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Abstract Exosomes are nanovesicles with multiple components used in several applications. Mesenchymal stem cells (MSCs) are well known for their great potential in clinical applications. MSC-derived exosomes (MSC-Exos) have been shown to mediate tissue regeneration in various diseases, including neurological, autoimmune, and inflammatory diseases, cancer, ischemic heart disease, lung injury, and liver fibrosis. They can modulate the immune response by interacting with immune effector cells in the presence of anti-inflammatory compounds and are involved in intercellular communication through various types of cargo. This review summarizes the MSC-Exos-mediated tissue regeneration in various diseases, including neurological, cardiovascular, liver, kidney, articular cartilage, and oral tissue applications. In addition, we discuss the challenges and prospects of MSC-Exos in tissue regeneration.

Abstract Person image generation aims to generate images that maintain the original human appearance in different target poses. Recent works have revealed that the critical element in achieving this task is the alignment of appearance domain and pose domain. Previous alignment methods, such as appearance flow warping, correspondence learning and cross attention, often encounter challenges when it comes to producing fine texture details. These approaches suffer from limitations in accurately estimating appearance flows due to the lack of global receptive field. Alternatively, they can only perform cross‐domain alignment on high‐level feature maps with small spatial dimensions since the computational complexity increases quadratically with larger feature sizes. In this article, the significance of multi‐scale alignment, in both low‐level and high‐level domains, for ensuring reliable cross‐domain alignment of appearance and pose is demonstrated. To this end, a novel and effective method, named Multi‐scale Cross‐domain Alignment (MCA) is proposed. Firstly, MCA adopts global context aggregation transformer to model multi‐scale interaction between pose and appearance inputs, which employs pair‐wise window‐based cross attention. Furthermore, leveraging the integrated global source information for each target position, MCA applies flexible flow prediction head and point correlation to effectively conduct warping and fusing for final transformed person image generation. Our proposed MCA achieves superior performance on two popular datasets than other methods, which verifies the effectiveness of our approach.

Abstract Hepatocellular carcinoma (HCC) is the most common primary liver cancer. It is characterized by occult onset resulting in most patients being diagnosed at advanced stages and with poor prognosis. Exosomes are nanoscale vesicles with a lipid bilayer envelope released by various cells under physiological and pathological conditions, which play an important role in the biological information transfer between cells. There is growing evidence that HCC cell-derived exosomes may contribute to the establishment of a favorable microenvironment that supports cancer cell proliferation, invasion, and metastasis. These exosomes not only provide a versatile platform for diagnosis but also serve as a vehicle for drug delivery. In this paper, we review the role of exosomes involved in the proliferation, migration, and metastasis of HCC and describe their application in HCC diagnosis and treatment. We also discuss the prospects of exosome application in HCC and the research challenges.

Abstract Purpose Optimal treatment of breast abscesses has been controversial. Herein, we report an innovative method for the operative treatment of lactational mammary abscesses. Methods Nineteen lactating patients diagnosed with breast abscesses were enrolled in the study, and abscess debridement and drainage were performed using an arthroscopic system. The clinical characteristics of the patients were recorded to evaluate the feasibility, efficacy, and cosmetic results of arthroscopic surgery for breast abscesses. Results All 19 patients were cured and did not relapse within the 6-month-follow-up period. One patient stopped breastfeeding due to breast leakage. All patients were satisfied with the postoperative appearance of the breast. Conclusion Arthroscopic debridement and drainage are effective treatment methods for lactational breast abscesses, with a high cure rate, few complications, and satisfactory cosmetic outcomes.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, and the third leading cause of cancer-related deaths worldwide. HCC is characterized by insidious onset, and most patients are diagnosed at an advanced stage with a poor prognosis. Identification of biomarkers for HCC onset and progression is imperative to development of effective diagnostic and therapeutic strategies. CD147 is a glycoprotein that is involved in tumor cell invasion, metastasis and angiogenesis through multiple mechanisms. In this review, we describe the molecular structure of CD147 and its role in regulating HCC invasion, metastasis and angiogenesis. We highlight its potential as a diagnostic and therapeutic target for HCC.

The wave-driven floating photobioreactors (PBRs) with advantages of easy in scaling-up, low energy inputs and low fabricating cost, hold great potential for massive and cost-energy effective microalgae production. However, their applications may be seriously challenged by intermittent waves that could produce very poor mixing under poor wave conditions, leading to a significant reduction of biomass productivity or even collapse of the cultures. To improve the utilization efficiency of waves for efficient and stable microalgae production in the floating PBRs, this work aims at numerically studying the fluid-dynamics of the floating PBRs, as well as the effects from wave conditions, culture depth and three different PBRs’ structures of square, rectangular and circular types. The results showed that the liquid inside the floating PBRs follow a periodic sinusoidal and reciprocating flow, and the square PBR had aggressive mixing characteristics at high wave excitation frequency, while the rectangular PBR produced more intense mixing at low wave excitation frequency. Regarding the culture depth, the dependence of liquid mixing on the culture depth showed a decreasing trend. Moreover, the results indicated that the PBRs with a high culture depth had several dead zones, although there was apparent upward flow at the high excitation frequency. This work provides valuable insight into increasing the utilization efficiency of wave energy for mixing enhancement in the floating PBRs and their design.

Abstract Facial reenactment has developed rapidly in recent years, but few methods have been built upon reenacted face in videos. Facial‐reenacted emotion synthesis can make the process of facial reenactment more practical. A facial‐reenacted emotion synthesis method is proposed that includes a dual‐path generative adversarial network (GAN) for emotion synthesis and a residual‐mask network to impose structural restrictions to preserve the mouth shape of the source person. To train the dual‐path GAN more effectively, a learning strategy based on separated discriminators is proposed. The method is trained and tested on a very challenging imbalanced dataset to evaluate the ability to deal with complex practical scenarios. Compared with general emotion synthesis methods, the proposed method can generate more realistic facial emotion synthesised images or videos with higher quality while retaining the expression contents of the original videos. The DualPathGAN achieves a Fréchet inception distance (FID) score of 9.20, which is lower than the FID score of 11.37 achieved with state‐of‐the‐art methods.

Abstract Background Lima bean (Phaseolus lunatus L.) is a member of subfamily Phaseolinae belonging to the family Leguminosae and an important source of plant proteins for the human diet. As we all know, lima beans have important economic value and great diversity. However, our knowledge of the chloroplast genome level of lima beans is limited. Results The chloroplast genome of lima bean was obtained by Illumina sequencing technology for the first time. The Cp genome with a length of 150,902 bp, including a pair of inverted repeats (IRA and IRB 26543 bp each), a large single-copy (LSC 80218 bp) and a small single-copy region (SSC 17598 bp). In total, 124 unique genes including 82 protein-coding genes, 34 tRNA genes, and 8 rRNA genes were identified in the P. lunatus Cp genome. A total of 61 long repeats and 290 SSRs were detected in the lima bean Cp genome. It has a typical 50 kb inversion of the Leguminosae family and an 70 kb inversion to subtribe Phaseolinae. rpl16, accD, petB, rsp16, clpP, ndhA, ndhF and ycf1 genes in coding regions was found significant variation, the intergenic regions of trnk-rbcL, rbcL-atpB, ndhJ-rps4, psbD-rpoB, atpI-atpA, atpA-accD, accD-psbJ, psbE-psbB, rsp11-rsp19, ndhF-ccsA was found in a high degree of divergence. A phylogenetic analysis showed that P. lunatus appears to be more closely related to P. vulgaris, V.unguiculata and V. radiata. Conclusions The characteristics of the lima bean Cp genome was identified for the first time, these results will provide useful insights for species identification, evolutionary studies and molecular biology research.

The ever-increasing computation cost of Convolutional Neural Network (CNN) makes it imperative for real-world applications to accelerate the key steps especially the inference. In this work, we propose an efficient yet general scheme called Sparse Prediction Layer (SPL) which can predict and skip the trivial elements in the CNN layer. Pruned weights are used to predict the locations of maximum values in max-pooling kernels and those of positive values before Rectified Linear Units (ReLUs). Thereafter, the precise values of these predicted important elements are calculated selectively and the complete outputs are restored from them. Our experiments on ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2012 show that SPL can reduce 68.3%, 58.6% and 59.5% Floating-point Operations (FLOPs) on AlexNet, VGG-16 and ResNet-50, respectively, within an accuracy loss of less than 1% without retraining. The proposed SPL scheme can further accelerate these networks pruned by other pruning-based methods, such as a FLOP reduction of 50.2% on the ResNet-50 which has been pruned by Channel Pruning (CP) before being applied with SPLs. A special matrix multiplication called Sparse Result Matrix Multiplication (SRMM) is proposed to support the implementation of SPL, and its acceleration effect is in line with expectations.

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm.

To enhance the security level of digital information, the biometric authentication method based on Electrocardiographic (ECG) is gaining increasing attention in a wide range of applications. Compared with other biometric features, e.g., fingerprint and face, the ECG signals have several advantages, such as higher security, simpler acquisition, liveness detection, and health information. Therefore, various methods for ECG-based authentication have been proposed. However, the generalization ability of these methods is limited because the feature extraction for the ECG signals in conventional methods is data dependent. To improve the generalization ability and achieve more stable results on different datasets, a parallel multi-scale one-dimensional residual network is proposed in this paper. This network utilizes three convolutional kernels with different kernel sizes, achieving better classification accuracy than the conventional schemes. Moreover, two loss functions named center loss and margin loss are used during the training of the network. Compared with the conventional softmax loss, these two loss functions can further improve the generalization ability of the extracted embedding features. Furthermore, we evaluate the effectiveness of our proposed method thoroughly on the ECG-ID database, the PTB Diagnostic ECG database, and the MIT-BIH Arrhythmia database, achieving 2.00%, 0.59%, and 4.74% of equal error rate (EER), respectively. Compared with other works, our proposed method improves 1.61% and 4.89% classification accuracy on the ECG-ID database and the MIT-BIH Arrhythmia database, respectively.

Global warming induces heat stress in eggplant, seriously affecting its quality and yield. The response to heat stress is a complex regulatory process; however, the exact mechanism in eggplant is unknown. We analyzed the transcriptome of eggplant under different high-temperature treatments using RNA-Seq technology. Three libraries treated at high temperatures were generated and sequenced. There were 40,733,667, 40,833,852, and 40,301,285 clean reads with 83.98%, 79.69%, and 84.42% of sequences mapped to the eggplant reference genome in groups exposed to 28°C (CK), 38°C (T38), and 43°C (T43), respectively. There were 3,067 and 1,456 DEGs in T38 vs CK and T43 vs CK groups, respectively. In these two DEG groups, 315 and 342 genes were up- and down-regulated, respectively, in common. Differential expression patterns of DEGs in antioxidant enzyme systems, detoxication, phytohormones, and transcription factors under heat stress were investigated. We screened heat stress-related genes for further validation by qRT-PCR. Regulation mechanisms may differ under different temperature treatments, in which heat shock proteins and heat stress transcription factors play vital roles. These results provide insight into the molecular mechanisms of the heat stress response in eggplant and may be useful in crop breeding.

Cardiovascular diseases (CVDs) have become a serious threat to human life and health. Though many drugs acting via different mechanism of action are available in the market as conventional formulations for the treatment of CVDs, they are still far from satisfactory due to poor water solubility, low biological efficacy, non-targeting, and drug resistance. Nano-drug delivery systems (NDDSs) provide a new drug delivery method for the treatment of CVDs with the development of nanotechnology, demonstrating great advantages in solving the above problems. Nevertheless, there are some problems about NDDSs need to be addressed, such as cytotoxicity. In this review, the types and targeting strategies of NDDSs were summarized, and the new research progress in the diagnosis and therapy of CVDs in recent years was reviewed. Future prospective for nano-carriers in drug delivery for CVDs includes gene therapy, in order to provide more ideas for the improvement of cardiovascular drugs. In addition, its safety was also discussed in the review.

Lowered power loss and asymmetrically electrical parameters are reported in the DC aging of Co2O3-doped ZnO varistors in this paper. Based on the frequency domain dielectric responses of the pristine and degraded samples, the present study explores the roles of point defects in the aging process via dielectric relaxations and their parameters. It is found that breakdown field increased more in the positive direction than the negative direction. Nonlinearity increased in the positive direction, whereas it decreased in the negative direction, and leakage current density increased more in the negative direction than the positive direction. Given the lowest migration energy of Zinc interstitial (Zni, 0.57 eV) and high oxygen ion conductivity in Bi2O3-rich phase, it is speculated that Zni and adsorbed oxygen (Oad) migrate under DC bias, and then change the defect structure and the double Schottky barrier (DSB) at grain boundaries. As a result, the forward-biased barrier height gradually decreases more than the reverse-biased one.

Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility. The iris center and anchor point are the most crucial factors for the accuracy of the system. The accurate iris center is detected by the simple active contour snakuscule, which is initialized by the prior knowledge of eye anatomical dimensions. After that, a novel anchor point is computed by the stable facial landmarks. Next, second-order mapping functions use the eye vectors and the head pose to estimate the points of regard. Finally, the gaze errors are improved by implementing a weight coefficient on the points of regard of the left and right eyes. The feature position of the iris center achieves an accuracy of 98.87% on the GI4E database when the normalized error is lower than 0.05. The accuracy of the gaze tracking method is superior to the-state-of-the-art appearance-based and feature-based methods on the EYEDIAP database.