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Pharmacological investigation of bergapten isolated from Glehnia littoralis against periodontitis
Regional variations in the diet of the South Polar Skua (Stercorarius maccormicki) in the Ross Sea region, Antarctica
The South Polar Skua ( Stercorarius maccormicki ) is an opportunistic feeder, predator, and scavenger widely distributed in the coastal ecosystems of Antarctica. However, although some studies have explored its foraging behavior, many aspects, including regional variations, remain insufficiently understood. Thus, this study aimed to characterize regional variations in the dietary composition of South Polar Skuas breeding at sites along the Northern Victoria Land coast in the Ross Sea, where environmental conditions vary markedly among colonies. The dietary composition and foraging characteristics of the South Polar Skua populations in the Ross Sea region were investigated by analyzing stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopes in blood samples. The values were then used to quantify isotopic niches and estimate dietary contributions. Results revealed significant regional variations in the dietary composition of skuas. Compared with skuas at other sites, skuas at Cape Möbius showed higher δ¹³C values, suggesting a greater reliance on fish and the placenta or carcasses of Weddell seals for food. While δ¹⁵N values did not significantly differ among skuas at various sites, skuas at Cape Möbius and Cape Washington had higher values than those at other sites, suggesting access to higher trophic-level prey. Site-specific dietary contributions were revealed, with Adélie Penguin eggs and fish dominating the diet at Cape Hallett and Inexpressible Island, whereas Emperor Penguin eggs and fish were more prominent at Cape Washington. Our findings demonstrate that the species exhibits site-specific foraging patterns shaped by local prey availability. These results provide novel insights into the trophic ecology of this top predator and contribute to a broader understanding of the spatial dietary variation in Antarctic seabirds.
In Silico identification of inhalable small-molecule IL-33/ST2 antagonists for severe type-2-high asthma endotypes
Emotion meets coordination: Designing multi-agent LLMs for fine-grained user sentiment detection on social media
Social media platforms have become central channels for emotional communication, posing new challenges for fine-grained sentiment analysis due to their high contextual variability, multimodal content, and pervasive ambiguity. Traditional end-to-end sentiment models often struggle to capture compositional or conflicting emotional cues in user-generated texts. This study presents a modular multi-agent architecture for sentiment analysis, implemented with the LLaMA-3.3-70B-Instruct model and guided by system-level design principles. The framework decomposes emotion inference into three coordinated stages, perception, reasoning, and resolution, each managed by a specialized agent trained with parameter-efficient tuning strategies. A meta-agent mediates conflicting predictions through a coordination protocol based on confidence estimation and discourse consistency, enabling adaptive consensus formation. Evaluations on the GoEmotions v2, SemEval-2024, and Twitter benchmarks demonstrate that the proposed system achieves higher accuracy, robustness, and interpretability compared with existing baselines. These findings indicate that architectural decomposition combined with collaborative reasoning enhances reliability and transparency in sentiment analysis, offering a scalable pathway toward intelligent and emotionally aware computational systems.
High palmitate induces ferroptosis in RIN-m5f cells via miR-3584-5p-mediated suppression of AQP7
Trends in usage and regional variations in Ethiopian animal feeding practices: A 15-year analysis for informed policy
Livestock feeding practices play a pivotal role in sustaining agricultural productivity and food security in Ethiopia. However, the sector continues to face structural challenges, including a heavy reliance on traditional feed resources and pronounced regional disparities. This study utilized secondary data from the Agricultural Sample Surveys conducted annually by the Central Statistical Agency (2004/05–2018/19) to assess feeding practices, examine regional variations, and analyze long-term trends. Descriptive statistics, trend analysis, and correspondence analysis were applied. Different data analytical techniques were employed, including descriptive statistics, trend analysis, and Correspondence Analysis (CA), were employed to monitor livestock feeding practices, examine the regional differences, and explore trends over the long horizon. Results showed a decline in reliance on green fodder (from 40.3% to 36.9%) and crop residues (from 33.5% to 32.0%), accompanied by a modest increase in the use of improved feeds (0.15% to 0.90%) and agro-industrial by-products (2.7% to 4.6%). Regional differences were substantial: Afar predominantly used green fodder (82%), while Harari relied more on crop residues (41%). In high-livestock-population regions such as Amhara, Oromia, and SNNP, feeding practices varied according to resource availability and management strategies. These findings underscore the need for targeted, region-specific interventions to enhance feed availability, promote adoption of sustainable and cost-effective feeding systems, and address persistent demand–supply imbalances. The evidence also offers insights for policy reforms in other agriculture-based developing economies.
Physicochemical characteristics and mechanism analysis of loess at different high-temperature stages
Relationship between land use type and bacterial composition in adjacent streams and riparian zones
Anthropogenic activities can negatively impact riparian and stream ecosystems, resulting in declines in biodiversity and certain ecosystem functions. Microbiomes in these environments play crucial roles in primary production, nutrient cycling, and maintaining air, soil, and water quality. While previous studies have examined the effects of land use on streams and soil microbiota, few have evaluated the effects of soil microbiota on aquatic ecosystems based on land use in the riparian systems. In this study, we characterized bacterial composition in six small to moderate-sized streams in both urban and agricultural land use areas using 16S rRNA gene amplicon sequencing and related this to measured physicochemical variables in these environments. Bacterial composition was comparable in soil samples collected at 3m and 1m from the river and in edge and sediment samples, but these differed significantly from bacterial composition in adjacent water. Bacterial alpha diversity (Shannon index) in streams was higher when adjacent to agricultural sites than urban sites, but no effect of land use type on bacterial alpha diversity was observed in soil samples. On the other hand, land use and location had significant impacts on bacterial composition in both soil and water samples. Furthermore, in our sampling sites, stream bacterial composition in agricultural sites was significantly influenced by NH 3 and NO 3 -NO 2 concentrations. These findings raise the possibility that aquatic bacterial function may be modified/influenced even when adjacent soil microbiomes appear relatively unaffected. Moreover, given the sensitivity of the water microbiota to land use variation, our results suggest that aquatic bacterial composition and diversity can serve as a powerful bioindicator for assessing riparian impacts on ecosystem health.
Autofluorescence and Fourier transform infrared analyses trace dietary fluorophores and reveal plastic contamination in the gut of mosquito larvae
Effects of Trichoderma harzianum and Azospirillum brasilense on tomato growth, fruit quality, yield, and water productivity under deficit irrigation
Abstract This study evaluated tomato plants’ physiological and biochemical changes influenced by the Trichoderma harzianum and the Azospirillum brasilense . This study aimed to determine whether microbial inoculation with T. harzianum and A. brasilense can mitigate water-deficit stress and improve tomato growth, yield, and crop water productivity under different irrigation regimes. The irrigation regime was applied at four levels: no irrigation, 50%, 75%, and 100% of the water requirement (WR), and biological fertilizer was applied in four treatments: control, Trichoderma, Azospirillum, and Trichoderma + Azospirillum. The experiment was conducted as a factorial design, and the effects of irrigation regime and biological fertilizer treatments were statistically significant for most measured traits. The results demonstrated that the highest fresh and dry root weights and leaf area, were observed under the 100% WR combined with the application of biological fertilizers, particularly Trichoderma . Additionally, chlorophyll content was higher under a 100% WR with biological fertilizers. In contrast, The highest contents of carotenoids (22%), anthocyanins (18%), glucose (79%), sucrose (96%), and total sugars (121%) in leaves were observed under no-irrigation conditions. Conversely, the assessment of fruit characteristics revealed that the highest fresh and dry fruit weights, fresh fruit yield and Wp were achieved under the 100% WR combined with applying biological fertilizers, particularly Trichoderma . Azospirillum treatment and combining Trichoderma with Azospirillum achieved the lowest levels of fruit firmness, total soluble solids, and anthocyanin in fruits under a 100% WR. Moreover, the Trichoderma treatment was able to achieve similar performance under 75% WR conditions as the control treatment under 100% WR. Overall, the results indicate that Trichoderma inoculation plays a key role in improving tomato physiological performance and water productivity under different irrigation regimes, with potential implications for sustainable water management.
A scalable and secure federated learning authentication scheme for IoT
Retraction Note: Probing the practice and factors associated with perineal wound care among postpartum women in public health facilities in Ethiopia
Enhancing subscription fraud detection through ensemble learning the case of Ethio telecom
Abstract Telecommunication companies globally face the critical challenge of subscription fraud, which threatens both financial stability and national security. This research addresses this issue by developing an advanced fraud detection model specifically for Ethio Telecom. The model utilizes Ensemble and Adaptive Learning techniques to enhance detection accuracy by combining multiple classifiers. The study used a dataset of 1,000,000 Call Detail Records (CDRs) collected over two months known for increased fraudulent activity3. After filtering out irrelevant data and aggregating multiple call records per subscriber, the dataset was refined to 349,164 records. Initially, 16 features were analyzed, with four excluded for lacking relevance. The remaining 11 features, excluding the target variable, underwent preprocessing including data cleaning, transformation, and balancing4. Feature selection, utilizing Correlation Matrix and Random Forest importance analysis, led to the removal of four additional features, resulting in a final set of 8 key features, including INT_DIALLED, RATIO_INT_TOTAL, and RATIO_UNIQUE_TOTAL4. Three individual models, namely Decision Tree (DT), Logistic Regression (LR), and Artificial Neural Network (ANN), were implemented alongside ensemble methods such as Bagging, Boosting, Stacking, and Voting, and adaptive models like Hoeffding Tree and Adaptive Random Forest45. The findings of this research recommend Stacking and Adaptive Random Forest (ARF) as robust tools for subscription fraud detection.
Photoinduced proton transfer in differently structured water: an EPR approach to solving a classic problem
Epidemiology of Kudoa septempunctata food poisoning in Japan from 2013 to 2023
Abstract Kudoa septempunctata , a parasite found in olive flounder, poses a growing food safety risk in East Asia, particularly in Japan and South Korea. K. septempunctata poisoning caused by raw fish consumption causes brief gastrointestinal symptoms. However, long-term, national-scale aggregated epidemiological data for K. septempunctata food poisoning are limited. In this retrospective study, we examined the recent epidemiological trends and characteristics of K. septempunctata food poisoning cases reported in Japan between January 2013 and December 2023. Ministry of Health “Foodborne Illness Statistical Data” were assessed for case counts, outbreaks, and implicated foods. Reported cases totaled 2009, reaching a peak in 2014 (429 cases) then declining to < 100 cases during the COVID-19 pandemic. October had the highest number of monthly reports. Flounder, particularly sashimi and sushi, were implicated in 99% of cases. The highest case counts occurred in Yamaguchi, Osaka, and Fukuoka prefectures (160, 155, and 154, respectively). Tottori, Shimane, Yamaguchi and Oita prefectures had the highest incidence rates (14.3, 10.9, 10.7, and 10.7 per 1,000,000 population, respectively). Prefectures along the Sea of Japan typically reported higher incidence rates. This study highlights the importance of continued surveillance and reporting of K. septempunctata poisoning, and the need to consider Kudoa infections in the differential diagnosis of food poisoning cases involving raw fish consumption.