Browse Articles

Similarity based city data transfer framework in urban digitization

Overweight and obesity trends and association with household wealth index among children aged 5 to 19 years in Ethiopia a multilevel analysis of 2016 EDHS data

Therapeutic treatment of hepatitis E virus infection in pigs with a neutralizing monoclonal antibody

Abstract Hepatitis E virus (HEV) poses a significant risk to human health. In Europe, the majority of HEV infection are caused by the zoonotic genotype 3 (HEV-3), which can cause chronic hepatitis E in immunocompromised patients and those with pre-existing liver disease, and may eventually develop into fatal liver cirrhosis. In this study, we examined the effectiveness of a monoclonal antibody (MAb) treatment strategy using a well established HEV-3 pig model with intravenous infection. For this purpose, nine MAbs raised against the viral capsid protein were generated and the neutralizing activities were compared using in vitro assays. The antibody with the highest neutralizing activity, MAb 5F6A1, was selected for an in vivo study in pigs infected with HEV-3. Following the initial infection of pigs with HEV-3, MAb 5F6A1 was administered intravenously one and seven days post-infection. The results suggest MAb 5F6A1 significantly reduced viremia and virus shedding in pigs infected with HEV-3. This study provides significant insight into the dynamics of HEV infection in pigs and highlights the efficacy of MAb based therapy as an option for treating HEV in porcine hosts and, potentially, humans.

Development of co-doped ZnS-CdS quantum dots based composite sensor for the detection of cefixime (CXM) and tetracycline (TET), and application in real samples from local dairies

The coupling relationship and driving mechanism between ecological environment and high-quality economic development in the Middle Yellow River Basin

Abstract Promoting the dynamic balance between economic development and ecological environment is key to achieving the “dual carbon” goals and sustainable development. The Middle Yellow River Basin, characterized by severe soil erosion and intensive resource utilization, serve as a critical area for advancing ecological protection and high-quality development in the Yellow River Basin. This study examines the spatial–temporal differentiation and coupling coordination characteristics of the ecological environment (EE) and high-quality economic development (HQED) across 226 counties in the Middle Yellow River Basin from 2010 to 2020. Utilizing the Random Forest model and the Geographical and Temporal Weighted Regression model, the study investigates the driving mechanism of high-quality economic development on ecological environment. The zoning management strategy is proposed based on the types of coupling coordination and the dominant driving factors, with the aim of providing theoretical support for sustainable development in the river basins. The results show that: (1) During the study period, the level of ecological environment initially declined and then improved, while high-quality economic development consistently increased. The EE exhibited a spatial pattern of "southeast low, northwest high," while the distribution pattern of HQED was the reverse. (2) The coupling coordination degree considerably increased after 2015, displaying the spatial pattern characterized by higher levels in the southeast and northwest and lower levels in the central region, with the strong spatial positive correlation. (3) Forest cover rate, PM2.5 concentration, agricultural fertilizer application intensity, and market activity make high contributions to the ecological environment, making them key drivers. Forest cover rate is the strongest positive driver, while PM2.5 concentration is the strongest negative driver. There are evident spatial distribution differences among the various driving factors. Ultimately, the study area is divided into six types of zones, and corresponding development strategies is proposed.

Comparative analysis of dehazing algorithms on real-world hazy images

Abstract Images captured in adverse weather conditions (haze, fog, smog, mist, etc.) often suffer significant degradation. Due to the scattering and absorption of these particles, various negative effects, such as reduced visibility, low contrast, and colour distortion are introduced into the image. These degraded images are unsuitable for many computer vision applications, including smart transportation, video surveillance, weather forecasting, and remote sensing. To ensure the reliable operation of such applications, a high-quality haze-free input image is essential, which is supplied by image dehazing techniques. This review categorises recent dehazing methods, highlighting popular approaches within each group. In recent years, deep learning methods and restoration-based techniques using priors have garnered attention, particularly for addressing challenges such as dense and non-homogeneous haze. In this paper, their typical candidates are compared by using real-world hazy images because most data-driven and neural augmentation methods are trained by using synthetic hazy images. Experimental results conducted on real-world hazy images reveal that physics-driven single-image dehazing algorithms exhibit a lack of robustness, while data-driven approaches perform well on thin hazy images but struggle in dense haze conditions. Neural augmentation algorithms, however, effectively combine the strengths of both approaches, offering a better overall solution. By identifying existing gaps in recent methods, this paper provides a valuable resource for both novice and experienced researchers, while pointing towards future directions in this rapidly advancing field.

S-scheme heterojunction of MoO3 nanobelts and MoS2 nanoflowers for photocatalytic degradation

Bioinformatic analysis of glycolysis and lactate metabolism genes in head and neck squamous cell carcinoma

Influence of bearing platform size on bearing capacity of NT-CEP pile group foundation under compound force

Pertinence of contact duration as edge feature for epidemic spread analysis

Abstract Identifying superspreading nodes has attracted greater attention because of its wide practical significance in various applications. Existing studies consider the edges mostly equally while designing the algorithms for the unweighted contact networks, where each connection explicitly shows whether the individuals are in contact or not. It will not consider other relevant information in the context of epidemiology study or infectious disease spread, such as proximity or total time spent between the contact nodes. The recent studies focused on the weighted network, where most of the methods have computed the edge weights by utilizing degree and k-shell measure, which captures the topological structure of the network but not the interaction duration between pair of contacts. In this study, we mainly aim to generate weighted networks to model the pathogen spread by optimal calculation of the edge weight in terms of contact duration (time spent) between individual contacts. Leveraging this interaction duration as the edge weight, we further design a novel technique, namely Real Weighted Influence (RWInf), for identifying the superspreading nodes during an epidemic outbreak. The empirical study revealed that the proposed approach outperforms with an improvement of 0.146–0.473 kendall’s score in comparison with baseline approaches.

Prediction of ultimate strength and strain in FRP wrapped oval shaped concrete columns using machine learning

A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning

Associations of Naples prognostic score with stroke in adults and all cause mortality among stroke patients

Prevalence of congenital malaria in an urban and a semirural area in Lagos: a two-centre cross-sectional study

‘Candidatus liberibacter solanacearum’ protein CKC_05770 interacts in vivo with tomato APX6 and APX7

Decrease in atmospheric pressure could increase endolymphatic space volume in Meniere’s disease

Sensitive and modular amplicon sequencing of Plasmodium falciparum diversity and resistance for research and public health

Abstract Targeted amplicon sequencing is a powerful and efficient tool for interrogating the Plasmodium falciparum genome, generating actionable data from infections to complement traditional malaria epidemiology. For maximum impact, genomic tools should be multi-purpose, robust, sensitive, and reproducible. We developed, characterized, and implemented MAD 4 HatTeR, an amplicon sequencing panel based on Multiplex Amplicons for Drug, Diagnostic, Diversity, and Differentiation Haplotypes using Targeted Resequencing, along with a bioinformatic pipeline for data analysis. Additionally, we introduce an analytical approach to detect gene duplications and deletions from amplicon sequencing data. Laboratory control and field samples were used to demonstrate the panel’s high sensitivity and robustness. MAD 4 HatTeR targets 165 highly diverse loci, focusing on multiallelic microhaplotypes, key markers for drug and diagnostic resistance (including duplications and deletions), and CSP and potential vaccine targets. The panel can also detect non- falciparum Plasmodium species. MAD 4 HatTeR successfully generated data from low-parasite-density dried blood spot and mosquito midgut samples and detected minor alleles at within-sample allele frequencies as low as 1% with high specificity in high-parasite-density dried blood spot samples. Gene deletions and duplications were reliably detected in mono- and polyclonal controls. Data generated by MAD 4 HatTeR were highly reproducible across multiple laboratories. The successful implementation of MAD 4 HatTeR in five laboratories, including three in malaria-endemic African countries, showcases its feasibility and reproducibility in diverse settings. MAD 4 HatTeR is thus a powerful tool for research and a robust resource for malaria public health surveillance and control.

Empowering agricultural ecological quality development through the digital economy with evidence from net carbon efficiency

Authenticable quantum secret sharing based on special entangled state

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging

Abstract The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images of cysts, normal states, stones, and tumors using a hyperparameter fine-tuned approach with convolutional neural networks (CNNs), VGG16, ResNet50, CNNAlexnet, and InceptionV3 transfer learning models. It introduces an innovative methodology that integrates finely tuned transfer learning, advanced image processing, and hyperparameter optimization to enhance the accuracy of kidney tumor classification. By applying these sophisticated techniques, the study aims to significantly improve diagnostic precision and reliability in identifying various kidney conditions, ultimately contributing to better patient outcomes in medical imaging. The methodology implements image-processing techniques to enhance classification accuracy. Feature maps are derived through data normalization and augmentation (zoom, rotation, shear, brightness adjustment, horizontal/vertical flip). Watershed segmentation and Otsu’s binarization thresholding further refine the feature maps, which are optimized and combined using the relief method. Wide neural network classifiers are employed, achieving the highest accuracy of 99.96% across models. This performance positions the proposed approach as a high-performance solution for automatic and accurate kidney CT image classification, significantly advancing medical imaging and diagnostics. The research addresses the pressing need for early kidney disease detection using an innovative methodology, highlighting the proposed approach’s capability to enhance medical imaging and diagnostic capabilities.