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Mangrove species classification using a proposed ensemble U-Net model and Planet satellite imagery: A case study in Ngoc Hien district, Ca Mau province, Vietnam
Land cover and plant species identification using satellite images and deep learning approaches have recently been a widely addressed area of research. However, mangroves, a specific species that have significantly declined in quantity and quality worldwide despite their numerous benefits, have not been the subject of attention. The novelty of this research is to deal with this species based on an advanced deep learning solution (a proposed ensemble U-Net model) and a high-resolution Planet satellite imagery (5 m x 5 m) in a case study of Ngoc Hien district, Ca Mau province, Vietnam. Twelve single U-Net backbone models were trained, and three quantitative metrics (Intersection over Union, F1-score, and Overall Accuracy) were used to evaluate. The findings indicate that three out of twelve models (MobileNet, SEResNeXt-101 and Efficientnet-B7) experienced the most efficient assessment results for identifying all classes, in which the MobileNet model was the best. These models were applied for the ensemble model’s development. The ensemble model’s quantitative assessment metrics increased considerably by about 3–10% compared to the single-component models. The IoU, F1-score, and OA values of this model were 80.08%, 95.82%, and 95.90%, respectively. Three classes of mangrove species (Avicennia alba, Rhizophora apiculate, and mixed mangroves) in the ensemble model had more uniform assessment results. In conclusion, to achieve optimal classification outcomes, a land-cover map comprising mangrove species is possibly established using the proposed ensemble model, while a distribution map of mangrove species enables to be developed using the MobileNet model.
Spotlight on Mechanosterics: A Bulky Macrocycle Promotes Functional Group Reactivity in a [2]Rotaxane
An Expedient Synthesis of the Antimitotic Natural Products Sarcodictyin and Eleutherobin, and Carbohydrate Analogues
Phosphorus-Driven Dual d-Band Harmonization for Reversible Electrocatalysis
NMR-Guided Studies to Establish the Binding Interaction between a Peptoid and Protein
Real-world super-resolution with VLM-based degradation prior learning
Comparative effect of dietary patterns on selected cardiovascular risk factors: A network study
Sex-specific differences in performance and pacing in the world’s longest triathlon in history
Sipeimine reduces ethanol-induced gastric ulcer in mice by suppressing Jak-Stat activation and restoring gut microbiota balance
Abstract Long-term excessive alcohol intake can directly injure the gastroduodenal mucosa, causing gastric erosions, gastric ulcers, and gastrorrhagia. Fritillaria ussuriensis Maxim is a famous traditional Chinese medicine and health food produced in China. Sipeimine is an alkaloidal component of Fritillaria ussuriensis Maxim. This research aimed to investigate the protective effects of sipeimine on ethanol-induced gastric ulcers in mice. The results displayed that sipeimine could alleviate gastric tissue damage and decrease the levels of SOD, MDA, IL-6, IFN-γ, TNF-α, and IL-1β. Sipeimine treatment also adjusted macrophage polarization and the balance of Th17/Treg cell by reducing the expression of Jak1/2, p-Jak1/2, Stat1/3, and p-Stat1/3. Moreover, sipeimine could increase the abundance of Lactobacillus_johnsonii and decrease the abundance of Bacteroides_vulgatus in the gut microbiota. Meanwhile, sipeimine treatment significantly decreased the abundance of Rodentibacter_heylii and Streptococcus_cuniculi in the gastric microbiota. In conclusion, sipeimine can improve gastric ulcers by suppressing the Jak-Stat pathway, reversing gut-gastro microbiota dysbiosis, inhibiting macrophage M1 polarization, maintaining the balance of Th17/Treg cell, and lessening sustained inflammatory injury.
Identification of new selective CD36 inhibitors to potentiate HER2-targeted therapy in HER2-positive breast cancer
Deep learning based localisation and classification of gamma photon interactions in thick nanocomposite and ceramic monolithic scintillators
Abstract Accurate localisation of the first point of interaction (FPoI) of incident gamma photons in monolithic scintillators is crucial for many radiation-based imaging applications - in particular, accurate estimation of the lines of response in positron emission tomography (PET). This is particularly challenging in thick nanocomposite and ceramic scintillator materials, which exhibit high levels of Rayleigh scattering compared to monocrystalline scintillators. In this work, we evaluate deep neural network-based approaches for (1) classifying the mode of photon interaction using an InceptionNet-based classifier and (2) accurately estimating the location of the FPoI based on scintillation photon distributions in several monolithic nanocomposite and ceramic scintillators using both CNN- and InceptionNet-based regression networks. The classifier was able to correctly categorise single-energy deposition events with an accuracy $$\ge$$ 90.1%, two-deposition interactions with an accuracy $$\ge$$ 77.6% and three-plus deposition interactions with an accuracy $$\ge$$ 66.7%. Across the evaluated materials, median total localisation error ranged from 0.58 mm to 2.91 mm with the CNN and 0.59 mm to 2.10 mm with InceptionNet, assuming 50% detector quantum efficiency. Localisation in nanocomposites using the InceptionNet-based regression network improved the most relative to previously-reported results based on classical techniques, in some cases approaching the accuracy achieved with ceramic scintillators.
Integrative metabolomics and system pharmacology reveal the antioxidant blueprint of Psoralea corylifolia
Study on statistical analysis and prevention of coal and gas outburst accidents in Guizhou Province
Suppression of COVID-19 death incidence on open west coasts in the USA
Abstract The drivers behind the large-scale patterns of COVID-19 infections are largely unknown. Earlier studies have shown a connection between continentality, a measure for oceanic air influence over land, where lowest continentality implies highest oceanic influence, and COVID-19. In Europe, open west coasts with lowest continentality had the lowest COVID-19 incidence. We test if this applies to the US. We use a combination of geographical information systems and statistics, and data for every US county, to assess the connection between the COVID-19 death incidence and continentality. We normalize for known factors that influence COVID-19 local scale death incidence, namely the socio-economic status, population aged over 65, and the index of urbanization (crowding). We find that open west-coasts in the US, where continentality index values are low, had the lowest COVID-19 death incidence, rising non-linearly with rising continentality values, with highest death incidence in areas with the highest continentality, in north-central USA. The influence of oceanic air was associated with lower COVID-19 death incidence on the west coast of the US. These findings suggest that oceanic influence may be an important environmental determinant of spatial variations in COVID-19 death incidence and provide a contribution to studies on the relationship between oceans and health.
Perfluorobutane CEUS for early-stage cervical lymphoma: diagnostic value of the postvascular phase starfield sign
Abstract Early differentiation of cervical lymphoma from benign lymphadenopathy remains challenging on conventional imaging. This study assesses the diagnostic efficacy of perfluorobutane contrast-enhanced ultrasound (CEUS) in differentiating early-stage cervical lymphoma from benign lymph nodes (LNs). From November 2023 to January 2025 we prospectively enrolled patients suspected of having cervical lymphoma based on ultrasound (US) findings and scheduled for LN biopsy. All patients underwent CEUS to evaluate LN vascular (5–60 s post-injection) and postvascular (10–30 min post-injection) phases before biopsy. Histopathology served as the reference standard. Diagnostic performance metrics, including sensitivity, specificity, and accuracy, were calculated. Logistic regression analyzed the area under the receiver operating characteristic curve (AUC) for US, CEUS, and combined features. Forty-seven LNs (23 lymphomas, 24 benign) were analyzed. The sensitivity of the postvascular phase starfield sign was 91.30%, specificity was 83.33%, positive predictive value was 84.00%, negative predictive value was 90.91%, and the AUC was 0.87 (95% CI 0.76–0.98). The AUC for CEUS was 0.89 (95% CI 0.79–1.00), and the AUC for the combination of postvascular phase and US features was 0.92 (95% CI 0.82–1.00), significantly higher than that for US features alone (AUC, 0.68; 95% CI 0.53–0.84; P < 0.05). Perflubutane CEUS can effectively distinguish between cervical lymphoma and benign LNs. The postvascular phase starfield sign demonstrates significant diagnostic efficacy and could improve clinical management strategies.
Technological limitations of solid-source chemical vapor deposition of van der Waals heterostructures
Abstract The large-scale synthesis of van der Waals heterostructures (vdWHSs) is required to adopt these materials in electronic devices. However, the repeatable and controllable growth of vdWHSs has proven challenging. Here, we investigate the technological aspects of solid-source chemical vapor deposition (CVD) of two-dimensional heterostructures, with WS2/graphene and MoS2/graphene as examples. We show that by modification of one variable at least one another is unintentionally altered. For example, change in the growth pressure influences the evaporation rate of sulfur and shifts the position of one of the growth zones. We also perform a statistical screening of the 11 process parameters, indicating which of them impact the evaporation of the precursors. The screening indicates that the evaporation depends on weight of growth promoter (NaCl), growth temperature, precursors temperature, time difference between main and sulfur growth zones reaching the set temperatures, pressure, carrier gas flow, and process time. Finally, the five consecutive, identical growth processes show the seemingly inherent variability in synthesizing vdWHSs. We suggest that the high but limited airtightness of the CVD system or the substrate features can cause repeatability issues. Our study can facilitate future research on van der Waals heterostructures growth.