Brain-Inspired Learning, Perception, and Cognition: A Comprehensive Review.

The progress of brain cognition and learning mechanisms has provided new inspiration for the next generation of artificial intelligence (AI) and provided the biological basis for the establishment of new models and methods. Brain science can effectively improve the intelligence of existing models and systems. Compared with other reviews, this article provides a comprehensive review of brain-inspired deep learning algorithms for learning, perception, and cognition from microscopic, mesoscopic, macroscopic, and super-macroscopic perspectives. First, this article introduces the brain cognition mechanism. Then, it summarizes the existing studies on brain-inspired learning and modeling from the perspectives of neural structure, cognitive module, learning mechanism, and behavioral characteristics. Next, this article introduces the potential learning directions of brain-inspired learning from four aspects: perception, cognition, understanding, and decision-making. Finally, the top-ten open problems that brain-inspired learning, perception, and cognition currently face are summarized, and the next generation of AI technology has been prospected. This work intends to provide a quick overview of the research on brain-inspired AI algorithms and to motivate future research by illuminating the latest developments in brain science.

Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey.

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.

Machine Learning-Based Figure of Merit Model of SIPOS Modulated Drift Region for U-MOSFET.

This paper presents a machine learning-based figure of merit model for superjunction (SJ) U-MOSFET (SSJ-UMOS) with a modulated drift region utilizing semi-insulating poly-crystalline silicon (SIPOS) pillars. This SJ drift region modulation is achieved through SIPOS pillars beneath the trench gate, focusing on optimizing the tradeoff between breakdown voltage (BV) and specific ON-resistance (RON,sp). This analytical model considers the effects of electric field modulation, charge-coupling, and majority carrier accumulation due to additional SIPOS pillars. Gaussian process regression is employed for the figure of merit (FOM = BV2/RON,sp) prediction and hyperparameter optimization, ensuring a reasonable and accurate model. A methodology is devised to determine the optimal BV-RON,sp tradeoff, surpassing the SJ silicon limit. The paper also delves into a discussion of optimal structural parameters for drift region, oxide thickness, and electric field modulation coefficients within the analytical model. The validity of the proposed model is robustly confirmed through comprehensive verification against TCAD simulation results.

Efficacy of mitral valve repair in combination with coronary revascularization for moderate ischaemic mitral regurgitation: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: The efficacy of mitral valve repair (MVR) in combination with coronary artery bypass grafting (CABG) for moderate ischaemic mitral regurgitation (IMR) remains unclear. To evaluate whether MVR + CABG is superior to CABG alone, the authors conducted a systematic review and meta-analysis of existing randomized controlled trials (RCTs). METHODS: The authors searched PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials for eligible RCTs from the date of their inception to October 2023. The primary outcomes were operative (in-hospital or within 30 days) and long-term (≥ 1 year) mortality. The secondary outcomes were postoperative stroke, worsening renal function (WRF), and reoperation for bleeding or tamponade. The authors performed random-effects meta-analyses and reported the results as risk ratios (RRs) with 95% CIs. RESULTS: Six RCTs were eligible for inclusion. Compared with CABG alone, MVR + CABG did not increase the risk of operative mortality (RR, 1.244; 95% CI, 0.514-3.014); however, it was also not associated with a lower risk of long-term mortality (RR, 0.676; 95% CI, 0.417-1.097). Meanwhile, there was no difference between the two groups in terms of postoperative stroke (RR, 2.425; 95% CI, 0.743-7.915), WRF (RR, 1.257; 95% CI, 0.533-2.964), and reoperation for bleeding or tamponade (RR, 1.667; 95% CI, 0.527-5.270). CONCLUSIONS: The findings of this meta-analysis suggest that MVR + CABG fails to improve the clinical outcomes of patients with moderate IMR compared to CABG alone.

Self-assembly of PEG-PPS polymers and LL-37 peptide nanomicelles improves the oxidative microenvironment and promotes angiogenesis to facilitate chronic wound healing.

Refractory diabetic wounds are associated with high incidence, mortality, and recurrence rates and are a devastating and rapidly growing clinical problem. However, treating these wounds is difficult owing to uncontrolled inflammatory microenvironments and defective angiogenesis in the affected areas, with no established effective treatment to the best of our knowledge. Herein, we optimized a dual functional therapeutic agent based on the assembly of LL-37 peptides and diblock copolymer poly(ethylene glycol)-poly(propylene sulfide) (PEG-PPS). The incorporation of PEG-PPS enabled responsive or controlled LL-37 peptide release in the presence of reactive oxygen species (ROS). LL-37@PEG-PPS nanomicelles not only scavenged excessive ROS to improve the microenvironment for angiogenesis but also released LL-37 peptides and protected them from degradation, thereby robustly increasing angiogenesis. Diabetic wounds treated with LL-37@PEG-PPS exhibited accelerated and high-quality wound healing in vivo. This study shows that LL-37@PEG-PPS can restore beneficial angiogenesis in the wound microenvironment by continuously providing angiogenesis-promoting signals. Thus, it may be a promising drug for improving chronic refractory wound healing.

Explore the Influence of Shallow Information on Point Cloud Registration.

Feature extraction is a key step for deep-learning-based point cloud registration. In the correspondence-free point cloud registration task, the previous work commonly aggregates deep information for global feature extraction and numerous shallow information which is positive to point cloud registration will be ignored with the deepening of the neural network. Shallow information tends to represent the structural information of the point cloud, while deep information tends to represent the semantic information of the point cloud. In addition, fusing information of different dimensions is conducive to making full use of shallow information. Inspired by this, we verify shallow information in the middle layers can bring a positive impact on the point cloud registration task. We design various architectures to combine shallow information and deep information to extract global features for point cloud registration. Experimental results on the ModelNet40 dataset illustrate that feature extractors that incorporate shallow information will bring positive performance.

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications.

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Superficial Circumflex Iliac Artery Perforator Flap with Bilobed Design for the Donor Defect after Wrap-Around Flap Transfer Reconstruction.

OBJECTIVE: The repair of great toe donor site defect after wrap-around flap transfer is still controversial. The bilobed superficial circumflex iliac artery perforator (SCIP) flap can improve the aesthetics of the great toe while maintaining its function. Thus, this study aimed to report our experience in the reconstruction of big toe donor site defects with the bilobed SCIP flap and describe the clinical outcomes. METHODS: This study was a retrospective trial. From May 2017 to May 2020, 13 patients with the great toe donor site defect after wrap-around flap transfer were included in this study. The average age of the patients was 44 years (range, 23-60 years). All patients received free bilobed SCIP flaps to reconstruct the donor site defect of the great toe. Relevant clinical features were recorded preoperatively. The thickness and design of the SCIP flap and the harvesting layer of the flap were measured during the operation. The survival rate of flaps and skin grafts and the incidence of infection were recorded after operation. At follow-up, donor site complications and postoperative outcomes were evaluated. RESULTS: In all cases, the SCIP flap covering the donor site of the great toe survived. All patients were followed up for 24-40 months (mean, 30.5 months). The average thickness of the SCIP flap was 0.38cm. All SCIP flaps were harvested from the superficial fascial layer except for three obese patients. The thin SCIP flap had a bilobed design with no further defatting procedures. Postoperatively, the great toe-nail flap donor site regained its original appearance without bloating or flap necrosis. There was a hidden linear scar in the groin donor site, which did not affect hip joint movement. All patients were satisfied with the aesthetics of the surgical site. CONCLUSION: The SCIP flap with bilobed design for repairing the donor defect of the great toe after wrap-around flap transfer is a kind of surgical method with excellent contour, meeting the requirements of function and aesthetics.

Multiscale Curvelet Scattering Network.

Feature representation has received more and more attention in image classification. Existing methods always directly extract features via convolutional neural networks (CNNs). Recent studies have shown the potential of CNNs when dealing with images' edges and textures, and some methods have been explored to further improve the representation process of CNNs. In this article, we propose a novel classification framework called the multiscale curvelet scattering network (MSCCN). Using the multiscale curvelet-scattering module (CCM), image features can be effectively represented. There are two parts in MSCCN, which are the multiresolution scattering process and the multiscale curvelet module. According to multiscale geometric analysis, curvelet features are utilized to improve the scattering process with more effective multiscale directional information. Specifically, the scattering process and curvelet features are effectively formulated into a unified optimization structure, with features from different scale levels being efficiently aggregated and learned. Furthermore, a one-level CCM, which can essentially improve the quality of feature representation, is constructed to be embedded into other existing networks. Extensive experimental results illustrate that MSCCN achieves better classification accuracy when compared with state-of-the-art techniques. Eventually, the convergence, insight, and adaptability are evaluated by calculating the trend of loss function's values, visualizing some feature maps, and performing generalization analysis.

A Multi-Scale Progressive Collaborative Attention Network for Remote Sensing Fusion Classification.

With the development of remote sensing technology, panchromatic images (PANs) and multispectral images (MSs) can be easily obtained. PAN has higher spatial resolution, while MS has more spectral information. So how to use the two kinds of images' characteristics to design a network has become a hot research field. In this article, a multi-scale progressive collaborative attention network (MPCA-Net) is proposed for PAN and MS's fusion classification. Compared to the traditional multi-scale convolution operations, we adopt an adaptive dilation rate selection strategy (ADR-SS) to adaptively select the dilation rate to deal with the problem of category area's excessive scale differences. For the traditional pixel-by-pixel sliding window sampling strategy, the patches which are generated by adjacent pixels but belonging to different categories contain a considerable overlap of information. So we change original sampling strategy and propose a center pixel migration (CPM) strategy. It migrates the center pixel to the most similar position of the neighborhood information for classification, which reduces network confusion and increases its stability. Moreover, due to the different spatial and spectral characteristics of PAN and MS, the same network structure for the two branches ignores their respective advantages. For a certain branch, as the network deepens, characteristic has different representations in different stages, so using the same module in multiple feature extraction stages is inappropriate. Thus we carefully design different modules for each feature extraction stage of the two branches. Between the two branches, because the strong mapping methods of directly cascading their features are too rough, we design collaborative progressive fusion modules to eliminate the differences. The experimental results verify that our proposed method can achieve competitive performance.

A Novel Bifunctional Nanoplatform with Aggregation-Induced Emission Property for Efficient Photodynamic Killing of Bacteria and Wound Healing.

Background: Photodynamic antimicrobial therapy (PDAT) has been extensively studied because of its potential applications such as precise controllability, high spatiotemporal accuracy, and non-invasiveness. More importantly, it is difficult for bacteria to develop resistance to the aforementioned PDATs. However, the selectivity of traditional PDAT methods to bacteria is generally poor, so it has been proposed to introduce positively charged components such as quaternary ammonium salts to enhance the targeting of bacteria; however, they always possess high toxicity to normal cells. As a result, measures should be taken to enhance the targeting of bacteria and avoid side effects on normal cells. Methods and Results: In our work, we creatively design a nanoplatform with high anti-bacterial efficiency, low side effects and its size is approximately 121 nm. BSA, as a nanocarrier, encapsulates the photosensitizer (E)-4-(4-(diphenylamino)styryl)-1-methylpyridin-1-ium with AIE properties named as BSA-Tpy, which increases its circulation time in vivo and improves the biocompatibility. Under acidic conditions (pH = 5.0), the surface positive charge of the BSA-Tpy is increased to +18.8 mV due to protonation of amine residues to achieve the targeting effect on bacteria. Besides, under the irradiation of white light, the BSA-Tpy will produce ROS to kill bacteria efficiently about 99.99% for both Gram-positive and Gram-negative bacteria, which shows the potential application value for the treatment of infected wounds. Conclusion: We have developed a feasible method for photodynamic antibacterial therapy, possessing excellent biocompatibility and high antibacterial efficiency with good fluorescence imaging property.

The Relationship Between Ferroptosis and Diseases.

Ferroptosis is an iron-dependent mode of cell death. It can occur through two major pathways, exogenous (or transporter-dependent) and endogenous (or enzyme-regulated) pathways are activated by biological or chemical inducers, and glutathione peroxidase activity is inhibited, which causes intracellular iron accumulation and lipid Peroxidation. Ferroptosis is closely related to the pathological process of many diseases. How to intervene in the occurrence and development of related diseases by regulating ferroptosis has become a hot research topic. At present, studies have shown that ferroptosis is found in common diseases such as tumors, inflammatory diseases, bacterial infections, pulmonary fibrosis, hepatitis, inflammatory bowel disease, neurodegenerative diseases, kidney injury, ischemia-reperfusion injury and skeletal muscle injury. This article reviews the characteristics and mechanism of ferroptosis, and summarizes how ferroptosis participates in the pathophysiological process in various systemic diseases of the body, which may provide new references for the treatment of clinical diseases in the future.

Coarse-to-Fine Contrastive Self-Supervised Feature Learning for Land-Cover Classification in SAR Images With Limited Labeled Data.

Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a novel contrasting strategy to address this problem. It consists of two stages, one for encoder pre-training to learn global features and the other for decoder pre-training to derive local features. Firstly, the novel contrasting strategy takes advantage of the spatial structure and semantic meaning of different regions and provides more cues to learn than that relying only on data augmentation. Specifically, a positive pair is built from two nearby patches sampled along the direction of the texture if they fall into the same cluster. A negative pair is generated from different clusters. When the novel contrasting strategy is applied to the coarse-to-fine CSSL framework, global and local features are learned successively by forcing the positive pair close to each other and the negative pair apart in an embedding space. Secondly, a discriminant constraint is incorporated into the per-pixel classification model to maximize the inter-class distance. It makes the classification model more competent at distinguishing between different categories that have similar appearance. Finally, the proposed method is validated on four SAR images for land-cover classification with limited labeled data and substantially improves the experimental results. The effectiveness of the proposed method is demonstrated in pixel-level tasks after comparison with the state-of-the-art methods.

Application of nanosonosensitizer materials in cancer sono-dynamic therapy.

Sonodynamic therapy (SDT) is a novel non-invasive treatment for cancer combining low-intensity ultrasound and sonosensitizers. SDT activates sonosensitizers through ultrasound, releasing energy and generating reactive oxygen species to kill tumor cells. Compared with traditional photodynamic therapy (PDT), SDT is a promising anti-cancer therapy with the advantages of better targeting, deeper tissue penetration, and higher focusing ability. With the development and broad application of nanomaterials, novel sonosensitizers with tumor-targeting specificity can deliver to deep tumors and enhance the tumor microenvironment. In this review, we first review the mechanisms of sonodynamic therapy. In addition, we also focus on the current types of sonosensitizers and the latest design strategies of nanomaterials in sonosensitizers. Finally, we summarize the combined strategy of sonodynamic therapy.

A Concurrent Multiscale Detector for End-to-End Image Matching.

This article focuses on end-to-end image matching through joint key-point detection and descriptor extraction. To find repeatable and high discrimination key points, we improve the deep matching network from the perspectives of network structure and network optimization. First, we propose a concurrent multiscale detector (CS-det) network, which consists of several parallel convolutional networks to extract multiscale features and multilevel discriminative information for key-point detection. Moreover, we introduce an attention module to fuse the response maps of various features adaptively. Importantly, we propose two novel rank consistent losses (RC-losses) for network optimization, significantly improving image matching performances. On the one hand, we propose a score rank consistent loss (RC-S-loss) to ensure that the key points have high repeatability. Different from the score difference loss merely focusing on the absolute score of an individual key point, our proposed RC-S-loss pays more attention to the relative score of key points in the image. On the other hand, we propose a score-discrimination RC-loss to ensure that the key point has high discrimination, which can reduce the confusion from other key points in subsequent matching and then further enhance the accuracy of image matching. Extensive experimental results demonstrate that the proposed CS-det improves the mean matching result of deep detector by 1.4%-2.1%, and the proposed RC-losses can boost the matching performances by 2.7%-3.4% than score difference loss. Our source codes are available at https://github.com/iquandou/CS-Net.

Deep Multiview Union Learning Network for Multisource Image Classification.

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extractor is designed to process the multisource data by canonical correlation analysis (CCA) in the head of the network. Second, an improved deep learning architecture with two branches is presented to extract high-level view features from the associated features. Third, a novel pooling, called view union pooling, is proposed to fuse the multiview feature from the deep model. Finally, the fused feature is fed into the classifier. The proposed framework is easy to optimize since it is an end-to-end network. Extensive experiments and analysis on the datasets IEEE_grss_dfc_2017 and IEEE_grss_dfc_2018 show that the proposed method achieves comparable results. Our results demonstrate that abundant multisource information can improve the classification performance.

New Generation Deep Learning for Video Object Detection: A Survey.

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.

Element-Wise Feature Relation Learning Network for Cross-Spectral Image Patch Matching.

Recently, the majority of successful matching approaches are based on convolutional neural networks, which focus on learning the invariant and discriminative features for individual image patches based on image content. However, the image patch matching task is essentially to predict the matching relationship of patch pairs, that is, matching (similar) or non-matching (dissimilar). Therefore, we consider that the feature relation (FR) learning is more important than individual feature learning for image patch matching problem. Motivated by this, we propose an element-wise FR learning network for image patch matching, which transforms the image patch matching task into an image relationship-based pattern classification problem and dramatically improves generalization performances on image matching. Meanwhile, the proposed element-wise learning methods encourage full interaction between feature information and can naturally learn FR. Moreover, we propose to aggregate FR from multilevels, which integrates the multiscale FR for more precise matching. Experimental results demonstrate that our proposal achieves superior performances on cross-spectral image patch matching and single spectral image patch matching, and good generalization on image patch retrieval.

DPFL-Nets: Deep Pyramid Feature Learning Networks for Multiscale Change Detection.

Due to the complementary properties of different types of sensors, change detection between heterogeneous images receives increasing attention from researchers. However, change detection cannot be handled by directly comparing two heterogeneous images since they demonstrate different image appearances and statistics. In this article, we propose a deep pyramid feature learning network (DPFL-Net) for change detection, especially between heterogeneous images. DPFL-Net can learn a series of hierarchical features in an unsupervised fashion, containing both spatial details and multiscale contextual information. The learned pyramid features from two input images make unchanged pixels matched exactly and changed ones dissimilar and after transformed into the same space for each scale successively. We further propose fusion blocks to aggregate multiscale difference images (DIs), generating an enhanced DI with strong separability. Based on the enhanced DI, unchanged areas are predicted and used to train DPFL-Net in the next iteration. In this article, pyramid features and unchanged areas are updated alternately, leading to an unsupervised change detection method. In the feature transformation process, local consistency is introduced to constrain the learned pyramid features, modeling the correlations between the neighboring pixels and reducing the false alarms. Experimental results demonstrate that the proposed approach achieves superior or at least comparable results to the existing state-of-the-art change detection methods in both homogeneous and heterogeneous cases.