ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method

TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis

Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by modern computer-aided diagnosis technology based on deep convolutions. However, the information aggregation across modalities in MSLD remains challenging due to severity unaligned spatial resolution (dermoscopic image and clinical image) and heterogeneous data (dermoscopic image and patients' meta-data). Limited by the intrinsic local attention, most recent MSLD pipelines using pure convolutions struggle to capture representative features in shallow layers, thus the fusion across different modalities is usually done at the end of the pipelines, even at the last layer, leading to an insufficient information aggregation. To tackle the issue, we introduce a pure transformer-based method, which we refer to as ``Throughout Fusion Transformer (TFormer)", for sufficient information intergration in MSLD. Different from the existing approaches with convolutions, the proposed network leverages transformer as feature extraction backbone, bringing more representative shallow features. We then carefully design a stack of dual-branch hierarchical multi-modal transformer (HMT) blocks to fuse information across different image modalities in a stage-by-stage way. With the aggregated information of image modalities, a multi-modal transformer post-fusion (MTP) block is designed to integrate features across image and non-image data. Such a strategy that information of the image modalities is firstly fused then the heterogeneous ones enables us to better divide and conquer the two major challenges while ensuring inter-modality dynamics are effectively modeled. Experiments conducted on the public Derm7pt dataset validate the superiority of the proposed method. Our TFormer outperforms other state-of-the-art methods. Ablation experiments also suggest the effectiveness of our designs

A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameter, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show our method outperforms other anti-rotation methods and achieves great improvements in dermoscopy image classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images

Efficacy and safety of Chinese medicine injections in combination with docetaxel and cisplatin for non-small cell lung cancer: a network meta-analysis

Background: Non-small cell lung cancer (NSCLC) poses a serious threat to human health. Several clinical studies have reported the benefits of Chinese herbal injections (CHIs) in combination with docetaxel and cisplatin (DP). This multidimensional network meta-analysis aimed to investigate the preferred regimen of CHIs in combination with DP for the treatment of NSCLC.Methods: Multiple databases were searched to identify randomized controlled trials (RCTs) of CHIs for NSCLC from the database inception to 30 April 2023. Studies that met the inclusion criteria and exhibited good methodological quality were included. Data analysis was conducted using Stata 15.0 and R 4.2.1 software. An odds ratio (OR) was used as the effect size, and the surface under the cumulative ranking curve (SCURA) was employed to rank the evaluated treatments.Results: The network meta-analysis included 85 eligible RCTs, encompassing 6,580 patients and 11 CHIs. Astragalus Injection combined with DP was identified as the most effective regimen for improving the response rate (SUCRAs: 90.25%). Brucea Javanica Oil Milk Injection combined with DP proved most effective in ameliorating the quality of life (SUCRAs: 76.89%). Shenfu Injection combined with DP emerged as the most effective for enhancing CD3+ and CD4+ (SUCRAs: 93.75%, 88.50%). Kanglaite Injection combined with DP exhibited the best efficacy in improving CD8+ (SUCRAs: 88.96%). Brucea Javanica Oil Milk Injection combined with DP was the most potent regimen for enhancing CD4+/CD8+ (SUCRAs: 93.13%).Conclusion: CHIs in combination with DP outperformed DP alone in NSCLC patients. Astragalus Injection plus DP, Brucea Javanica Oil Milk Injection plus DP, Shenfu Injection plus DP, Kanglaite Injection plus DP, and Brucea Javanica Oil Milk Injection plus DP were significantly effective. However, further multicenter and well-designed RCTs are required to validate our findings

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Cloud Detection Method for Landsat 8 Images Based on PCANet

Cloud detection for remote sensing images is often a necessary process, because cloud is widespread in optical remote sensing images and causes a lot of difficulty to many remote sensing activities, such as land cover monitoring, environmental monitoring and target recognizing. In this paper, a novel cloud detection method is proposed for multispectral remote sensing images from Landsat 8. Firstly, the color composite image of Bands 6, 3 and 2 is divided into superpixel sub-regions through Simple Linear Iterative Cluster (SLIC) method. Then, a two-step superpixel classification strategy is used to predict each superpixel as cloud or non-cloud. Thirdly, a fully connected Conditional Random Field (CRF) model is used to refine the cloud detection result, and accurate cloud borders are obtained. In the two-step superpixel classification strategy, the bright and thick cloud superpixels, as well as the obvious non-cloud superpixels, are firstly separated from potential cloud superpixels through a threshold function, which greatly speeds up the detection. The designed double-branch PCA Network (PCANet) architecture can extract the high-level information of cloud, then combined with a Support Vector Machine (SVM) classifier, the potential superpixels are correctly classified. Visual and quantitative comparison experiments are conducted on the Landsat 8 Cloud Cover Assessment (L8 CCA) dataset; the results indicate that our proposed method can accurately detect clouds under different conditions, which is more effective and robust than the compared state-of-the-art methods

A Closed-Loop Network for Single Infrared Remote Sensing Image Super-Resolution in Real World

Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset

Angiotensin-Converting Enzyme 2 Inhibits Apoptosis of Pulmonary Endothelial Cells During Acute Lung Injury Through Suppressing MiR-4262

Background/Aims: Angiotensin converting enzyme 2 (ACE2) treatment suppresses the severity of acute lung injury (ALI). The effects of ACE2 in ALI have been shown to not only result from its antagonizing hydrolyzing angiotensin II (AngII), which is responsible for reduction in the vascular tension and pulmonary accumulation of inflammatory cells, but also result from a role of ACE2 in suppressing the ALI-induced apoptosis of pulmonary endothelial cells (PECs). Nevertheless, the underlying mechanisms of the role of ACE2 on PEC apoptosis are not completely understood. Methods: Here, we used a bleomycin-induced mouse model for ALI that has been published in our previous studies. We analyzed the mRNA and protein levels of an anti-apoptotic protein Bcl-2 in the ALI-mice that have been treated w/o ACE2. We analyzed miR-4262 levels in the mouse lung in these mice. Bcl-2-targeting miRNAs were predicted using bioinformatics algorithms and a luciferase reporter assay was applied to examine the effects of miR-4262 on the Bcl-2 protein translation upon their binding to 3'-UTR of Bcl-2 mRNA. Adeno-associated viruses carrying either miR-4262 mimics or antisense were injected into ALI-mice without ACE2, and their effects on the apoptosis in mouse lung cells were analyzed by Western blot. Results: ACE2 inhibited the ALI-induced apoptosis of pulmonary cells in vivo partially through upregulation of Bcl-2 protein, but not Bcl-2 mRNA. ACE2 appeared to significantly suppress the upregulation of miR-4262 in mouse lung after ALI. MiR-4262 was found to target 3'-UTR of Bcl-2 mRNA to inhibit its protein translation in PECs. In vivo administration of antisense of miR-4262 decreased apoptosis of pulmonary cells and severity of the ALI in mice. Conclusion: ACE2-induced suppression of miR-4262 partially contribute to the inhibition of the PEC apoptosis after ALI through Bcl-2. MiR-4262 may be a novel promising treatment target for ALI and ARDS

Estimates of Economic Loss of Materials Caused by Acid Deposition in China

China is facing severe acid deposition. Acid deposition can cause economic loss, corrosion, and damage to materials, and the reduction of material life span. In this study, the administrative areas (including municipalities, prefecture-level cities, regions, autonomous prefectures, and leagues—hereinafter referred to the cities) at and above the prefecture level were selected as research areas. Monitoring results of acid precipitation and ambient air sulfur dioxide (SO2) from the China National Environmental Monitoring Network were used, research findings available domestically and abroad were summarized, and a set of material exposure inventory per capita was established, based on urban and rural areas in Eastern, Central, and Western China regions. Losses of construction materials caused by acid deposition in the cities were assessed by using the said materials’ acid rain exposure response functions available. The results showed that, material loss caused by acid deposition in China was 32.165 billion yuan (RMB, similarly hereinafter) in 2013, accounting for 0.057% of GDP (Gross Domestic Product) and 3.4% of the total investment for environmental pollution governance this year