Image harmonization with Simple Hybrid CNN-Transformer Network.

Image harmonization seeks to transfer the illumination distribution of the background to that of the foreground within a composite image. Existing methods lack the ability of establishing global-local pixel illumination dependencies between foreground and background of composite images, which is indispensable for sharp and color-consistent harmonized image generation. To overcome this challenge, we design a novel Simple Hybrid CNN-Transformer Network (SHT-Net), which is formulated into an efficient symmetrical hierarchical architecture. It is composed of two newly designed light-weight Transformer blocks. Firstly, the scale-aware gated block is designed to capture multi-scale features through different heads and expand the receptive fields, which facilitates to generate images with fine-grained details. Secondly, we introduce a simple parallel attention block, which integrates the window-based self-attention and gated channel attention in parallel, resulting in simultaneously global-local pixel illumination relationship modeling capability. Besides, we propose an efficient simple feed forward network to filter out less informative features and allow the features to contribute to generating photo-realistic harmonized results passing through. Extensive experiments on image harmonization benchmarks indicate that our method achieve promising quantitative and qualitative results. The code and pre-trained models are available at https://github.com/guanguanboy/SHT-Net.

Robust Subcluster Search and Mergence Clustering.

In recent years, graph-based clustering presents outstanding performance and has been widely investigated. It segments the data similarity graph into multiple subgraphs as final clusters. Many methods integrate graph learning and segmentation into a unified optimization problem to explore the graph structure. However, existing research 1) attempts to derive the final clusters from the learned graph directly, which relies on a highly tight internal distribution within each cluster, and is too strict for the real-world data; 2) generally constructs a holistic full sample graph, which means the outliers are involved in graph learning explicitly, and may corrupt the graph quality. To overcome the above limitations, a new clustering model called robust subcluster search and mergence (RSSM) is established in this article. Inspired by the positive-incentive noise (Pi-Noise), RSSM assumes that the outliers are useful for learning the data structure. Considering a few samples with large errors as outliers, RSSM finds the subcentroids by searching an imbalanced residue distribution. In this way, the subcentroids pull the normal samples together and push the outliers far away. Compared with the traditional clusters, the subclusters indicated by the subcentroids are more explicit, where the normal samples are tightly connected. After that, a subcluster similarity graph is constructed to guide the mergence of subclusters. To sum up, RSSM performs the search and mergence of subclusters simultaneously with the help of outliers, and generates a graph that is more suitable for clustering. Experiments on several datasets demonstrate the rationality and superiority of RSSM.

Spatial Continuity and Nonequal Importance in Salient Object Detection With Image-Category Supervision.

Due to the inefficiency of pixel-level annotations, weakly supervised salient object detection with image-category labels (WSSOD) has been receiving increasing attention. Previous works usually endeavor to generate high-quality pseudolabels to train the detectors in a fully supervised manner. However, we find that the detection performance is often limited by two types of noise contained in pseudolabels: 1) holes inside the object or at the edge and outliers in the background and 2) missing object portions and redundant surrounding regions. To mitigate the adverse effects caused by them, we propose local pixel correction (LPC) and key pixel attention (KPA), respectively, based on two key properties of desirable pseudolabels: 1) spatial continuity, meaning an object region consists of a cluster of adjacent points; and 2) nonequal importance, meaning pixels have different importance for training. Specifically, LPC fills holes and filters out outliers based on summary statistics of the neighborhood as well as its size. KPA directs the focus of training toward ambiguous pixels in multiple pseudolabels to discover more accurate saliency cues. To evaluate the effectiveness of our method, we design a simple yet strong baseline we call weakly supervised saliency detector with Transformer (WSSDT) and unify the proposed modules into WSSDT. Extensive experiments on five datasets demonstrate that our method significantly improves the baseline and outperforms all existing congeneric methods. Moreover, we establish the first benchmark to evaluate WSSOD robustness. The results show that our method can improve detection robustness as well. The code and robustness benchmark are available at https://github.com/Horatio9702/SCNI.

The effect of preoperative supplementary parenteral nutrition on nutrition and inflammation in gastric cancer patients.

OBJECTIVES: Supplemental parenteral nutrition (SPN) is recommended to add when enteral nutrition alone is not sufficient. This research aims to evaluate the effect of preoperative SPN in patients with gastric cancer. METHODS: A total of 180 patients with gastric cancer were divided into three groups (60 patients per group) according to different nutritional support scheme. The primary endpoint was the changes in nutrition and inflammatory, while the secondary endpoint included the changes in prognosis. RESULTS: Compared with the control group, there were significant differences in nutrition and inflammation related indicators in the oral nutrition supplement (ONS) group and the SPN + ONS group (P < 0.05). Compared with the ONS group, the SPN + ONS group showed significant differences in the above indicators (P < 0.05). However, no significant changes were observed in the incidence of complications, the postoperative exhaust time, and the hospitalization time. CONCLUSIONS: Preoperative SPN had a positive effect on nutrition and inflammation of gastric cancer patients undergoing surgery, but had no significant effect on their prognosis.

Co-Clustering by Directly Solving Bipartite Spectral Graph Partitioning.

Bipartite spectral graph partitioning (BSGP) method as a co-clustering method, has been widely used in document clustering, which simultaneously clusters documents and words by making full use of the duality between documents and words. It consists of two steps: 1) graph construction and 2) singular value decomposition on the bipartite graph to compute a continuous cluster assignment matrix, followed by post-processing to get the discrete solution. However, the generated graph is unstructured and fixed. It heavily relies on the quality of the graph construction. Moreover, the two-stage process may deviate from directly solving the primal problem. In order to tackle these defects, a novel bipartite graph partitioning method is proposed to learn a bipartite graph with exactly c connected components (c is the number of clusters), which can obtain clustering results directly. Furthermore, it is experimentally and theoretically proved that the solution of the proposed model is the discrete solution of the primal BSGP problem for a special situation. Experimental results on synthetic and real-world datasets exhibit the superiority of the proposed method.

Tensorized and Compressed Multi-view Subspace Clustering via Structured Constraint.

Multi-view learning has raised more and more attention in recent years. However, traditional approaches only focus on the difference while ignoring the consistency among views. It may make some views, with the situation of data abnormality or noise, ineffective in the progress of view learning. Besides, the current datasets have become high-dimensional and large-scale gradually. Therefore, this paper proposes a novel multi-view compressed subspace learning method via low-rank tensor constraint, which incorporates the clustering progress and multi-view learning into a unified framework. First, for each view, we take the partial samples to build a small-size dictionary, which can reduce the effect of both redundancy information and computation cost greatly. Then, to find the consistency and difference among views, we impose a low-rank tensor constraint on these representations and further design an auto-weighted mechanism to learn the optimal representation. Last, due to the non-square of the learned representation, the bipartite graph has been introduced, and under the structured constraint, the clustering results can be obtained directly from this graph without any post-processing. Extensive experiments on synthetic and real-world benchmark datasets demonstrate the efficacy and efficiency of our method, especially for the views with noise or outliers.

Role and Regulatory Mechanism of circRNA_14820 in the Proliferation and Differentiation of Goat Skeletal Muscle Satellite Cells.

Skeletal muscle satellite cells (SMSCs), a type of myogenic stem cell, play a pivotal role in postnatal muscle regeneration and repair in animals. Circular RNAs (circRNAs) are a distinct class of non-coding RNA molecules capable of regulating muscle development by modulating gene expression, acting as microRNAs, or serving as protein decoys. In this study, we identified circ_14820, an exonic transcript derived from adenosine triphosphatase family protein 2 (ATAD2), through initial RNA-Seq analysis. Importantly, overexpression of circ_14820 markedly enhanced the proliferation of goat SMSCs while concomitantly suppressing their differentiation. Moreover, circ_14820 exhibited predominant localization in the cytoplasm of SMSCs. Subsequent small RNA and mRNA sequencing of circ_14820-overexpressing SMSCs systematically elucidated the molecular regulatory mechanisms associated with circ_14820. Our preliminary findings suggest that the circ_14820-miR-206-CCND2 regulatory axis may govern the development of goat SMSCs. These discoveries contribute to a deeper understanding of circRNA-mediated mechanisms in regulating skeletal muscle development, thereby advancing our knowledge of muscle biology.

A Novel and Effective Method to Directly Solve Spectral Clustering.

Spectral clustering has been attracting increasing attention due to its well-defined framework and excellent performance. However, most traditional spectral clustering methods consist of two separate steps: 1) Solving a relaxed optimization problem to learn the continuous clustering labels, and 2) Rounding the continuous clustering labels into discrete ones. The clustering results of the relax-and-discretize strategy inevitably result in information loss and unsatisfactory clustering performance. Moreover, the similarity matrix constructed from original data may not be optimal for clustering since data usually have noise and redundancy. To address these problems, we propose a novel and effective algorithm to directly optimize the original spectral clustering model, called Direct Spectral Clustering (DSC). We theoretically prove that the original spectral clustering model can be solved by simultaneously learning a weighted discrete indicator matrix and a structured similarity matrix whose connected components are equal to the number of clusters. Both of them can be used to directly obtain the final clustering results without any post-processing. Further, an effective iterative optimization algorithm is exploited to solve the proposed method. Extensive experiments performed on synthetic and real-world datasets demonstrate the superiority and effectiveness of the proposed method compared to the state-of-the-art algorithms.

Risk Factors for Non-Alcoholic Fatty Liver Disease in Patients with Bipolar Disorder: A Cross-Sectional Retrospective Study.

Purpose: The co-morbidity of non-alcoholic fatty liver disease (NAFLD) in patients with bipolar disorder (BD) has a negative impact on patient treatment and prognosis. This study aimed to identify the prevalence of NAFLD in patients with BD and investigate the risk factors of NAFLD. Patients and Methods: A total of 678 patients with BD were included in the study. Clinical data were obtained from the hospital's electronic health record system. Data included fasting blood glucose, alanine aminotransferase, triglycerides, aspartate aminotransferase, high-density lipoprotein cholesterol (HDL), alkaline phosphatase, total cholesterol, glutamine transpeptidase, uric acid, apolipoprotein A1, apolipoprotein B, and liver ultrasound findings. Results: The prevalence of NAFLD was 43.66% in patients with BD. Significant differences in body mass index (BMI), mean age, diabetes prevalence, course of BD, fasting blood glucose, alanine aminotransferase, HDL, alkaline phosphatase, triglycerides, aspartate aminotransferase, uric acid, glutamine transpeptidase, apolipoprotein B, total cholesterol, and apolipoprotein A1 were seen between the groups (all P<0.01). Male sex, age, BMI, course of BD, alanine aminotransferase, fasting blood glucose, aspartate aminotransferase, diabetes, glutamine transpeptidase, total cholesterol, alkaline phosphatase, triglycerides, uric acid, apolipoprotein B, HDL, and apolipoprotein A1 levels were correlated with NAFLD (all P<0.05). In patients with BD, diabetes (OR=6.412, 95% CI=1.049-39.21), BMI (OR=1.398, 95% CI=1.306-1.497), triglycerides (OR=1.456, 95% CI=1.036-2.045), and apolipoprotein A1 (OR=0.272, 95% CI=0.110-0.672) were risk factors for NAFLD (all P<0.05). Conclusion: Risk factors for NAFLD in patients with BD include diabetes, BMI, course of BD, and a low level of apolipoprotein A1. A proactive approach to disease management, such as appropriate physical activity and adoption of a healthy diet, and regular monitoring of changes in patient markers should be adopted to reduce the prevalence of NAFLD.

QKSAN: A Quantum Kernel Self-Attention Network.

The Self-Attention Mechanism (SAM) excels at distilling important information from the interior of data to improve the computational efficiency of models. Nevertheless, many Quantum Machine Learning (QML) models lack the ability to distinguish the intrinsic connections of information like SAM, which limits their effectiveness on massive high-dimensional quantum data. To tackle the above issue, a Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. Further, a Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques to release half of quantum resources by mid-circuit measurement, thereby bolstering both feasibility and adaptability. Simultaneously, the Quantum Kernel Self-Attention Score (QKSAS) with an exponentially large characterization space is spawned to accommodate more information and determine the measurement conditions. Eventually, four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST, where the QKSAS tests and correlation assessments between noise immunity and learning ability are executed on the best-performing sub-model. The paramount experimental finding is that the QKSAN subclasses possess the potential learning advantage of acquiring impressive accuracies exceeding 98.05% with far fewer parameters than classical machine learning models. Predictably, QKSAN lays the foundation for future quantum computers to perform machine learning on massive amounts of data while driving advances in areas such as quantum computer vision.

Four New Species of Tomentella (Thelephorales, Basidiomycota) from Subtropical Forests in Southwestern China.

Species of the basidiomycetous genus Tomentella are widely distributed throughout temperate forests. Numerous studies on the taxonomy and phylogeny of Tomentella have been conducted from the temperate zone in the Northern hemisphere, but few have been from subtropical forests. In this study, four new species, T. casiae, T. guiyangensis, T. olivaceomarginata and T. rotundata from the subtropical mixed forests of Southwestern China, are described and illustrated based on morphological characteristics and phylogenetic analyses of the internal transcribed spacer regions (ITS) and the large subunit of the nuclear ribosomal RNA gene (LSU). Molecular analyses using Maximum Likelihood and Bayesian analysis confirmed the phylogenetic positions of these four new species. Anatomical comparisons among the closely related species in phylogenetic and morphological features are discussed. Four new species could be distinguished by the characteristics of basidiocarps, the color of the hymenophoral surface, the size of the basidia, the shape of the basidiospores and some other features.

Flow to Candidate: Temporal Knowledge Graph Reasoning With Candidate-Oriented Relational Graph.

Reasoning over temporal knowledge graphs (TKGs) is a challenging task that requires models to infer future events based on past facts. Currently, subgraph-based methods have become the state-of-the-art (SOTA) techniques for this task due to their superior capability to explore local information in knowledge graphs (KGs). However, while previous methods have been effective in capturing semantic patterns in TKG, they are hard to capture more complex topological patterns. In contrast, path-based methods can efficiently capture relation paths between nodes and obtain relation patterns based on the order of relation connections. But subgraphs can retain much more information than a single path. Motivated by this observation, we propose a new subgraph-based approach to capture complex relational patterns. The method constructs candidate-oriented relational graphs to capture the local structure of TKGs and introduces a variant of a graph neural network model to learn the graph structure information between query-candidate pairs. In particular, we first design a prior directed temporal edge sampling method, which is starting from the query node and generating multiple candidate-oriented relational graphs simultaneously. Next, we propose a recursive propagation architecture that can encode all relational graphs in the local structures in parallel. Additionally, we introduce a self-attention mechanism in the propagation architecture to capture the query's preference. Finally, we design a simple scoring function to calculate the candidate nodes' scores and generate the model's predictions. To validate our approach, we conduct extensive experiments on four benchmark datasets (ICEWS14, ICEWS18, ICEWS0515, and YAGO). Experiments on four benchmark datasets demonstrate that our proposed approach possesses stronger inference and faster convergence than the SOTA methods. In addition, our method provides a relational graph for each query-candidate pair, which offers interpretable evidence for TKG prediction results.

Motion-Aware Video Frame Interpolation.

Video frame interpolation methodologies endeavor to create novel frames betwixt extant ones, with the intent of augmenting the video's frame frequency. However, current methods are prone to image blurring and spurious artifacts in challenging scenarios involving occlusions and discontinuous motion. Moreover, they typically rely on optical flow estimation, which adds complexity to modeling and computational costs. To address these issues, we introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames by introducing a novel hierarchical pyramid module. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, enabling the model to capture intricate motion patterns, but also effectively reduces the required computational cost and complexity. Subsequently, a cross-scale motion structure is presented to estimate and refine intermediate flow maps by the extracted features. This approach facilitates the interplay between input frame features and flow maps during the frame interpolation process and markedly heightens the precision of the intervening flow delineations. Finally, a discerningly fashioned loss centered around an intermediate flow is meticulously contrived, serving as a deft rudder to skillfully guide the prognostication of said intermediate flow, thereby substantially refining the precision of the intervening flow mappings. Experiments illustrate that MA-VFI surpasses several representative VFI methods across various datasets, and can enhance efficiency while maintaining commendable efficacy.

Nonlinear Locality-Preserving Projections With Dynamic Graph Learning.

The affinity graph is regarded as a mathematical representation of the local manifold structure. The performance of locality-preserving projections (LPPs) and its variants is tied to the quality of the affinity graph. However, there are two drawbacks in current approaches. First, the pre-designed graph is inconsistent with the actual distribution of data. Second, the linear projection way would cause damage to the nonlinear manifold structure. In this article, we propose a nonlinear dimensionality reduction model, named deep locality-preserving projections (DLPPs), to solve these problems simultaneously. The model consists of two loss functions, each employing deep autoencoders (AEs) to extract discriminative features. In the first loss function, the affinity relationships among samples in the intermediate layer are determined adaptively according to the distances between samples. Since the features of samples are obtained by nonlinear mapping, the manifold structure can be kept in the low-dimensional space. Additionally, the learned affinity graph is able to avoid the influence of noisy and redundant features. In the second loss function, the affinity relationships among samples in the last layer (also called the reconstruction layer) are learned. This strategy enables denoised samples to have a good manifold structure. By integrating these two functions, our proposed model minimizes the mismatch of the manifold structure between samples in the denoising space and the low-dimensional space, while reducing sensitivity to the initial weights of the graph. Extensive experiments on toy and benchmark datasets have been conducted to verify the effectiveness of our proposed model.

Multi-input mutual supervision network for single-pixel computational imaging.

In this study, we propose a single-pixel computational imaging method based on a multi-input mutual supervision network (MIMSN). We input one-dimensional (1D) light intensity signals and two-dimensional (2D) random image signal into MIMSN, enabling the network to learn the correlation between the two signals and achieve information complementarity. The 2D signal provides spatial information to the reconstruction process, reducing the uncertainty of the reconstructed image. The mutual supervision of the reconstruction results for these two signals brings the reconstruction objective closer to the ground truth image. The 2D images generated by the MIMSN can be used as inputs for subsequent iterations, continuously merging prior information to ensure high-quality imaging at low sampling rates. The reconstruction network does not require pretraining, and 1D signals collected by a single-pixel detector serve as labels for the network, enabling high-quality image reconstruction in unfamiliar environments. Especially in scattering environments, it holds significant potential for applications.

Allergen Sensitization of Parents and Children With Respiratory Allergic Diseases.

OBJECTIVE: To study the differences in allergen sensitization of parents and their offspring with respiratory allergic diseases. METHODS: We included parents and their children who were both diagnosed with allergic asthma and/or allergic rhinitis, between January 2018 and December 2022. Parent-child dyads were evaluated for sensitization to six categories of allergens viz, dust mite, fungus, animal dander, weed pollen, tree pollen and food allergen, by measuring the allergen-specific immunoglobulin E levels (sIgE). Data of gender, age, feeding history, serum total IgE (tIgE), and absolute eosinophil counts (AEC) were collected and analyzed for differences in allergen sensitization of parents and children. RESULTS: Overall, the AEC in children were significantly higher than that of parents. The sensitivity to fungal allergens in children was significantly higher than that in fathers (33.3% vs 6.7%, P = 0.01) as well as mothers (29.3% vs 8.3%, P = 0.03). Sensitization to food allergens was also higher in children compared to fathers (25.4% vs 7.9%, P = 0.01). Fathers with tree pollen allergen sensitivity, and mothers with weed pollen allergen sensitivity had a significantly increased risk (aOR, 95% CI) of having increased sensitivity to these allergens in their offspring; 24.01 (1.08, 53.99; P = 0.04) and 3.27 (1.08, 9.92; P = 0.04), respectively. CONCLUSION: Children had greater sensitivity for fungal allergens compared to both parents, as well as food allergy compared to fathers. Fathers with tree pollen allergen sensitivity, and mothers with weed pollen allergen sensitivity had an increased risk of having their children sensitive to these types of allergens.

GPC3-targeted CAR-M cells exhibit potent antitumor activity against hepatocellular carcinoma.

Chimeric antigen receptor (CAR)-modified macrophages are a promising treatment for solid tumor. So far the potential effects of CAR-M cell therapy have rarely been investigated in hepatocellular carcinoma (HCC). Glypican-3 (GPC3) is a biomarker for a variety of malignancies, including liver cancer, which is not expressed in most adult tissues. Thus, it is an ideal target for the treatment of HCC. In this study, we engineered mouse macrophage cells with CAR targeting GPC3 and explored its therapeutic potential in HCC. First, we generated a chimeric adenoviral vector (Ad5f35) delivering an anti-GPC3 CAR, Ad5f35-anti-GPC3-CAR, which using the CAR construct containing the scFv targeting GPC3 and CD3ζ intracellular domain. Phagocytosis and killing effect indicated that macrophages transduced with Ad5f35-anti-GPC3-CAR (GPC3 CAR-Ms) exhibited antigen-specific phagocytosis and tumor cell clearance in vitro, and GPC3 CAR-Ms showed significant tumor-killing effects and promoted expression of pro-inflammatory (M1) cytokines and chemokines. In 3D NACs-origami spheroid model of HCC, CAR-Ms were further demonstrated to have a significant tumor killing effect. Together, our study provides a new strategy for the treatment of HCC through CAR-M cells targeting GPC3, which provides a basis for the research and treatment of hepatocellular carcinoma.

Pretrained Quantum-Inspired Deep Neural Network for Natural Language Processing.

Natural language processing (NLP) may face the inexplicable "black-box" problem of parameters and unreasonable modeling for lack of embedding of some characteristics of natural language, while the quantum-inspired models based on quantum theory may provide a potential solution. However, the essential prior knowledge and pretrained text features are often ignored at the early stage of the development of quantum-inspired models. To attacking the above challenges, a pretrained quantum-inspired deep neural network is proposed in this work, which is constructed based on quantum theory for carrying out strong performance and great interpretability in related NLP fields. Concretely, a quantum-inspired pretrained feature embedding (QPFE) method is first developed to model superposition states for words to embed more textual features. Then, a QPFE-ERNIE model is designed by merging the semantic features learned from the prevalent pretrained model ERNIE, which is verified with two NLP downstream tasks: 1) sentiment classification and 2) word sense disambiguation (WSD). In addition, schematic quantum circuit diagrams are provided, which has potential impetus for the future realization of quantum NLP with quantum device. Finally, the experiment results demonstrate QPFE-ERNIE is significantly better for sentiment classification than gated recurrent unit (GRU), BiLSTM, and TextCNN on five datasets in all metrics and achieves better results than ERNIE in accuracy, F1-score, and precision on two datasets (CR and SST), and it also has advantage for WSD over the classical models, including BERT (improves F1-score by 5.2 on average) and ERNIE (improves F1-score by 4.2 on average) and improves the F1-score by 8.7 on average compared with a previous quantum-inspired model QWSD. QPFE-ERNIE provides a novel pretrained quantum-inspired model for solving NLP problems, and it lays a foundation for exploring more quantum-inspired models in the future.

In situ construction of A-site high-entropy perovskites with interfacial CeO2 for a high-performance IT-SOFC air-electrode.

Developing an intermediate-temperature solid oxide fuel cell (IT-SOFC) is one of the most promising ways of achieving carbon neutrality, but its air-electrode is restricted by the conflict between the sluggish catalytic activity and durability. Herein, an A-site high-entropy perovskite composite La0.2Ba0.2Sr0.2Ca0.2Ce0.2-xCoO3-δ-xCeO2 (LBSCCC-CeO2) air-electrode material is fabricated via a one-step self-constructing strategy, which shows excellent oxygen reduction activity and stability due to the high-entropy structure and the synergy effect between LBSCCC and interfacial CeO2. This work provides a new way of fabricating high-performance air-electrodes in IT-SOFCs.

Misclassification in Weakly Supervised Object Detection.

Weakly supervised object detection (WSOD) aims to train detectors using only image-category labels. Current methods typically first generate dense class-agnostic proposals and then select objects based on the classification scores of these proposals. These methods mainly focus on selecting the proposal having high Intersection-over-Union with the true object location, while ignoring the problem of misclassification, which occurs when some proposals exhibit semantic similarities with objects from other categories due to viewing perspective and background interference. We observe that the positive class that is misclassified typically has the following two characteristics: 1) It is usually misclassified as one or a few specific negative classes, and the scores of these negative classes are high; 2) Compared to other negative classes, the score of the positive class is relatively high. Based on these two characteristics, we propose misclassification correction (MCC) and misclassification tolerance (MCT) respectively. In MCC, we establish a misclassification memory bank to record and summarize the class-pairs with high frequencies of potential misclassifications in the early stage of training, that is, cases where the score of a negative class is significantly higher than that of the positive class. In the later stage of training, when such cases occur and correspond to the summarized class-pairs, we select the top-scoring negative class proposal as the positive training example. In MCT, we decrease the loss weights of misclassified classes in the later stage of training to avoid them dominating training and causing misclassification of objects from other classes that are semantically similar to them during inference. Extensive experiments on the PASCAL VOC and MS COCO demonstrate our method can alleviate the problem of misclassification and achieve the state-of-the-art results.