiously developing field. In recent years, platinum group element-based (Pt, Pd, Ru, Rh, Ir, and Os) nanozymes developed successively, have not only promoted the research of nanozymes but also expanded the biomedical applications of nanomaterials. Generally speaking, platinum group element-based nanozymes process high catalytic efficiency, specific surface area, stability, and other physical/chemical properties, which benefit for their applications in biosensing, biological medicine, biomedical imaging, and environmental protection. This paper will introduce the research progress of platinum group element-based nanozymes including their synthesis, characterization, enzyme-like activities, stability, biocompatibility, toxicity, and applications for biological detection and clinical relevance. Our emphasis is put on unfolding the roles of platinum group element-based nanozymes in biomedical applications and how they overcome the limitations. Last but not least, trends and future perspectives of platinum group element-based nanozymes in biomedical applications are also provided.

Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification.

There is a continuing high demand to design effective sensors for the determination of heavy metal ions (HMIs) since they are hazardous to both human health and the environment. In this study, we reported a facile fluorescent sensor array for rapid discrimination of HMIs based on a single gold nanocluster (AuNC) probe. This AuNC probe was prepared by using 2-mercapto-1-methylimidazole (MMI) as a ligand and polyvinypyrrolidone (PVP) as a dispersing agent. The fluorescence emission of PVP/MMI-AuNC was observed to be closely related to the pH value of the aqueous solution, which displays yellow (λmax = 512 nm) and red (λmax = 700 nm) fluorescence at pH 12.0 and 6.0, respectively. Further experiments indicated that different HMIs can produce differential effects on the photoluminescence of PVP/MMI-AuNC and thus generate distinct fluorescent responses at 512 and 700 nm. On the basis of this phenomenon, a fluorescent sensor array based on the PVP/MMI-AuNC was then built by simply changing pH value in the sensor element. A total of seven HMIs had their unique response patterns and were successfully distinguished by hierarchical cluster analysis and linear discriminant analysis both in buffer solution and spiked water samples, achieving 100% identification accuracy. This study provides a simple and powerful fingerprinting sensing platform for multiple HMIs, showing broad application prospects in the field of environmental monitoring.

Platinum nanozymes exhibiting highly efficient and robust oxidase-like activity are successfully synthesized and modified using sodium alginate (SA-PtNPs). According to a steady-state dynamic assay, Michaelis-Menton constant (K m ) is calculated as 11.6 μM, indicating that the affinity of SA-PtNPs toward the substrate, 3, 3', 5, 5'-tetramethylbenzidine (TMB), is high. It shows in the paper that SA-PtNPs exhibit a significant oxidant effect on substrate-O2 to produce O 2 • - as an oxidase mimic. Moreover, the oxidase-like activity fluctuated slightly under changes in environmental pH and incubation time, implying that SA can increase the dispersibility and stability of PtNPs. A colorimetric assay for oligomeric proanthocyanidins (OPC) was realized given how few explorations of the former there are. We found that the significant inhibitory effect of OPC on the oxidase-like activity is due to the competitive effect between OPC and TMB for binding to the active site of SA-PtNPs, resulting in a color change. Under optimal conditions, the logarithmic value of the chromogenic difference (ΔA450nm) to OPC concentration was linear (4-32.5 μM, r = 0.999) with a limit of detection (LOD) of 2.0 μM. The antioxidant capacity of OPC obtained by the Soxhlet extraction method from grape seeds was 2.85 U/mg. The recovery from the experiment in which OPC was added to grape seeds ranged from 97.0 to 98.6% (RSDs of 0.5-3.4%), suggesting a high accuracy in OPC detection. These findings are important because OPC is an internationally recognized antioxidant that eliminates free radicals in the human body and, therefore, may prevent a variety of diseases. Thus, we envisage that this Pt nanozyme-based assay may be prevalent for antioxidant capacity evaluation and analytical applications.

Diabetic Retinopathy (DR), the complication leading to vision loss, is generally graded according to the amalgamation of various structural factors in fundus photography such as number of microaneurysms, hemorrhages, vascular abnormalities, etc. To this end, Convolution Neural Network (CNN) with impressively representational power has been exhaustively utilized to address this problem. However, while existing multi-stream networks are costly, the conventional CNNs do not consider multiple levels of semantic context, which suffers from the loss of spatial correlations between the aforementioned DR-related signs. Therefore, this paper proposes a Densely Reversed Attention based CNN (DRAN) to leverage the learnable integration of channel-wise attention at multi-level features in a pretrained network for unambiguously involving spatial representations of important DR-oriented factors. Consequently, the proposed approach gains a quadratic weighted kappa of 85.6% on Kaggle DR detection dataset, which is competitive with the state-of-the-arts.

Recently, deep learning (DL) is an innovative machine learning (ML) technique that has gained the outstanding achievements in computer vision and natural language processing. This work takes advantage of DL for effectively handling automatic modulation classification (AMC), which is the fundamental function of numerous cognitive radio-based and spectrum sensing-based applications in many modern communication systems. Concretely, a novel deep convolutional neural network (DCNN) is proposed for learning a classification model from a massive amount of modulated signals, in which the network architecture has several convolutional blocks specialized to simultaneously capture the temporal intra-signal correlations and the spatial inter-signal relations. To this end, each block comprises various convolutional layers of asymmetric convolution kernels, whose outputs are gathered via a concatenation layer. For the enrichment of multi-scale deep feature and the prevention of gradient vanishing problem, these blocks are associated by skip connections to take into account the useful residual information. In experiments, the proposed CNN-based AMC method achieves the overall 24-modulation classification rate of 88.22% at 10dB SNR on the well-known DeepSig dataset.

Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole sequence. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3. In experiments, besides the compatibility verification with various backbone networks, the proposed approach achieves the state-of-theart performance in battle with several deep learning-based methods on the updated NTU RGB+D 120 dataset.

Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms. However, most existing conventional approaches pose the obstacle of effectively learning weakly discriminative modulation patterns. This paper proposes a robust modulation classification method by taking advantage of deep learning to capture the meaningful information of modulation signal at multi-scale feature representations. To this end, a novel architecture of convolutional neural network, namely Chain-Net, is developed with various asymmetric kernels organized in two processing flows and associated via depth-wise concatenation and element-wise addition for optimizing feature utilization. The network is evaluated on a big dataset of 14 challenging modulation formats, including analog and high-order digital techniques. The simulation results demonstrate that Chain-Net robustly classifies the modulation of radio signals suffering from a synthetic channel deterioration and further performs better than other deep networks., Comment: Accepted for presentation at IEEE GLOBECOM 2020

Numerous existing handcrafted feature-based and conventional machine learning-based approaches cannot seize the intensive correlations of skeleton structure in the spatiotemporal dimension. On another hand, some modern methods exploiting Long Short Term Memory (LSTM) to learn temporal action attribute, which lack an efficient scheme of revealing high-level informative features. To handle the aforementioned issues, this research introduces a novel hierarchical deep feature fusion model for 3D skeleton-based human action recognition, in which the deep information for modeling human appearance and action dynamic is gained by Convolutional Neural Networks (CNNs). The deep features of geometrical joint distance and orientation are extracted via a multi-stream CNN architecture to uncovering the hidden correlations in both the spatial and temporal dimensions. The experimental results on the NTU RGB+D dataset demonstrates the superiority of the proposed fusion model against several recently deep learning (DL)-based action recognition approaches.

The emergence of convolutional neural network (CNN) has enabled facial expression recognition to accomplish significant outcomes nowadays. However, while existing multistream networks are subject to costly computation, the attention- embedded approaches do not involve multiple levels of semantic context in the predefined CNN. Based on the observation that emotions via a person's face are fusion of various muscular modalities, relying upon the outputs and corresponding attentional features of the deepest layer in the CNN is insufficient due to loss of informative details through multiple sub-sampling stages. Therefore, this paper introduces a CNN with densely backward attention to leverage the aggregation of channel-wise attention at multi-level features in a backbone network for reaching high recognition performance with cost-effective resource consumption. Particularly, cross-channel semantic information in high-level features are exploited densely to recalibrate finegrained details in low-level versions. Then, a step of multi-level aggregation is further executed for thorougly involving spatial representations of important facial modalities. As a consequence, the proposed approach gains highest mean class accuracy of 79.37% on RAF-DB, which is competitive with the state-of-the- arts.

The spread of illegal replication of digital image has seriously affected the protection of intellectual property rights. Nowadays, although watermarking is the most popular technology for copyright protection and ownership authentication, most of the current works cannot encounter numerous intentional attacks of watermark destruction. In this research, we introduce a novel framework of blind image watermarking that is able to learn attacking patterns effectively based on a deep convolutional encoder-decoder network. For details, a binary watermark image is hidden into selective wavelet blocks by the mean of an optimal encoding rule, wherein the quality image degradation is minimized over the Mean Square Error metric for a significant image imperceptibility enhancement. Then, the embedding maps, defined as the wavelet coefficient difference values, of various attacks simulated as digital image transformations of the watermarked image are revealed for training the deep learning-based watermark extraction model. Accordingly, the watermark information hidden in an attacked image can be precisely recovered from its embedding map by the trained model. From experimental results, the proposed watermark framework achieves a good performance trade-off between image imperceptibility and watermark robustness through an adjustable embedding strength. In addition, our approach is strongly defeats several state-of-the-art methods in terms of watermark robustness against many critical attacks.

Molybdenum oxide quantum dots (MoOx QDs) were synthesized by a one-pot method and used as a versatile probe in an electrochemiluminescent (ECL) immunoassay of the non-small cell lung cancer biomarker cytokeratin 19 fragment 21–1 (CYFRA21-1) as a model analyte. The MoOx QDs exhibited stable and strong cathodic green ECL, with an emission peak at 535 nm, in the presence of K2S2O8 within the potential range of −2.0 to 0 V. On exposure to CYFRA21-1, the ECL decreases because of the immunoreaction between CYFRA21-1 and its antibody which generates a barrier for electron transfer. The determination of CYFRA21-1 with favorable analytical performances was successfully realized under the optimal conditions. ECL decreases linearly in the 1 pg mL−1 to 350 ng mL−1 CYFRA21-1 concentration range, and the detection is as low as 0.3 pg mL−1. Excellent recoveries from CYFRA21-1-spiked human serum indicate that the assay can be operated under physiological conditions.

Epothilones are a kind of macrolides with strong cytotoxicity toward cancer cells and relatively lower side effects compared with taxol. Epothilone B derivate ixabepilone has been used for the clinical treatment of advanced breast cancer. However, the low yield of epothilones and the difficulty in the genetic manipulation of Sorangium cellulosum limited their wider application. Transcription activator-like effectors-Trancriptional factor (TALE-TF)-VP64 and clustered regularly interspaced short palindromic repeats (CRISPR)/dCas9-VP64 have been demonstrated as effective systems for the transcriptional improvement. In this study, a promoter for the epothilone biosynthesis cluster was obtained and the function has been verified. The TALE-TF-VP64 and CRISPR/dcas9-VP64 target P3 promoter were electroporated into S. cellulosum strain So ce M4, and the transcriptional levels of epothilone biosynthesis-related genes were significantly upregulated. The yield of epothilone B was improved by 2.89- and 1.53-fold by the introduction of recombinant TALE-TF-VP64-P3 and dCas9-VP64-P3 elements into So ce M4, respectively. The epothilone D yield was also improved by 1.12- and 2.18-fold in recombinant dCas9-So ce M4 and TALE-VP64 strains, respectively. The transcriptional regulation mechanism of TALE-TF-VP64 and the competition mechanism with endogenous transcriptional factor were investigated by electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP), demonstrating the combination of the P3 promoter and TALE-TF element and the competition between TALE-TF and endogenous transcriptional protein. This is the first report on the transcriptional regulation of the epothilone biosynthetic gene cluster in S. cellulosum using the TALE-TF and dCas9-VP64 systems, and the regulatory mechanism of the TALE-TF system for epothilone biosynthesis in S. cellulosum was also firstly revealed, thus shedding light on the metabolic engineering of S. cellulosum to improve epothilone yields substantially and promoting the application of epothilones in the biomedical industry.

Gold nanoclusters (AuNCs) have attracted much attention as signal transducers in photoluminescence chemical/biological sensors. Herein, we employ bovine serum albumin/3-mercaptopropionic acid co-modified AuNCs as a fluorescence probe, Fe3+ as a quencher, and pyrophosphate as an alkaline phosphatase (ALP) substrate and Fe3+ chelator to design a novel biosensor for ALP detection, achieving a detection linear range of 0.8–16 U/L and a detection limit of 0.78 U/L. The developed method is successfully applied to the detection of ALP in human osteosarcoma cells and is shown to be suited for ALP inhibitor screening.

We present a systematic method for producing double-sided minimal surfaces by wire-cut machines. A link between minimal surfaces and ruled surfaces is pursued through wire cutting. Weierstrass parameterization is employed to define minimal surfaces ( $$\mathbb {R}^3$$ ) over a complex plane ( $$\mathbb {C}$$ ). Our method consists of three components. First, the orthogonal double-sided cuts match a pair of orthonormal tangent vectors on the surface. Second, A closed-form expression for the principal directions facilitates the global quadrangulation of minimal surfaces. Third, the CNC machine’s toolpath results from the surface’s analytic characterization. Asymptotic cutting and principal cutting are compared in terms of collisions and cutting error. We employed a general-purpose language (Java) to create machine instructions from the Weierstrass representation of minimal surfaces. Thus, the entire workflow from mathematical modeling to production involves no 3D models or CAD/CAM software. Both a 5-axis wire cutter and a customized robotic system were tested.

Despite impressive achievements in image processing and artificial intelligence in the past decade, understanding video-based action remains a challenge. However, the intensive development of 3D computer vision in recent years has brought more potential research opportunities in pose-based action detection and recognition. Thanks to the advantages of depth camera devices like the Microsoft Kinect sensor, we developed an effective approach to in-depth analysis of indoor actions using skeleton information, in which skeleton-based feature extraction and topic model-based learning are two major contributions. Geometric features, i.e. joint distance, joint angle, and joint-plane distance are calculated in the spatio-temporal dimension. These features are merged into two types, called pose and transition features, and then are provided to codebook construction to convert sparse features into visual words by k-means clustering. An efficient hierarchical model is developed to describe the full correlation of feature - poselet - action based on Pachinko Allocation Model. This model has the potential to uncover more hidden poselets, which have been recognized as the valuable information and help to differentiate pose-sharing actions. The experimental results on several well-known datasets, such as MSR Action 3D, MSR Daily Activity 3D, Florence 3D Action, UTKinect-Action 3D, and NTU RGB+D Action Recognition, demonstrate the high recognition accuracy of the proposed method. Our method outperforms state-of-the-art methods in the field in most dataset benchmarks.

The rising popularity of data calls and the slowed global economy have posed a challenge to voice data networking—how to satisfy the growing user demand for VoIP calls under limited network resources. In a bandwidth-constrained network in particular, raising the bitrate for one call implies a lowered bitrate for another. Therefore, knowing whether it is worthwhile to raise one call's bitrate while other users might complain is crucial to the design of a user-centric rate control mechanism. To this end, previous work (Chen et al. 2012) has reported a log-like relationship between bitrate and user experience (i.e., QoE) in Skype calls. To show that the relationship extends to more general VoIP calls, we conduct a 60-participant user study via the Amazon Mechanical Turk crowdsourcing platform and reaffirm the log-like relationship between the call bitrate and user experience in widely used AMR-WB. The relationship gives rise to a simple and practical rate control scheme that exponentially quantizes the steps of rate change, therefore the name—exponential quantization (EQ). To support that EQ is effective in addressing the challenge, we show through a formal analysis that the resulting bandwidth allocation is optimal in both the overall QoE and the number of calls served. To relate EQ to existing rate control mechanisms, we show in a simulation study that the bitrates of calls administered by EQ converge over time and outperform those controlled by a (naïve) greedy mechanism and the mechanism implemented in Skype.

In this paper, we propose a novel robust blind color image watermarking method, namely SMLE, that allows to embed a gray-scale image as watermark into a host color image in the wavelet domain. After decomposing the gray-scale watermark to component binary images in digits ordering from least significant bit (LSB) to most significant bit (MSB), the retrieved binary bits are then embedded into wavelet blocks of two optimal color channels by using an efficient quantization technique, where the wavelet coefficient difference in each block is quantized to either two pre-defined thresholds for corresponding 0-bits and 1-bits. To optimize the watermark imperceptibility, we equally split the coefficient modified quantity on two middle-frequency sub-bands instead of only one as in existing approaches. The improvement of embedding rule increases approximately 3 dB of watermarked image quality. An adequate trade-off between robustness and imperceptibility is controlled by a factor representing the embedding strength. As for extraction process, we exploit 2D Otsu algorithm for higher accuracy of watermark detection than that of 1D Otsu. Experimental results prove the robustness of our SMLE watermarking model against common image processing operations along with its efficient retention of the imperceptibility of the watermark in the host image. Compared to state-of-the-art methods, our approach outperforms in most of robustness tests at a same high payload capacity.

High-energy-density enzymatic biofuel cells (EBFCs) are urgently needed not only as green energy conversion devices but also as energy generators for medical devices. Low enzyme loading efficiency and insufficient direct electron transfer (DET) between enzymes and electrodes greatly hamper the development of EBFCs. Herein, we fabricated nitrogen-doped hollow carbon nanospheres with large pores (pNHCSs) via a green microwave-assisted hydrothermal method (MWHM). Due to their three-dimensional structure and the incorporation of nitrogen, the pNHCS-functionalized electrode not only efficiently enriched the load of enzyme (ca. 8.65 × 10−10 mol cm−2) but also significantly facilitated DET rate (ca. 10.73 s−1). Thus, the pNHCS-based EBFCs exhibited an outstanding maximum power output (Pmax), which achieved to 325 ± 0.6 µW cm−2. Then, based on a dual-fuel-driven EBFC, we developed an innovative self-powered biosensor to sense two kinds of cancer-related microRNAs, miR-21 and miR-141, at the same time. The detection limits were as low as 0.1 fM for miR-21 and 4.0 fM for miR-141 (S/N = 3). This material has excellent potential in the development of superior green bio-batteries as well as economical and portable self-powered biomedical sensors.

Visualization technology for network security situation adopts images to present the massive abstract data regarding network events. It reduces the workload of data analysis and benefits the manager to grasp the overall network status and trend. Secret information in the visual image requires confidentiality protection while transmitting. Comparing with some conventional methods realized by complicated encryptions such as DES and AES, we present a novel multilevel protection scheme based on visual cryptography (VC) with the beauty of decryption done only via the human eyes without using more computing devices. Essentially, a region incrementing VC scheme (RIVCS) is proposed in this paper dealing with the encoding of a secret situation image regarding network security. The secret image includes a number of regions, where each region is allocated with a certain secrecy level. Different secrecy levels can be decoded incrementally when different combinations of participants are gained. Firstly, we develop the model called the general AS (GAS) based RIVCS. Secondly, we design the algorithm for allocating secrecy levels. Thirdly, we construct the encoding matrices for sharing the secret pixels. Experimental results show that our method is more suitable to visualization data protection for network security situation with lower cost, higher reliability and richer application scenarios.

The emergence of drug-resistant bacterial strains has challenged the use of traditional antibiotic therapies. Therefore, there is an urgent need to develop novel antibacterial materials or methods to effectively eradicate bacteria. Herein, we demonstrated a novel antibacterial system consisting of cupric oxide nanoparticles combined with ascorbic acid (CuO NPs/AA), which could act as a potent nanoantibiotic targeting both Escherichia coli (gram-negative) and Staphylococcus aureus (gram-positive) in vivo and in vitro, without noticeable cytotoxicity to mammalian tissue. The CuO NP/AA antibacterial system killed these microbes through a combined mechanism involving biofilm inhibition, cell membrane destruction, DNA damage, and enzyme inactivation. The antibacterial properties of CuO NPs/AA were mainly attributed to the generation of reactive oxygen species (e.g., ·OH) produced through the intrinsic ascorbate oxidase- and peroxidase-like activities of CuO NPs, highlighting their use as antibacterial nanozymes to treat bacterial infections.