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Daily briefing: Plastics pollution talks end in stalemate
GDNF attenuates a-synuclein aggregation-induced damage to VTA-NAc dopaminergic transmission and alleviates depression-like behaviors in mice
Eyes in the sky: Drone monitoring of the largest gharial and mugger populations in the East Rapti River, Chitwan National Park
Drone-based aerial monitoring can play a pivotal role in scaling up efforts to monitor species at risk. In this study, we assessed the population size, occupancy, and spatial interactions of gharials and muggers in the Eastern Rapti River and its tributaries within Chitwan National Park, complying with national regulations. Using a Wingtra Tail-Sitter Vertical Take-Off and Landing fixed-wing drone, we surveyed a 73-km river stretch during the species’ basking period. The drone captured 24,129 photographs across 27 flight missions, covering 702.66 km and 44.68 km², of which 153 contained dorsal images of gharials (77) and muggers (76). An experienced image analyst identified and counted 323 crocodiles (205 gharials and 118 muggers) from the images. The encounter rates were 14.33 gharials and 9.95 muggers detections per 1 hour of drone flight time. To measure habitat-use through an occupancy framework, we divided the 73-km river stretch into 809 grid cells of 0.04 km² each. The site-level probabilities of habitat-use were 0.47 for gharials and 0.24 for muggers. As anticipated, both species co-occurred spatially along the Eastern Rapti River during the winter season, with a spatial interaction factor (SIF) of 1.94. This study demonstrates the effectiveness of drones in collecting high-resolution ecological data—both spatial and temporal—for assessing population parameters and monitoring threatened crocodile species at scale. Drones offer a cost-effective and less labor-intensive (~US$ 0.61 per km) alternative to traditional ground-based surveys (~US$ 21 per km). Integrating machine learning with drone surveys for automated image analyses has significant potential to further reduce costs and increase efficiency and could strengthen conservation efforts across South Asian River system.
Study on effects of the train-induced airflow on the temperature field of single-track high-speed railway tunnels in cold regions
Abstract High-speed trains bring cold air into tunnels, significantly affecting the temperature field in cold-region tunnels, leading to risks of lining freeze-thaw damage, track deformation, and safety hazards, which is detrimental to thermal insulation design. Using the Kunlunshan Tunnel as a case study and based on unsteady flow theory, this study constructed the Navier-Stokes equations and proposed a theoretical calculation method for train-induced airflow. Dynamic mesh technology was adopted to develop an “equivalent wind speed” train-induced-airflow-temperature coupling computational model. The main idea of this model is to divide the effect of external wind speed on the tunnel into three distinct phases: the train-induced airflow phase, the residual airflow phase, and the natural wind phase. During each phase, the changes in wind speed are governed by the principle of conservation of cold air volume and converted into a steady airflow equivalent for that specific phase. This model was used to investigate the effects of train frequency, ground temperature of surrounding rock, external temperature, and external wind speed on the temperature field variation in single-track high-speed railway tunnels in cold regions. After verification, the temperature field inside the tunnel calculated by the “equivalent wind speed” method was in good agreement with the measured data, and can be used for subsequent numerical simulation calculations. The results show that when the interval between two trains is less than 20 min, the average temperature in the tunnel decreases by approximately 1.71 ℃ due to the significant impact of train-induced airflow. The impact of the ground temperature of surrounding rock on the longitudinal temperature gradient in cold-region tunnels ranges from 0.059 ℃ to 1.13 ℃ for every 100 m, and the ranges for external temperature and external wind speed are 0.080 ℃ to 2.286 ℃ and 0.002 ℃ to 1.134 ℃, respectively.
Investigating the role of FOX gene family in development and stress response in Labeo rohita: A multi-faceted analysis of phylogeny and genome characterization
The forkhead box (FOX) gene family of transcription factors regulates muscle development, immune responses, and metabolic processes across species. Despite extensive studies on FOX genes in other organisms, their evolutionary and functional roles in Labeo rohita, an economically and ecologically important freshwater fish, remain unclear. Owing to its unique physiological and ecological traits, L. rohita is an ideal model for exploring these roles. Here, we present the first computational analysis of the FOX gene family in L. rohita, identifying 21 FOX genes. Physicochemical analysis revealed that most FOX proteins have a basic nature except for FOX A3, D3, I2, O1, O3, O4, P1, and P2. Instability index analysis indicated that all FOX proteins are unstable (values > 40), while hydrophobicity assessment showed that except FOX O1, all proteins are hydrophobic. Phylogenetic analysis grouped FOX homologs into 11 major clades with other vertebrates. All proteins exhibited structural homogeneity by sharing the Forkhead Box domain. Gene structure comparisons revealed seven duplicated pairs, and Circos analysis demonstrated organization into 20 clusters. This study highlights the critical roles of FOX genes and fills a significant knowledge gap, providing a foundation for future functional and phylogenomic studies with implications for aquaculture and evolutionary biology.
Elemental and micromorphological analysis of ion releasing restoration/carious dentin interface
Abstract The objective of the current study was to evaluate elemental analysis and micromorphological patterns at the interfaces between tooth substrates either sound or demineralized dentin and ion-releasing restorations, including conventional glass ionomer and ion-releasing composite. The evaluation was conducted immediately (after 24 h) and after six months of artificial saliva storage. A total of 48 sound human premolars were used in this study. The premolars were randomly divided into two groups based on substrate type: sound and demineralized dentin (n = 24). Each substrate group was further subdivided into two subgroups according to restoration type: a conventional glass ionomer (Equia Forte HT) and an ion-releasing composite (ACTIVA-Bioactive Restorative) (n = 12). Each restoration subgroup was further divided according to aging condition: whether tested immediately after 24 h or after storage for six months in artificial saliva (n = 6). Specimens were sectioned buccolingually and longitudinally into two halves. For elemental analysis, one-half of each sectioned specimen was investigated using energy dispersive X-ray spectroscopy (EDX). Four elements (Ca, P, Al, and Si) were specifically examined at three different sites: dentin, hybrid layer, and restoration. For micromorphological patterns, two representative specimens from the second half of sectioned teeth were randomly selected to be observed under scanning electron microscope (SEM). EDX data were tabulated and subjected to statistical analysis, with a significance level set at p < 0.05. Kruskal-Wallis test revealed no statistically significant differences in distribution of the four elements across different tooth substrates or aging times. However, significant differences were observed based on the site factor. SEM micrographs revealed an ion-exchange zone in aged Equia Forte specimens and crystal-like structure deposition in artificially carious dentin bonded to ACTIVA. It has been concluded that both conventional glass ionomer restorative material and ion-releasing composite highlighted their bioactive behavior by demonstrating their potential ability to remineralize the demineralized dentin, reinforcing their promise as effective restorative options for dentin remineralization.
An improved YOLOv8s-based UAV target detection algorithm
At present, the low-altitude economy is booming, and the application of drones has shown explosive growth, injecting new vitality into economic development. UAVs will face complex environmental perception and security risks when operating in low airspace. Accurate target detection technology has become a key support to ensure the orderly operation of UAVs. This paper studies UAV target detection algorithm based on deep learning, in order to improve detection accuracy and speed, and meet the needs of UAV autonomous perception under the background of low altitude economy. This study focuses on the limitations of the YOLOv8s target detection algorithm, including its low efficiency in multi-scale feature processing and insufficient small target detection capability, which hinder its ability to perform rapid and accurate large-scale searches for drones. An improved target detection algorithm is proposed to address these issues. The algorithm introduces AKConv into the C2F module. AKConv allows for convolution kernels with arbitrary numbers and sampling shapes, enabling convolution operations to more precisely adapt to targets at different locations, thereby achieving more efficient feature extraction. To further enhance the model’s ability to extract critical features of small targets, the SPPF module incorporates the LSKA mechanism. This mechanism captures long-range dependencies and adaptivity more effectively while addressing computational complexity issues associated with large convolution kernels. Finally, the Bi-FPN feature pyramid network structure is introduced at the 18th layer of the model to accelerate and enrich feature fusion in the neck. Combined with the SCDown structure, a novel Bi-SCDown-FPN feature pyramid network structure is proposed, making it more suitable for detecting targets with insufficient feature capture in complex environments. Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. Moreover, the parameter count and weight file size are reduced by 13.41% and 13.33%, respectively. Compared to other mainstream target detection algorithms, the proposed method demonstrates certain advantages. In summary, the target detection algorithm proposed in this paper achieves a dual improvement in model lightweighting and detection accuracy.
Tropical response to ocean circulation slowdown raises future drought risk
Metagenomic analysis and genomic characterization of enterovirus A76 and Norovirus GI.6[P11] co-infection in a patient with acute gastroenteritis in Thailand
Burden of digestive system neoplasms in middle-aged and elderly adults: Temporal trends and geographic disparities (1990–2021)
Objective This study aims to comprehensively analyze the temporal trends and geographic disparities in the burden of seven digestive system neoplasms—esophageal cancer, stomach cancer, colon and rectum cancer, liver cancer, gallbladder and biliary tract cancer, pancreatic cancer, and benign and in situ intestinal neoplasms—among middle-aged and elderly populations from 1990 to 2021, and to project future trends through 2035. Methods This study utilized data from the Global Burden of Disease (GBD) 2021 database to analyze the burden of seven digestive system neoplasms among middle-aged and elderly adults across 204 countries and territories from 1990 to 2021. Estimates of incidence, mortality, and disability-adjusted life years (DALYs) were extracted and stratified by year, sex, age group, region, and Socio-Demographic Index (SDI). Trends were evaluated using estimated annual percentage change (EAPC), and risk factors were analyzed using the GBD comparative risk assessment framework. Results In 2021, colon and rectum cancer dominated the global burden of digestive system neoplasms. Trends from 1990 to 2021 revealed a clear split: the incidence of colon and rectum cancer (EAPC = 0.26, 95% CI 0.19 to 0.33), liver cancer (EAPC = 0.25, 95% CI 0.82 to 1.38), pancreatic cancer (EAPC = 0.55, 95% CI 0.53 to 0.57), and benign intestinal neoplasms (EAPC = 1.1, 95% CI 0.82 to 1.38) rose globally, while esophageal (EAPC = −0.92, 95% CI −1.06 to −0.79), stomach (EAPC = −1.63, 95% CI −1.71 to −1.55), and gallbladder/biliary tract cancers (EAPC = −0.28, 95% CI −0.31 to −0.25) declined. Regional hotspots for burden were East Asia and High-income Asia Pacific. Males generally faced higher burdens, except for gallbladder and biliary tract cancer. Key risk factors included smoking, alcohol consumption, dietary patterns, and metabolic factors such as high BMI and elevated fasting plasma glucose. Conclusions The burden of digestive system neoplasms remains substantial in middle-aged and elderly adults, with significant regional differences. With the growing aging population, efforts should prioritize high-risk populations and invest in healthcare infrastructure in middle- and low-SDI regions to control established cancers. Additionally, implementing robust global prevention policies, such as expanding disease screening, raising public health awareness, and managing metabolic disorders, is essential to mitigate the rising tide of digestive system neoplasms.
An improved elastic net clustering algorithm with dynamic parameter strategy
Global burden and trends of age-related and other hearing loss: A 32-year analysis and future projections based on the GBD 2021
Background To evaluate the global, regional, and national burdens of and trends in age-related and other hearing loss (ARoHL) from 1990–2021 based on the Global Burden of Disease 2021 database. Methods This study examined trends and disparities in the prevalence and years lived with disability (YLDs) of patients with ARoHL across age, sex, and the sociodemographic index (SDI). The estimated annual percentage change (EAPC) was calculated to assess temporal trends. Decomposition analysis, cross-country inequality analysis, and frontier analysis were employed to reveal additional facets of the ARoHL burden, whereas Bayesian Age-Period-Cohort (BAPC) modeling projected future trends to 2040. Results ARoHL remains a critical public health challenge. The global age-standardized prevalence rate (ASPR) increased significantly from 1.71 (95% UI: 1.63–1.80) ×10⁴ to 1.81 (95% UI: 1.73–1.89) ×10⁴ per 100,000 (EAPC = 0.163; 95% CI: 0.154–0.172), whereas the age-standardized YLD rate (ASYR) increased from 499.37 to 525.87 per 100,000 (EAPC = 0.171; 95% CI: 0.161–0.180). Decomposition analysis revealed that epidemiological changes contributed 37.28% to the increase in YLDs. Globally and across all five SDI regions without age distinction, the male ASPR and ASYR were consistently greater than the female ASPR and ASYR at all time points. The relationship between the SDI and ARoHL burden is complex. BAPC projections indicate stable ASPRs and ASYRs through 2040 despite increasing cases and YLDs. Conclusions The global ASPR of ARoHL increased by 5.63% and that of ASYR increased by 5.31% from 1990–2021, with the number of cases and YLDs doubling. Targeted interventions and policies must address this growing public health challenge.
The optimization of low Earth orbit satellite constellation visibility with genetic algorithm for improved navigation potential
Invasive candidiasis following lung transplant: An Assessment of impact utilizing a national insurance claims cohort
Introduction Lung transplant recipients (LTRs) are susceptible to invasive candidiasis (IC). This study aimed to assess the incidence, risk factors, and impact of IC on mortality in LTRs using a national insurance claims cohort. Methods We conducted a retrospective cohort study using administrative claims data from the OptumLabs® Data Warehouse. We identified LTRs from January 1, 2005, to December 31, 2023, using procedural codes. Exclusion criteria included re-transplantations and pre-transplant IC. We employed multivariable logistic regression to identify risk factors for IC and Cox Proportional Hazard models to assess the impact of IC on mortality. Results Among 1279 LTRs, 131 (10.2%) developed IC, primarily during the initial hospitalization for lung transplantation (index hospitalization). The median time to IC diagnosis was 32.0 days following transplant. Post-transplant extra-corporeal membrane oxygenation (ECMO) for more than 8 days was associated with IC (OR: 2.34; 95% CI 1.03 to 5.34). Mortality was higher in LTRs with IC (HR: 2.31; 95% CI: 1.45 to 3.67; p < 0.001). LTRs with IC also had longer hospital stays (median 26.0 days vs. 20.0 days; p < 0.001) and more re-operations (36.7% vs. 27.3%; p = 0.003) compared to those without IC. Conclusion Invasive candidiasis affects approximately 10% of lung transplant recipients, most often during the initial hospitalization. It is associated with increased mortality, prolonged hospital stays, and a greater need for surgical re-intervention. These findings highlight the importance of early identification and targeted preventive strategies to improve outcomes in this high-risk population.
ORCID launched more than a decade ago, but has yet to fulfil its potential
Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
Epidemiology of liver cancer in Kazakhstan: data from the Unified National Electronic Health System, 2014–2023
Background Liver cancer is the third-deadliest cancer globally, significantly affecting the Asian population, which accounts for 70% of cases. Despite these numbers, the burden of liver cancer in Central Asia, including Kazakhstan, remains underexplored. This paper reviews the epidemiology of liver cancer in Kazakhstan by analyzing large-scale administrative data from the national registry. Methods The study cohort included 10,455 patients diagnosed with liver neoplasms (C22.0-C22.9, D13.4) from 2014 to 2023, using the International Classification of Diseases, 10th Revision (ICD-10). Age-standardized incidence, mortality, and prevalence rates per 100,000 population were calculated. Cox regression analysis estimated hazard ratios across social-demographic and medical predictors. Results Over the decade, age-standardized incidence rates decreased from 5.55 to 5.40 per 100,000, while mortality rates rose from 3.75 to 4.75 per 100,000. A downward trend appeared between 2017–2019 but gradually reversed post-2020. Prevalence rates steadily increased from 1.30 to 7.32 per 100,000. The cohort’s mean age was 63.2 ± 0.1 years, with most cases diagnosed as stage III hepatocellular carcinoma. West Kazakhstan, East Kazakhstan, and Pavlodar were the most burdened regions. Liver cirrhosis history predicted worse survival (Hazard Ratio, HR = 1.14; 95% CI 1.06–1.22), while primary hypertension showed a protective effect (HR = 0.82; 95% CI 0.78–0.86). Conclusions Liver cancer morbidity and mortality rates have risen in Kazakhstan over the past decade, with a slight decrease during the early COVID-19 pandemic. Patients are mostly diagnosed with stage III hepatocellular carcinoma. West Kazakhstan, East Kazakhstan, and Pavlodar are the most affected regions, requiring better management of high-risk groups. Poor survival was linked to liver cirrhosis. Further research on the role of rising metabolic liver diseases and treatment options in Kazakhstan is needed to better predict liver cancer prognosis.
Chemical profile of essential oil from Pimenta racemosa leaves, antioxidant potential, and its enzyme inhibitory properties
Abstract Pimenta racemosa var. recemosa (Mill.) J. W. Moore is an aromatic plant belonging to the family Myrtaceae, native to Venezuela, Puerto Rico, and Jamaica and well-known for its traditional and medicinal uses. Our study was designated to explore the chemical composition of essential oil isolated from P. racemosa leaves growing in Egypt via Gas Chromatography/Mass Spectrometry (GC-MS) analysis, alongside investigation of its antioxidant properties and enzyme inhibitory activities against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), tyrosinase, α-glucosidase, and α-amylase. The GC-MS analysis of the leaf oil revealed the presence of fourteen compounds (99.76%), predominated by eugenol (70.87%), β-myrcene (12.88%) and D-limonene (8.35%). The oil demonstrated the highest antioxidant capability in ferric ion-reducing antioxidant power (FRAP; 1506.62 mg TE/g), followed by 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS; 1346.85 mg TE/g), 1,1-diphenyl-2-picrylhydrazyl (DPPH; 1032.83 mg TE/g) and cupric-reducing antioxidant capacity (CUPRAC; 1001.03 mg TE/g). Further, it showed a metal chelating ability (MCA) of 25.63 mg EDTAE/, and phosphomolybdenum (PBD) activity of 209.59 mmol TE/g. The oil displayed significant AChE and BChE inhibitory activities, with values of 1.96 mg GALAE/g and 1.42 mg GALAE/g, respectively. Additionally, it exhibited a moderate level of tyrosinase inhibitory activity (38.83 mg KAE/g) and a significantly higher α-glucosidase inhibitory activity (2.38 mmol ACAE/g) than α-amylase (0.08 mmol ACAE/g). Consequently, the leaf oil of Pimenta racemosa could be used as adjuvant therapy for management of oxidative stress-related conditions and chronic diseases such as Alzheimer’s, diabetes mellitus, and skin pigmentation. However, further toxicological, in vivo and clinical studies are recommended.
A cross-sectional study of COVID-19 testing and disclosure hesitancy: The role of responsibility attribution in South Korea
Efficient and effective public health surveillance during epidemics relies heavily on active and voluntary public participation, including timely COVID-19 testing and disclosure of results to contacts. This study aimed to investigate predictors of COVID-19 testing and disclosure hesitancy, with a focus on the role of responsibility attribution during the early stages of the Omicron epidemic in South Korea. A cross-sectional survey was conducted with 1,000 participants between February 25 and March 2, 2022. Chi-square tests and multivariable logistic regression models were used for analysis. Findings showed that 41.5% of participants expressed hesitancy toward COVID-19 testing, and 59.4% expressed hesitancy toward disclosing test results to contacts. Greater attribution of responsibility to individuals was significantly associated with increased hesitancy toward testing (OR = 0.75, 95% CI = 0.63–0.90, p < 0.001) and disclosure (OR = 0.67, 95% CI = 0.56–0.80, p < 0.001). Conversely, testing acceptance was positively associated with trust in the government (OR = 1.29, 95% CI = 1.07–1.55, p = 0.01), social support (OR = 2.18, 95% CI = 1.73–2.73, p < 0.001), and full vaccination status (OR = 1.98, 95% CI = 1.11–3.50, p = 0.02). Disclosure acceptance was similarly associated with trust in the government (OR = 1.26, 95% CI = 1.05–1.51, p = 0.01) and social support (OR = 2.23, 95% CI = 1.77–2.81, p < 0.001). These results underscore the central role of responsibility attribution in shaping public participation in surveillance efforts. Mitigating excessive individual attribution and fostering a balanced perspective that integrates both personal and contextual factors may enhance public engagement. While attribution remains a key psychological predictor, building trust in government and strengthening social support systems also emerge as important strategies for promoting active and voluntary participation in public health surveillance.