Search Results (198,521)

Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation for remote sensing images. In this paper, we propose a bidirectional feature fusion and enhanced alignment-based multimodal semantic segmentation model (BEMSeg) for remote sensing images. Specifically, BEMSeg first extracts image and text features by image and text encoders, respectively, and then the features are provided for fusion and alignment to obtain complementary multimodal feature representation. Secondly, a bidirectional feature fusion module is proposed, which employs self-attention and cross-attention to adaptively fuse image and text features of different modalities, thus reducing the differences between multimodal features. For multimodal feature alignment, the similarity between the image pixel features and text features is computed to obtain a pixel–text score map. Thirdly, we propose a category-based pixel-level contrastive learning on the score map to reduce the differences among the same category’s pixels and increase the differences among the different categories’ pixels, thereby enhancing the alignment effect. Additionally, a positive and negative sample selection strategy based on different images is explored during contrastive learning. Averaging pixel values across different training images for each category to set positive and negative samples compares global pixel information while also limiting sample quantity and reducing computational costs. Finally, the fused image features and aligned pixel–text score map are concatenated and fed into the decoder to predict the segmentation results. Experimental results on the ISPRS Potsdam, Vaihingen, and LoveDA datasets demonstrate that BEMSeg is superior to comparison methods on the Potsdam and Vaihingen datasets, with improvements in mIoU ranging from 0.57% to 5.59% and 0.48% to 6.15%, and compared with Transformer-based methods, BEMSeg also performs competitively on LoveDA dataset with improvements in mIoU ranging from 0.37% to 7.14%. Full article

Background: Femoral fractures often lead to complications such as altered pulmonary hemodynamics. Right ventricular global longitudinal strain (RV GLS), which correlates with pulmonary hemodynamics, indicates the subclinical function of the right ventricle (RV). This study aimed to investigate the predictive value of RV GLS for the risk of adverse clinical composite outcomes in patients with femoral fractures. Methods: Data were obtained from a prospective single-center cohort of patients hospitalized for femoral fractures and followed up for at least 1 year between March 2021 and October 2022. The primary outcome was the development of an adverse composite clinical event, which included pneumonia, pulmonary oedema or effusion, pulmonary thromboembolism, and all-cause mortality within the 1-year period following surgery. Results: Among the 163 patients, 36 (22.09%) experienced adverse composite clinical events during 1-year follow-up. The adverse outcome group demonstrated poorer RV GLS and RV free wall strain values than the non-adverse outcome group. The optimal cut-off value of RV GLS for predicting composite adverse clinical events was −12.55%. The cumulative composite event-free survival rate was significantly lower in the RV GLS ≥ −12.55% group (log-rank p-value = 0.003). After adjusting for confounding factors, multivariate Cox proportional hazards regression analyses showed that RV GLS ≥ −12.55% independently increased the risk of composite adverse clinical events by 2.65-fold. Conclusions: Poor RV GLS is a significant predictor of adverse clinical outcomes in patients with femoral fractures. Specifically, an RV GLS value of ≥ −12.55% indicated a substantially increased risk of adverse events. Full article

Soils are a finite resource that is under threat, mainly due to human pressure. Therefore, there is an urgent need to produce maps of soil properties, functions and behaviors that can support land management and various stakeholders’ decisions. Compaction is a major threat to soil functions, such as water infiltration and storage, and crops’ root growth. However, there is no general agreement on a universal and easy-to-implement indicator of soil susceptibility to compaction. The proposed indicators of soil compaction require numerous analytical determinations (mainly bulk density measurements) that are cost prohibitive to implement. In this study, we used data collected in numerous in situ topsoil observations during conventional soil survey and compared field observations to usual indicators of soil compactness. We unraveled the relationships between field estimates of soil compactness and measured soil properties. Most of the quantitative indicators proposed by the literature were rather consistent with the ordering of soil compactness classes observed in the field. The best relationship was obtained with an indicator using bulk density and clay (BDr₂) to define three classes of rooting limitation. We distinguished six clusters of topsoil behaviors using hierarchical clustering. These clusters exhibited different soil behaviors to compaction that were related to soil properties, such as particle-size fractions, pH, CaCO₃ and organic carbon content, cation exchange capacity, and some BDr₂ threshold values. We demonstrate and discuss the usefulness of field observations to assess topsoil behavior to compaction. The main novelty of this study is the use of large numbers of qualitative field observations of soil profiles and clustering to identify contrasting behavior. To our knowledge, this approach has almost never been implemented. Overall, analysis of qualitative and quantitative information collected in numerous profiles offers a new way to discriminate some broad categories of soil behavior that could be used to support land management and stakeholders’ decisions. Full article

The incidence of Neonatal Systemic Inflammatory Response Syndrome (SIRS) is a critical concern in neonatal care. This study aimed to identify maternal laboratory parameters predictive of SIRS in newborns, focusing on the establishment of diagnostic cutoffs and evaluating the predictive power of these biomarkers. This prospective cohort study was conducted from January 2023 to January 2024 across several regional hospitals specializing in neonatal care. It included 207 mother-newborn pairs, divided into groups based on the neonatal development of SIRS (66 cases) or its absence (141 controls). Key maternal parameters measured included inflammatory markers and liver enzymes, analyzed using standard biochemical methods. The study applied receiver operating characteristic (ROC) analysis to establish optimal cutoff values and conducted multivariate logistic regression to determine hazard ratios (HRs) for SIRS prediction, with adjustments for potential confounders. The study identified significant ROC/AUC values for several biomarkers. The neutrophil-to-lymphocyte ratio (NLR) demonstrated an AUC of 0.926, with a cutoff value of 3.64, achieving 81.8% sensitivity and 90.9% specificity (p < 0.001). The systemic immune–inflammation index (SII) showed an AUC of 0.819 and a cutoff of 769.12, with 75.8% sensitivity and 81.8% specificity (p < 0.001). Multivariate regression analysis highlighted that neonates with maternal SII values above this cutoff were three times more likely to develop SIRS (HR 3.09, 95% CI 2.21–4.17, p < 0.0001). Other notable biomarkers included dNLR and ALRI, with respective HRs of 1.88 (p = 0.018) and 1.75 (p = 0.032). These findings confirm the significant predictive value of specific maternal inflammatory markers for neonatal SIRS. These findings support the utility of these biomarkers in prenatal screening to identify neonates at increased risk of SIRS, potentially guiding preemptive clinical interventions. Full article

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like “renewable energy sources”, “optimization”, and “simulation” are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future. Full article

Abstract: Corn straw is one kind of agricultural by-product containing 70–80% insoluble dietary fiber (IDF). In order to develop corn straw dietary fiber, this study was conducted to increase soluble dietary fiber (SDF) yield and improve the structure, functional and prebiotic properties of IDF and SDF from corn straw treated by alkali oxidation treatment, enzymatic hydrolysis, microbial fermentation and the combination of these methods. The results demonstrated that the yield of SDF was significantly increased from 2.64% to 17.15% after corn straw was treated by alkali oxidation treatment + Aspergillus niger fermentation + cellulase hydrolysis, compared with untreated corn straw. The SDF extracted from corn straw treated by alkali oxidation treatment + Aspergillus niger fermentation + cellulase hydrolysis (F-SDF) exhibited a honeycomb structure, low crystallinity (11.97%), good antioxidant capacity and high capacities of water holding, water solubility and cholesterol absorption and promoted short-chain fatty acids production by chicken cecal microbial fermentation in vitro. F-SDF enhanced the antibacterial activity against Escherichia coli and Staphylococcus aureus proliferations of Lactobacillus plantarum when it was used as a substrate for Lactobacillus plantarum fermentation. It could be concluded that the combined treatments could increase SDF yield from corn straw and improve its functional and prebiotic properties. Full article

This study reports organic, nutrient, and coliform removal performances of two integrated wetlands designed to treat landfill leachate. Each integrated system included two components: a normal or electrode-integrated upflow-based wetland and a surface flow wetland (with internal baffle walls). The components were fully or partially filled with stone dust media and planted with Canna indica. Two hydraulic loading rates, i.e., 15 L and 60 L (per day), were applied. The integrated wetlands achieved a mean biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and coliform removal efficiency ranges of 89–94%, 95–97%, 85–91%, 91–98%, and 70–88%, respectively, within the applied loading ranges. The electrode-dependent system achieved better pollutant removal performances due to the influence of electrochemical-based bioreactions that fostered microbial decomposition. Nitrogen accumulation percentage (with respect to observed removal) in plant tissues ranged between 0.6 and 25%; phosphorus accumulation percentage was negligible, i.e., ≤0.009%. The chemical composition of the stone dust media supported nutrient adsorption. Stable nutrient removal performance was observed with both systems despite variable loading ranges due to pollutant removal in the upflow-based wetlands followed by controlled flow direction (induced by baffle walls) in the surface flow wetlands that triggered chemical and biological removals. Mean power density production ranged between 235 and 946 mW/m³ with the electrode-based integrated wetland system. In summary, this study demonstrates the application of integrated wetland systems to treat landfill leachate and the associated factors to achieve stable removal under variable loading ranges. Full article

Background and Objective: Segmentation of the femur in Dual-Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. Methods: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. Results: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging. Full article

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments. Full article

Vandammella animalimorsus is a Gram-negative and non-motile bacterium typically transmitted to humans through direct contact with the saliva of infected animals, primarily through biting, scratches, or licks on fractured skin. The absence of a confirmed post-exposure treatment of V. animalimorsus bacterium highlights the imperative for developing an effective vaccine. We intended to determine potential vaccine candidates and paradigm a chimeric vaccine against V. animalimorsus by accessible public data analysis of the strain by utilizing reverse vaccinology. By subtractive genomics, five outer membranes were prioritized as potential vaccine candidates out of 2590 proteins. Based on the instability index and transmembrane helices, a multidrug transporter protein with locus ID A0A2A2AHJ4 was designated as a potential candidate for vaccine construct. Sixteen immunodominant epitopes were retrieved by utilizing the Immune Epitope Database. The epitope encodes the strong binding affinity, nonallergenic properties, non-toxicity, high antigenicity scores, and high solubility revealing the more appropriate vaccine construct. By utilizing appropriate linkers and adjuvants alongside a suitable adjuvant molecule, the epitopes were integrated into a chimeric vaccine to enhance immunogenicity, successfully eliciting both adaptive and innate immune responses. Moreover, the promising physicochemical features, the binding confirmation of the vaccine to the major innate immune receptor TLR-4, and molecular dynamics simulations of the designed vaccine have revealed the promising potential of the selected candidate. The integration of computational methods and omics data has demonstrated significant advantages in discovering novel vaccine targets and mitigating vaccine failure rates during clinical trials in recent years. Full article

Background: Recent investigations have suggested that-sidedness is associated with the prognosis of colon cancer patients. However, the role of sidedness in surgical outcome is unclear. In this study, we tried to demonstrate the real role of sidedness in postoperative results for colon cancer patients receiving surgical intervention. Methods: This is a propensity score matching study using the database of the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) from 2009 to 2013. Sidedness groups including right-sided and left-sided colon cancer were created according to the associated diagnosis and procedure codes. Postoperative 30-day mortality, morbidity, overall complications, and total length of hospital stay were analyzed after performing propensity score matching. Results: Out of a total of 24,436 colon cancer patients who received associated operations, 15,945 patients had right-sided cancer and 8941 patients had left-sided cancer. Right-sided colon cancer patients were accompanied by more preoperative comorbidities including old age, female sex, hypertension, dyspnea, anemia, hypoalbuminemia, and a high American Society of Anesthesiologists grade (SMD > 0.1). Postoperative mortality, morbidities including re-intubation, bleeding, urinary tract infection and deep vein thrombosis, postoperative overall complications, and total length of hospital stay were significantly associated with right-sided cancer (p < 0.05). After 1:1 propensity score matching, postoperative mortality was not significantly different between right-sided cancer (2.3%) and left-sided cancer (2.4%) patients. The patients with left-sided colon cancer had significantly more postoperative morbidities, more overall complications, and longer total length of hospital stay. Conclusions: Poor clinical characteristics and postoperative outcomes were noted in right-sided cancer patients. After propensity score matching, left-sided cancer patients had worse postoperative outcomes than those with right-sided cancer. Full article

The hot rolled strip is a basic industrial product whose surface quality is of utmost importance. The condition of hot rolling work rolls that have been worn for a long time is the key factor. However, the traditional scheduling method controls risks to the surface quality by setting fixed rolling length limits and penalty scores, ignoring the wear condition differences caused by various products. This paper addresses this limitation by reconstructing a hot rolling-scheduling model, after developing a model for pre-assessment of the risk to surface quality based on the Weibull failure function, the deformation resistance formula, and real production data from a rolling plant. Additionally, Ant Colony Optimization (referred to as ACO) is employed to implement the scheduling model. The simulation results of the experiments demonstrate that, compared to the original scheduling method, the proposed one significantly reduces the cumulative risk of surface defects on products. This highlights the efficacy of the proposed method in improving scheduling decisions and surface quality of hot rolled strips. Full article

The abnormal growth of oligodendrocyte precursor cells (OPCs) significantly contributes to the progression of glioblastoma tumors. Hence, molecules that block OPC growth may be of therapeutic importance in treating gliomas. 2-Methoxyestradiol (2ME), an endogenous tubulin-interacting metabolite of estradiol, is effective against multiple proliferative disorders. Based on its anti-carcinogenic and anti-angiogenic actions, it is undergoing phase II clinical trials. We hypothesize that 2ME may prevent glioma growth by targeting OPC growth. Here, we tested this hypothesis by assessing the impact of 2ME on the growth of an OPC line, “Oli-neu”, and dissected the underlying mechanism(s). Treatment with 2ME inhibited OPC growth in a concentration-dependent manner, accompanied by significant upregulation in the expression of p21 and p27, which are negative cell-cycle regulators. Moreover, treatment with 2ME altered OPC morphology from multi-arm processes to rounded cells. At concentrations of 1uM and greater, 2ME induced apoptosis, with increased expressions of caspase 3, PARP, and caspase-7 fragments, externalized phosphatidylserine staining/APOPercentage, and increased mitochondrial activity. Flow cytometry and microscopic analysis demonstrated that 2ME triggers endoreduplication in a concentration-dependent fashion. Importantly, 2ME induced cyclin E, JNK1/2, and p53 expression, as well as OPC fusion, which are key mechanisms driving endoreduplication and whole-genome duplication. Importantly, the inhibition of p53 with pifithrin-α rescued 2ME-induced endoreduplication. The pro-apoptotic and endoreduplication actions of 2ME were accompanied by the upregulation of survivin, cyclin A, Cyclin B, Cyclin D2, and ppRB. Similar growth inhibitory, apoptotic, and endoreduplication effects of 2ME were observed in CG4 cells. Taken together, our findings provide evidence that 2ME not only inhibits OPC growth and triggers apoptosis, but also activates OPCs into survival (fight or flight) mode, leading to endoreduplication. This inherent survival characteristic of OPCs may, in part, be responsible for drug resistance in gliomas, as observed for many tubulin-interacting drugs. Importantly, the fate of OPCs after 2ME treatment may depend on the cell-cycle status of individual cells. Combining tubulin-interfering molecules with drugs such as pifithrin-α that inhibit endoreduplication may help inhibit OPC/glioma growth and limit drug resistance. Full article

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (O_BARPs) are often comparable to those of the pixels surrounding them (S_BARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of O_BARPs and S_BARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. Full article

Invasive candidiasis poses a worldwide threat because of the rising prevalence of antifungal resistance, resulting in higher rates of morbidity and mortality. Additionally, Candida species, which are opportunistic infections, have significant medical and economic consequences for immunocompromised individuals. This study explores the antifungal potential of chitosan to mitigate caspofungin resistance in caspofungin-resistant Candida albicans, C. krusei, and C. tropicalis isolates originating from human and animal sources using agar well diffusion, broth microdilution tests, and transmission electron microscope (TEM) analysis of treated Candida cells. Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) was performed to assess the expression of SAGA complex genes (GCN5 and ADA2) and the caspofungin resistance gene (FKS) in Candida species isolates after chitosan treatment. The highest resistance rate was observed to ketoconazole (80%) followed by clotrimazole (62.7%), fluconazole (60%), terbinafine (58%), itraconazole (57%), miconazole (54.2%), amphotericin B (51.4%), voriconazole (34.28%), and caspofungin (25.7%). Nine unique FKS mutations were detected, including S645P (n = 3 isolates), S645F, L644F, S645Y, L688M, E663G, and F641S (one isolate in each). The caspofungin minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values before chitosan treatment ranged from 2 to 8 µg/mL and 4 to 16 µg/mL, respectively. However, the MIC and MFC values were decreased after chitosan treatment (0.0625–1 µg/mL) and (0.125–2 µg/mL), respectively. Caspofungin MIC was significantly decreased (p = 0.0007) threefold following chitosan treatment compared with the MIC values before treatment. TEM analysis revealed that 0.5% chitosan disrupted the integrity of the cell surface, causing irregular morphologies and obvious aberrant changes in cell wall thickness in caspofungin-resistant and sensitive Candida isolates. The cell wall thickness of untreated isolates was 0.145 μm in caspofungin-resistant isolate and 0.125 μm in sensitive isolate, while it was significantly lower in chitosan-treated isolates, ranging from 0.05 to 0.08 μm when compared with the cell wall thickness of sensitive isolate (0.03 to 0.06 μm). Moreover, RT-qPCR demonstrated a significant (p < 0.05) decrease in the expression levels of histone acetyltransferase genes (GCN5 and ADA2) and FKS gene of caspofungin-resistant Candida species isolates treated with 0.5% chitosan when compared with before treatment (fold change values ranged from 0.001 to 0.0473 for GCN5, 1.028 to 4.856 for ADA2, and 2.713 to 12.38 for FKS gene). A comparison of the expression levels of cell wall-related genes (ADA2 and GCN5) between caspofungin-resistant and -sensitive isolates demonstrated a significant decrease following chitosan treatment (p < 0.001). The antifungal potential of chitosan enhances the efficacy of caspofungin against various caspofungin-resistant Candida species isolates and prevents the development of further antifungal resistance. The results of this study contribute to the progress in repurposing caspofungin and inform a development strategy to enhance its efficacy, appropriate antifungal activity against Candida species, and mitigate resistance. Consequently, chitosan could be used in combination with caspofungin for the treatment of candidiasis. Full article