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The antimethanogenic efficacy and fate of bromoform and its transformation products in rumen fluid
Abstract Enteric methane emissions from ruminant livestock are a significant source of atmospheric methane. Efforts to address rising atmospheric methane concentrations have led to an expansion of research into mitigating enteric methane production. One of the most effective approaches utilizes bromoform-containing feed supplements, such as the algae Asparagopsis spp., to inhibit methanogenesis in the rumen. Understanding the fate and persistence of bromoform in the rumen is important for developing safe, effective products and feeding strategies. This study conducted a series of in vitro rumen fluid experiments monitoring bromoform, dibromomethane, and bromomethane concentrations, methane production and several biochemical parameters to understand the inhibitory thresholds and degradation processes of these compounds. Analysis of the rumen fluid confirmed bromoform is rapidly dehalogenated. The half-life of bromoform was 26 min, coinciding with the production of dibromomethane accumulating to 22.1% of the initial bromoform amendment, but no bromomethane was detected. Dibromomethane demonstrated a considerably longer half-life of 775 min. In separate dose-response experiments, bromoform, dibromomethane and bromomethane all exhibited anti-methanogenic activity. Bromoform and dibromomethane produced sigmoidal-relationships between concentration and inhibition at approximately 1–2 µM, and yielded similar effective concentration values (EC50s) for antimethanogenic activity. Experiments using Asparagopsis taxiformis algae revealed less accumulation of bromoform and formation of dibromomethane, likely driven by a slower release from the seaweed material. The A. taxiformis dose response was less effective at inhibiting methane per mole of bromoform added compared with direct bromoform additions. These results have significant implications for understanding the dynamics of bromoform-mediated methane inhibition and will aid the development of effective halocarbon additives, feeding strategies, and testing protocols for bromoform and its degradation byproducts.
A multi model ensemble reveals net climate benefits from regenerative practices in US Midwest croplands
Beta sitosterol inhibits the proliferation and migration of synoviocytes in rheumatoid arthritis via lactylation of GPI
The impact of increasing urban surface albedo on outdoor air and surface temperatures during summer in newly developed areas
Abstract This study investigates the influence of increasing road surface albedo on outdoor air and surface temperatures in residential areas, taking into account constraints on broader environmental modifications. Urban albedo, which is determined by spatial geometry and material reflectance, influences the amount of solar radiation bouncing back into the atmosphere. Field measurements were conducted on-site to document Air Temperature (Ta), Wind Speed (WS), Relative Humidity (RH), Mean Radiant Temperature (MRT) providing the basis for validating simulation models. The urban geometry was reconstructed from real site data and simulated using a hybrid modeling approach, combining Ladybug with Grasshopper for Surface Temperature (Ts), MRT, and Universal Thermal Climate Index simulations, and ENVI-met for Ta, RH, and WS simulations. ENVI-met outputs were integrated into Grasshopper to achieve high-accuracy environmental modeling. Results demonstrate that increasing pavement albedo from 0.12 to 0.50 reduced Ts by up to 12.94 °C at peak solar hours and lowered Ta by a maximum of 1.96 °C during the day. The research addresses a critical gap by focusing solely on altering material reflectivity without changing urban morphology or adding any canopies either structured or vegetation. The findings confirm that enhancing surface albedo is an effective method to reduce daytime heat trapping & accumulation, and shortwave radiation absorption which mitigate the Urban Heat Island phenomenon.
Quality and content evaluation of thyroid eye disease treatment information on TikTok and Bilibili
Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10
Abstract To address the challenges of low performance in vehicle image detection from UAV aerial imagery, difficulties in small target feature extraction, and the large parameter size of existing models, we propose the OSD-YOLOv10 algorithm, an enhanced version based on YOLOv10n. The proposed algorithm incorporates several key innovations: First, we employ online convolutional reparameterization to construct the OCRConv module and design a lightweight feature extraction structure, SPCC, to replace the conventional C2f module, thereby reducing computational load and parameter count. Second, we integrate an efficient dual-layer feed-forward hybrid attention module to enhance the model’s feature extraction capabilities. We also construct a dual small-target detection layer that combines shallow and ultra-shallow features to improve small-target detection. Finally, we introduce the DySample dynamic upsampling module to enhance feature fusion in the neck network from a point sampling perspective. Extensive experiments on the VisDrone-DET2019 and UAVDT datasets demonstrate that OSD-YOLOv10 achieves a 40.7% reduction in parameter count and a 3.6% decrease in floating-point operations, while improving accuracy and mean average precision by 1.3% and 1.6%, respectively. Compared to other YOLO series and lightweight models, OSD-YOLOv10 exhibits superior detection accuracy and lower computational complexity, achieving an optimal balance between high accuracy and low resource consumption. These advancements make it particularly suitable for deployment in UAV onboard hardware for vehicle target detection tasks. Code will be available online (https://github.com/Z76y/OSD-YOLO).
Change in elevation predicts 100 km ultra marathon performance
Three dimensional analysis on the deformation of the master cast during maxillary complete denture fabrication
Correction: Biotransformation of medicarpin from homopterocarpin by Aspergillus niger and its biological characterization
Amniotic and cervical fluids progranulin levels in pregnancies complicated by spontaneous preterm delivery with respect to intra-amniotic complications—a retrospective cohort study
Abstract The main aim of the study was to determine progranulin levels in amniotic and cervical fluid samples from pregnancies complicated by preterm prelabor rupture of membranes (PPROM) or preterm labor with intact membranes (PTL), with concomitant microbial invasion of the amniotic cavity and/or intra-amniotic inflammation. A total of 104 and 108 women with PPROM and PTL, respectively, were included. Paired amniotic and cervical fluid samples were obtained using transabdominal amniocentesis and Dacron polyester swabs, respectively. Progranulin levels were assessed with an enzyme-linked immunosorbent assay. Women with PPROM and PTL were divided into subgroups based on microbial invasion of the amniotic cavity and/or intra-amniotic inflammation. Differences in progranulin levels among the PPROM and PTL subgroups were found in amniotic fluid: (a) PPROM: intra-amniotic infection: 51.8 pg/mL, sterile intra-amniotic inflammation: 52.8 pg/mL, colonization: 36.4 pg/mL, and negative amniotic fluid: 35.0 pg/mL; p < 0.0001; (b) PTL: intra-amniotic infection: 75.3 pg/mL, sterile intra-amniotic inflammation: 54.0 pg/mL, and negative amniotic fluid: 39.1 pg/mL; p < 0.0001. The corresponding differences were not found in cervical fluid: (a) PPROM: p = 0.14; (b) PTL: p = 0.53. In conclusion, amniotic fluid progranulin levels increased in PPROM and PTL cases with concomitant intra-amniotic inflammation, regardless of whether microbial invasion of the amniotic cavity was present or absent.
Author Correction: Evaluation of LNR and modified N stage systems for prognostic stratification of metastatic lymph nodes in stage III colorectal Cancer
Association between monocyte to high-density lipoprotein cholesterol ratio and thyroid function: a cross-sectional study
Sericin-coated MnO2@CeO2 nanocatalysts enable pH-responsive and synergistic vincristine delivery for lung cancer therapy
Construction of a macrophage-related prognostic signature and assessment of immune checkpoint inhibitor efficacy of HCC
Population-based spectral characteristics of normal interictal scalp EEG inform diagnosis and treatment planning in focal epilepsy
Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics
Abstract Low-dose computed tomography (LDCT) has gained considerable attention for its ability to minimize patients’ exposure to radiation thereby reducing the associated cancer risks. However, this reduction in radiation dose often results in degraded image quality due to the presence of noise and artifacts. To address this challenge, the present study proposes an LDCT image denoising method that leverages a pixel-level nonlocal self-similarity (NSS) prior in combination with a nonlocal means algorithm. The NSS prior identifies similar pixels within non-local regions, which proves more feasible and effective than patch-based similarity in enhancing denoising performance. By utilizing this pixel-level prior, the method accurately estimates noise levels and subsequently applies a non-local Haar transform to execute the denoising process. Furthermore, the study incorporates an enhanced version of a recently proposed nonlocal means algorithm. This revised approach uses discrete neighbourhood filtering properties to enable efficient, vectorized, and parallel computation on modern shared-memory platforms thereby reducing computational complexity. Experimental evaluations on publicly available benchmark dataset NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge demonstrate that the proposed method effectively suppresses noise and artifacts while preserving critical image details. Both visual and quantitative comparisons confirm that this approach outperforms several state-of-the-art techniques in terms of image quality and denoising efficiency.
Relationship between breakfast skipping and hyperuricemia in Korean adults: results from KNHANES 2016–2023
High-resolution source inversion of 2024 Noto Peninsula earthquake tsunami with modeling error corrections
Abstract On New Year’s Day, January 1, 2024, a significant earthquake struck the Noto Peninsula, Japan, triggering a tsunami with a maximum height of exceeding 5 m along the coast. This rare and destructive event is associated with several active submarine faults in the area, which are inferred to have ruptured simultaneously. However, due to the lack of direct observation from the ocean, the precise nature of the initial disturbance remains uncertain. In this study, we utilized the latest high-resolution adjoint synthesis inversion technique to analyze the tsunami’s initial state based on observed water level and velocity fluctuations. Furthermore, we propose a method to correct modeling errors in amplitude and phase, thereby reducing misfits in trace heights: the geometric mean $$K$$ improves from 1.16 to 0.99, and the geometric standard deviation $$\kappa$$ decreases from 1.34 to 1.29. Our estimated tsunami source model identified three distinct uplift peaks, each approximately 3 m in height, in the offshore region. The model accurately reproduced both offshore and onshore observation data. This research provides a robust framework for understanding tsunami generation mechanisms and improving hazard assessment models. By offering accurate initial conditions for tsunami simulations, our findings contribute to better disaster preparedness and mitigation strategies, particularly for coastal regions prone to similar events.