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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people
Increased risk of lung cancer in individuals with preserved ratio impaired spirometry: a nationwide cohort study
Systemic inflammation partially mediates the association between lipid accumulation and osteoarthritis in normal BMI adults
A versatile fluorescence polarization-based deubiquitination assay using an isopeptide bond substrate mimetic (IsoMim)
Mixed-methods study to assess delay among patients with tuberculosis in an urban setting of Bangladesh
Background Tuberculosis (TB) regained its position as the leading cause of death globally from a single infectious disease agent in 2024. Delayed diagnosis and treatment hamper effective TB control. We investigated the duration of diagnostic and treatment delay along with the associated factors among people with pulmonary TB in Bangladesh. Methods A mixed-method study was conducted between December’19 and March’21, at icddr,b TB Screening and Treatment Centres (TBSTCs), Dhaka. We interviewed people with TB (PWTB) seeking care at these TBSTCs using a structured questionnaire to collect data on socio-demographic, clinical and healthcare seeking behaviors. We used established frameworks to define stages of delay and associated factors. Qualitative interviews were conducted among a subset of participants to gain further insight into the factors associated with delay. Results We enrolled 895 PWTB with mean (±SD) age 36.6 (±16.1) years; majority of participants were males (69.9%) and living in urban areas (82.3%). The median (IQR) patient delay estimated was 47 (29–72) days, with diagnostic delay 45 (30–70) days and treatment delay 2 (2–4) days. The predictors of delay were those with diabetes (OR 2.0, 95% CI – 1.11, 3.42), who initially self-treated (OR 2.1, 95% CI – 1.09, 3.88), and were bacteriologically diagnosed (OR 3.7, 95% CI – 1.31, 10.46). Qualitative approach supported the quantitative findings and revealed the practice of visiting formal physicians during worsening illness, neglecting to acknowledge signs or symptoms consistent with TB, lack of TB related knowledge, and financial insolvency as major reasons for delay. Conclusion Our findings showed that improper health-seeking behavior is one of the major drivers of patient delay. Thus, targeted programmatic intervention to raise community awareness on TB and its care services with a special focus on informal providers can help reduce this delay.
Sexually dimorphic impact of prenatal hyperandrogenism on offspring growth trajectory in sheep
The antimicrobial efficacy of rutin encapsulated chitosan versus multidrug-resistant Pseudomonas aeruginosa
Secure data transmission through fractal-based cryptosystem: a Noor iteration approach
Two-dimensional β-phase copper iodide: a promising candidate for low-temperature thermoelectric applications
Sound quality control method of 110 kV transformer based on distributed variable stiffness dynamic vibration absorber
Finite element analysis of the modified intramedullary nail-II for managing reverse obliquity trochanteric fractures
Genotyping-by-sequencing of Illicium difengpi highlights its potential genetic diversity and conservation status
Predicting compressive and splitting tensile strength of high volume fly ash roller compacted concrete using ANN and ANN-biogeography based optimization models
Abstract Roller compacted concrete (RCC) has gained prominence in the construction industry due to its durability, cost-effectiveness, and environmental benefits, particularly with the incorporation of high-volume fly ash (HVFA). However, traditional experimental approaches to evaluating RCC’s mechanical properties, such as compressive strength (CS) and splitting tensile strength (STS), are resource-intensive and time-consuming. To address these challenges, this study explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN) and a hybrid ANN-Biogeography-Based Optimization (ANN-BBO) model, to predict the CS and STS of RCC. A dataset comprising 168 RCC mixtures, incorporating various material and process parameters, was analyzed. The ANN-BBO model demonstrated superior predictive accuracy compared to a standalone ANN, with R2 values exceeding 0.98 for both CS and STS, significantly reducing error margins. The findings highlight the effectiveness of AI-driven modeling in optimizing RCC mix designs, minimizing experimental costs, and enhancing the sustainability of concrete production. This research underscores the potential of integrating AI with optimization techniques to refine RCC performance assessment, which enables and facilitates more efficient and sustainable infrastructure development.
Select early growth response (Egr) isoforms augment hypoxia inducible factor 2 (HIF-2) regulation of erythropoietin (Epo) gene expression in mammals
Imidacloprid decreases energy production in the hemolymph and fat body of western honeybees even though, in sublethal doses, it increased the values of six of the nine compounds in the respiratory and citric cycle
Background Neonicotinoids, including imidacloprid (IM), cause harm to Apis mellifera in a number of ways, among others by impairing body maintenance, resistance and immunity. Energy resources are important to preventing this, as we hypothesized, not only in the hemolymph but particularly in the fat body, the insufficiently investigated, as yet, equivalent of the mammalian liver and pancreas. Both suppression and hormesis (diaphasic stressor response) of energy supply was reported in the energy-dependent traits of bees exposed to sublethal doses of imidacloprid. Therefore, our goal was to answer which of these two phenomena occurs in the hemolymph/fat body and at what doses of imidacloprid. Methods concentrations/activities of respiratory/citric cycle compounds (acetyl-CoA, IDH-2, AKG, succinate, fumarate, NADH2, COX, UQRC, and ATP) were compared in the hemolymph and fat bodies of nurse workerbees sampled from honeybee colonies fed with diets containing 200 ppb (IM-200), 5 ppb (IM-5; sublethal), and 0 ppb of IM in a field experiment. Results the assayed compounds had higher values in the fat body than in the hemolymph, whereas their variability was higher in the hemolymph. The pattern of response to IM was the same in both tissues, but markedly differed between IM-200 and IM-5. The concentrations of the strongly correlated NADH2, ATP and acetyl-CoA decreased both in IM-200 and IM-5, whereas the levels of the other compounds decreased in IM-200 but increased in IM-5. Conclusions and significance decreased ATP and acetyl-CoA levels both in IM-5 and IM-200 show that the pesticide impairs the hemolymph and fat-body energy metabolism in spite of hormesis in six of the nine respiratory and citric cycle compounds even in low, residual doses. This finding better explains how residual doses of neonicotinoids may disturb the fat body functions, and therefore suppress the apian resistance, which expands our knowledge about honeybee colony losses.
Particulate matter exposure from different heating stoves and fuels in UK homes
Abstract Traditional wood stoves emit high levels of particulate matter ≤ 2.5 μm (PM2.5), prompting the development of improved models to reduce emissions. However, these stoves may unintentionally increase ultrafine particle ≤ 100 nm (UFP) emissions, which can penetrate biological barriers and pose health risks. This study evaluates the impacts of four solid fuel types on indoor air quality (IAQ) in five non-smoking households in Guildford, United Kingdom, using different wood stoves (eco-design, multifuel eco-design, clear skies stage (v), and open fireplace) during winter. Indoor UFP, PM10, PM2.5, black carbon (BC), and carbon monoxide (CO) levels were measured using handheld monitors in living areas. Fuel type, room volume, stove type, and burning duration significantly influenced IAQ, exacerbated by inadequate ventilation. Open fireplaces had the highest exposure levels, followed by multifuel eco-design, eco-design, and clear skies stage (v) stoves. During burning periods, median (interquartile range) indoor pollutant concentrations were UFP: 3.6 (5.8) ×10⁴ # cm⁻³, PM2.5: 38.4 (65.5) µg m⁻³, PM10: 89.6 (89.0) µg m⁻³, and BC: 1.7 (3.6) µg m⁻³ for open fireplaces. Among improved stoves, multifuel eco-design had the highest exposure; UFP: 2.2 (4.9) ×10⁴ # cm⁻³, PM2.5: 14.2 (16.9) µg m⁻³, PM10: 37.9 (45.9) µg m⁻³, and BC: 1.5 (2.3) µg m⁻³ followed by eco-design, and clear skies stage (v) stoves. Wood briquettes produced the highest pollutant levels, followed by smokeless coal, kiln-dried wood, and seasoned wood. Burning manufactured fuels (wood briquettes) increased PM2.5 and UFP by 4- and 1.5-times, respectively, compared to seasoned wood. The mean CO concentration for open fireplaces was 3.1 ppm, below the World Health Organisation’s (WHO) 24-hour exposure limit guideline (3.49 ppm). Smaller rooms (< 40 m³) with longer burning durations increased exposure by 2- and 3-times compared to larger rooms (> 50 m³). Low air changes per hour (ACH) (< 1.2 h⁻¹) contributed to pollutant accumulation. Our findings indicate that residential wood burning significantly increases short-term exposure to UFPs, PM2.5, BC, and CO, posing potential health risks. These results underscore the need for health-focused strategies when considering wood burning for domestic heating.
The impact of digital education on household allocation of risky financial assets in China
Trajectory analysis of Mythimna Loreyi migration into Korea using the HYSPLIT model
Abstract Migratory moth species have a negative impact on agriculture in Korea, causing economic crop losses for farming households. The moths fly a considerable distance from their origins and settle in Korea, where they live for several generational cycles, feeding and laying eggs until the arrival of cold winter hinders their overwintering. Therefore, it is important to determine the timing and location of the moths’ departure and arrival to prevent potential harm from invasive moths. The goal of this study is to select the most likely origin and destination of the migratory moths by using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model when there is limited moth occurrence data at the potential origin and destination. The HYSPLIT model was adopted and modified to investigate the potential trajectory paths of the loreyi leafworm (Mythimna loreyi), using atmospheric data. Occurrence data of M. loreyi was collected from 18 trap sites in Korea and five in China, and fed into the HYSPLIT model to conduct backward and forward trajectory analyses. As a result, we successfully prioritized the most likely origins and destinations of M. loreyi in China and Korea, respectively, which has potential implications for subsequent validation of moth migration, using genotypic and phenotypic characterizations. In conclusion, our results show that it is feasible to use the HYSPLIT model simulation for the initial screening of the most likely origins of migratory moths, leading to more efficient and effective validation study of insect migration between countries.
Design of a deep fusion model for early Parkinson’s disease prediction using handwritten image analysis
Abstract Parkinson’s Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very challenging. However, using patterns like tremors for early diagnosis, handwriting analysis has become a useful diagnostic technique. This work aims to improve early PD diagnosis by proposing a hybrid deep fusion model that blends ResNet-50 and GoogLeNet (RGG-Net). We demonstrated the RGG-Net model in a series of steps such as preprocessing images, ResNet-50 and GoogLeNet models for feature extraction, combining the features using the Adaptive Feature Fusion technique and selecting the relevant features using the attention process, making the models stronger through Hierarchical Ensemble Learning. The grad-CAM technique is used for decision-making in PD prediction. The proposed model is a reliable way to analyze handwritten images using advanced techniques like adaptive feature fusion, hierarchical ensemble learning, and eXplainable Artificial Intelligence. Here, we analyzed ten pre-trained models to determine which model best captures the relevant features for PD classification using handwritten images. The models included are AlexNet, DenseNet-201, SqueezeNet1.1, VGG-16, VGG-19, ResNet-50, ResNet-101, GoogleNet, MobileNetV1, and MobileNetV2. The proposed deep transfer learning model showed an accuracy of 99.12%, outperforming the other state-of-the-art methods, indicating the model’s excellence and vigor. The proposed model performs better than all pre-trained models with and without freezing convolutional layers. These results underscore the efficacy of the proposed approach in enhancing accuracy and transparency in Parkinson’s disease prediction and the potential of deep learning in promoting early diagnosis.