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Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.

To achieve a comprehensive evaluation of candidate drugs in terms of both biological activity and ADMET properties, this study proposes a two-stage predictive framework based on Quantitative Structure–Activity Relationship (QSAR) modeling integrated with machine learning techniques, elucidating the quantitative relationships between molecular structure and pharmacological properties. A novel Dual-Filter Feature Selection (DFFS) method integrates statistical analysis and feature importance scores derived from machine learning models. The averaged rankings are used to obtain a robust set of molecular descriptors. In the first stage, 20 key two-dimensional molecular descriptors were selected via DFFS from ERα antagonists. RF, XGBoost, LightGBM, and gcForest—were employed for activity prediction. Experimental results indicated LightGBM achieved the best performance, with MRE of 0.0775. The comparative experiment demonstrates that under the same LightGBM regression framework, DFFS outperformed its individual components—Mutual Information and XGBoost—as well as the high-dimensional features generated by ChemBERTa. In the second stage, based on 40 descriptors selected by DFFS, a stacking model was constructed to perform multitask prediction of ADMET properties, ensuring that high-activity candidate compounds also exhibit favorable profiles in absorption, distribution, metabolism, excretion, and toxicity. The AUC scores for all five ADMET models exceeded 0.95. To elucidate the molecular mechanisms and interpret the model decisions, we applied Phi coefficient analysis to assess inter-property correlations and SHAP analysis to identify key molecular features governing compound activity. Furthermore, molecular docking was performed to evaluate the binding affinity of highly active compounds towards the target protein, thereby providing quantitative validation of the predicted biological activities.

Human epidermal growth factor receptor 2 (HER2) regulates cell mitosis, proliferation, and apoptosis. Trastuzumab is a HER2-targeted monoclonal antibody (mAB), which can prolong the overall survival rate of patients with HER2 overexpression in later periods of gastric cancer and breast cancer. Although anti-HER2 monoclonal antibody has a curative effect, adjuvant chemotherapy is still necessary to upgrade the curative effect maximumly. Antibody-drug conjugate (ADC) is a kind of therapeutic drug that contains antigen-specific antibody and cytotoxic payload, which can improve the survival time of tumor patients. To date, there are several HER2-ADC products on the market, for which two anti-HER2 ADC (trastuzumab emtansine and trastuzumab deruxtecan) have been authorized by the FDA for distinct types of HER2-positive carcinoma in the breast. Disitamab vedotin (RC48) is a newly developed ADC drug targeting HER2 that is comprised of hertuzumab coupling monomethyl auristatin E (MMAE) via a cleavable linker. This paper aims to offer a general insight and summary of the mechanism of action and the currently completed and ongoing clinical studies of RC-48 in HER-2 positive solid tumors.

Chemotherapy was conventionally applied to kill cancer cells, but regrettably, they also induce damage to normal cells with high-proliferative capacity resulting in cardiotoxicity, nephrotoxicity, peripheral nerve toxicity, and ovarian toxicity. Of these, chemotherapy-induced ovarian damages mainly include but are not limited to decreased ovarian reserve, infertility, and ovarian atrophy. Therefore, exploring the underlying mechanism of chemotherapeutic drug-induced ovarian damage will pave the way to develop fertility-protective adjuvants for female patients during conventional cancer treatment. Herein, we firstly confirmed the abnormal gonadal hormone levels in patients who received chemotherapy and further found that conventional chemotherapeutic drugs (cyclophosphamide, CTX; paclitaxel, Tax; doxorubicin, Dox and cisplatin, Cis) treatment significantly decreased both the ovarian volume of mice and the number of primordial and antral follicles and accompanied with the ovarian fibrosis and reduced ovarian reserve in animal models. Subsequently, Tax, Dox, and Cis treatment can induce the apoptosis of ovarian granulosa cells (GCs), likely resulting from excessive reactive oxygen species (ROS) production-induced oxidative damage and impaired cellular anti-oxidative capacity. Thirdly, the following experiments demonstrated that Cis treatment could induce mitochondrial dysfunction through overproducing superoxide in GCs and trigger lipid peroxidation leading to ferroptosis, first reported in chemotherapy-induced ovarian damage. In addition, N-acetylcysteine (NAC) treatment could alleviate the Cis-induced toxicity in GCs by downregulating cellular ROS levels and enhancing the anti-oxidative capacity (promoting the expression of glutathione peroxidase, GPX4; nuclear factor erythroid 2-related factor 2, Nrf2 and heme oxygenase-1, HO-1). Our study confirmed the chemotherapy-induced chaotic hormonal state and ovarian damage in preclinical and clinical examination and indicated that chemotherapeutic drugs initiated ferroptosis in ovarian cells through excessive ROS-induced lipid peroxidation and mitochondrial dysfunction, leading to ovarian cell death. Consequently, developing fertility protectants from the chemotherapy-induced oxidative stress and ferroptosis perspective will ameliorate ovarian damage and further improve the life quality of cancer patients.

Luminescent carbon dots (L-CDs) with high quantum yield value (44.7%) and controllable emission wavelengths were prepared via a facile hydrothermal method. Importantly, the surface states of the materials could be engineered so that their photoluminescence was either excitation-dependent or distinctly independent. This was achieved by changing the density of amino-groups on the L-CD surface. The above materials were successfully used to prepare multicolor L-CDs/polymer composites, which exhibited blue, green and even white luminescence. In addition, the excellent excitation-independent luminescence of L-CDs prepared at low temperature was tested for detecting various metal ions. As an example, the detection limit of toxic Be 2+ ions, tested for the first time, was as low as 23 μM.

Primal–dual hybrid gradient (PDHG) and alternating direction method of multipliers (ADMM) are popular first-order optimization methods. They are easy to implement and have diverse applications. As first-order methods, however, they are sensitive to problem conditions and can struggle to reach the desired accuracy. To improve their performance, researchers have proposed techniques such as diagonal preconditioning and inexact subproblems. This paper realizes additional speedup about one order of magnitude. Specifically, we choose general (non-diagonal) preconditioners that are much more effective at reducing the total numbers of PDHG/ADMM iterations than diagonal ones. Although the subproblems may lose their closed-form solutions, we show that it suffices to solve each subproblem approximately with a few proximal-gradient iterations or a few epochs of proximal block-coordinate descent, which are simple and have closed-form steps. Global convergence of this approach is proved when the inner iterations are fixed. Our method opens the choices of preconditioners and maintains both low per-iteration cost and global convergence. Consequently, on several typical applications of primal–dual first-order methods, we obtain 4–95 × speedup over the existing state-of-the-art.

Background To investigate the role and regulatory mechanisms of fargesin, one of the main components of Magnolia fargesii , in macrophage reprogramming and crosstalk across cartilage and synovium during osteoarthritis (OA) development. Methods Ten-week-old male C57BL/6 mice were randomized and assigned to vehicle, collagenase-induced OA (CIOA), or CIOA with intra-articular fargesin treatment groups. Articular cartilage degeneration was evaluated using the Osteoarthritis Research Society International (OARSI) score. Immunostaining and western blot analyses were conducted to detect relative protein. Raw264.7 cells were treated with LPS or IL-4 to investigate the role of polarized macrophages. ADTC5 cells were treated with IL-1β and conditioned medium was collected to investigate the crosstalk between chondrocytes and macrophages. Results Fargesin attenuated articular cartilage degeneration and synovitis, resulting in substantially lower Osteoarthritis Research Society International (OARSI) and synovitis scores. In particular, significantly increased M2 polarization and decreased M1 polarization in synovial macrophages were found in fargesin-treated CIOA mice compared to controls. This was accompanied by downregulation of IL-6 and IL-1β and upregulation of IL-10 in serum. Conditioned medium (CM) from M1 macrophages treated with fargesin reduced the expression of matrix metalloproteinase-13, RUNX2, and type X collagen and increased Col2a1 and SOX9 in OA chondrocytes, but fargesin alone did not affect chondrocyte catabolic processes. Moreover, fargesin exerted protective effects by suppressing p38/ERK MAPK and p65/NF-κB signaling. Conclusions This study showed that fargesin switched the polarized phenotypes of macrophages from M1 to M2 subtypes and prevented cartilage degeneration partially by downregulating p38/ERK MAPK and p65/NF-κB signaling. Targeting macrophage reprogramming or blocking the crosstalk between macrophages and chondrocytes in early OA may be an effective preventive strategy.

Background The numbers of confirmed cases of coronavirus disease 2019 (COVID-19) and COVID-19 related deaths are still increasing, so it is very important to determine the risk factors of COVID-19. Dyslipidemia is a common complication in patients with COVID-19, but the association of dyslipidemia with the severity and mortality of COVID-19 is still unclear. The aim of this study is to analyze the potential association of dyslipidemia with the severity and mortality of COVID-19. Methods We searched the PubMed, Embase, MEDLINE, and Cochrane Library databases for all relevant studies up to August 24, 2020. All the articles published were retrieved without language restriction. All analysis was performed using Stata 13.1 software and Mantel–Haenszel formula with fixed effects models was used to compare the differences between studies. The Newcastle Ottawa scale was used to assess the quality of the included studies. Results Twenty-eight studies involving 12,995 COVID-19 patients were included in the meta-analysis, which was consisted of 26 cohort studies and 2 case–control studies. Dyslipidemia was associated with the severity of COVID-19 (odds ratio [OR] = 1.27, 95% confidence interval [CI] 1.11–1.44, P = 0.038, I 2  = 39.8%). Further, patients with dyslipidemia had a 2.13-fold increased risk of death compared to patients without dyslipidemia (95% CI 1.84–2.47, P = 0.001, I 2  = 66.4%). Conclusions The results proved that dyslipidemia is associated with increased severity and mortality of COVID-19. Therefore, we should monitor blood lipids and administer active treatments in COVID-19 patients with dyslipidemia to reduce the severity and mortality.

To address the issues of weak robustness and low accuracy of traditional SLAM data processing algorithms in weak texture environments such as low light and low contrast, this paper first studies and improves the data feature extraction method, optimizing the AGAST-based feature extraction algorithm to adaptively adjust the extraction threshold according to the gradient size of different data features. Meanwhile, a fusion-based incremental loop closure detection method is proposed, which integrates the similarity scores of multi-dimensional data features based on the Borda counting strategy, thereby enhancing the accuracy of loop closure detection. The performance of loop closure detection was evaluated on public datasets (such as KITTI sequences 00, 05, and 06), achieving an average AP value of 92.03%. The overall system performance was evaluated on the EuRoC dataset, with the results showing a root mean square error range from 0.0061 to 0.0281 m, demonstrating the excellent accuracy and robustness of the proposed method in large-scale data processing.

Traditional visual SLAM systems are predominantly designed for static environments, where they encounter challenges in dynamic scenes, leading to increased system errors and redundancy. This paper introduces a dynamic feature detection and filtering algorithm. Through a feature point selection and optimization strategy within quadtree nodes, high-response feature points are prioritized. Semantic information is leveraged to remove features on prior dynamic objects, and geometric constraints are applied to filter truly dynamic features. For unmatched features, an extension method is used, and high-confidence points are weighted to obtain feature point status information. Compared with the standard ORB-SLAM2 algorithm, our improved algorithm achieves over a 90% performance increase in highly dynamic environments, with absolute trajectory error performance improvements up to 96.84% in low-dynamic settings. Overall, our algorithm demonstrates superior adaptability and robustness in dynamic environments.