Search Results (32,411)

Transient gene expression (TGE) is commonly employed for protein production, but its reliance on plasmid transfection makes it challenging to scale up. In this paper, an alternative TGE method is presented, utilizing pseudoinfectious alphavirus as an expression vector. Pseudoinfectious viruses (PIV) and a replicable helper construct were derived from the genome of the Venezuelan equine encephalitis virus. The PIV carries a mutant capsid protein that prevents packaging into infectious particles, while the replicable helper encodes a wild-type capsid protein but lacks other viral structural proteins. Although PIV and the helper cannot independently spread infection, their combination results in increased titers in cell cultures, enabling easier scale-up of producing cultures. The PIV-driven production of a model protein outperforms that of alphavirus replicon vectors or simple plasmid vectors. Another described feature of the expression system is the modification to immobilized metal affinity chromatography (IMAC), allowing purification of His-tagged recombinant proteins from a conditioned medium in the presence of substances that can strip metal from the IMAC columns. The PIV-based expression system allows for the production of milligram quantities of recombinant proteins in static cultures, without the need for complex equipment such as bioreactors, and complies with regulatory requirements due to its distinction from common recombinant viruses. Full article

Background/Objectives: The methacholine bronchial provocation test (MBPT) is a diagnostic test frequently used to evaluate airway hyper-reactivity. MBPT is essential for diagnosing asthma; however, it can be time-consuming and resource-intensive. This study aimed to develop an artificial intelligence (AI) model to predict the MBPT results using forced expiratory volume in one second (FEV₁) and bronchodilator test measurements from spirometry. Methods: a dataset of spirometry measurements, including Pre- and Post-bronchodilator FEV₁, was used to train and validate the model. Results: Among the evaluated models, the multilayer perceptron (MLP) achieved the highest area under the curve (AUC) of 0.701 (95% CI: 0.676–0.725), accuracy of 0.758, and an F1-score of 0.853. Logistic regression (LR) and a support vector machine (SVM) demonstrated comparable performance with AUC values of 0.688, while random forest (RF) and extreme gradient boost (XGBoost) achieved slightly lower AUC values of 0.669 and 0.672, respectively. Feature importance analysis of the MLP model identified key contributing features, including Pre-FEF_25–75 (%), Pre-FVC (L), Post FEV₁/FVC, Change-FEV₁ (L), and Change-FEF_25–75 (%), providing insight into the interpretability and clinical applicability of the model. Conclusions: These results highlight the potential of the model to utilize readily available spirometry data, particularly FEV₁ and bronchodilator responses, to accurately predict MBPT results. Our findings suggest that AI-based prediction can improve asthma diagnostic workflows by minimizing the reliance on MBPT and enabling faster and more accessible assessments. Full article

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R² of 0.69 during the flowering and milk stages, and an R² of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R² of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning. Full article

Orthoflavivirus, a genus encompassing arthropod-borne, positive-sense, single-stranded RNA viruses in the Flaviviridae family, represents clinically relevant viruses that pose significant threats to human and animal health worldwide. With warming climates and persistent urbanization, arthropod vectors and the viruses they transmit continue to widen their geographic distribution, expanding endemic zones. Flaviviruses such as dengue virus, Zika virus, West Nile virus, and tick-borne encephalitis virus cause debilitating and fatal infections globally. In 2024, the World Health Organization and the Pan American Health Organization declared the current dengue situation a Multi-Country Grade 3 Outbreak, the highest level. FDA-approved treatment options for diseases caused by flaviviruses are limited or non-existent, and vaccines are suboptimal for many flaviviruses. Understanding the molecular characteristics of the flavivirus life cycle, virus-host interactions, and resulting pathogenesis in various cells and model systems is critical for developing effective therapeutic intervention strategies. This review will focus on the virus-host interactions of mosquito- and tick-borne flaviviruses from the virus replication and assembly perspective, emphasizing the interplay between viral non-structural proteins and host pathways that are hijacked for their advantage. Highlighting interaction pathways, including innate immunity, intracellular movement, and membrane modification, emphasizes the need for rigorous and targeted antiviral research and development against these re-emerging viruses. Full article

Pollen forecasting models are helpful tools to predict environmental processes and allergenic risk events. Parietaria belongs to the Urticaceae family, and due to its high-level pollen production, is responsible for many cases of severe pollinosis reactions. This research aims to develop different machine learning models such as the random forest—RF, support vector machine—SVM, and artificial neural network—ANN models, to predict Parietaria pollen concentrations in the atmosphere of northwest Spain using 24 years of data from 1999 to 2022. The results obtained show an increase in the duration and intensity of the Parietaria main pollen season in the Mediterranean region (Ourense). Machine learning models exhibited their capacity to forecast Parietaria pollen concentrations at one, two, and three days ahead. The best selected models presented high correlation coefficients between 0.713 and 0.859, with root mean squared errors between 5.55 and 7.66 pollen grains·m⁻³ for the testing phase. The models developed could be improved by increasing the number of years, studying other hyperparameter ranges, or analyzing different data distributions. Full article

Chagas disease, caused by Trypanosoma cruzi, remains a significant public health challenge, particularly in Latin America, where it is one of the most neglected diseases and is primarily transmitted by triatomine insects. The disease exhibits complexity due to its diverse transmission routes, including vectorial and non-vectorial mechanisms such as blood transfusions and congenital transmission. Effective monitoring and control strategies are critical to mitigating its impact. This review focuses on current monitoring and control efforts, emphasizing the importance of enhanced surveillance systems, improved risk assessments, and integrated vector control programs. Surveillance plays a pivotal role in early detection and timely intervention, particularly in endemic regions, while vector control remains central to reducing transmission. Moreover, the development of novel diagnostic tools, treatments, and vaccines is a crucial step in advancing control efforts. This review also highlights the involvement of local governments, international organizations, and civil society in executing these strategies, stressing the need for sustained political commitment to ensure the success of public health programs. By addressing key challenges in monitoring, control, and prevention, this review aims to provide insights and recommendations to further global efforts in reducing the burden of Chagas disease. Full article

In this paper, the fault type and location of high-impedance short-circuit faults, which are difficult to detect in distribution networks, are determined in real time using the Real-Time Digital Simulator (RTDS). In this study, an IEEE 39-bar system model is created using the Real-Time Simulation Software Package (RSCAD). In this model, a short-circuit fault is generated at different fault impedance values. For high-impedance short-circuit fault detection, 14 feature vectors are created. Six of these feature vectors are newly developed, and it is found that these six new feature vectors contribute 10% to the detection of hard-to-detect high-impedance short-circuit faults. We propose a data-driven online algorithm for fault type and location detection based on robust regularized random vector function networks (ORR-RVFLNs). Moreover, the robustness of the model is improved by adding a certain amount of noise to the detected short-circuit fault data. In this study, the method ORR-RVFLN for the 39-bus system IEEE detects the average error type for all error impedances, with 92.2% success for the data with noise added. In this study, the fault location is shown to be more than 90% accurate for distances greater than 400 m. Full article

Ticks pose significant challenges to public and veterinary health, acting as vectors of several diseases that affect animals and humans. Traditional chemical control methods, such as pyrethroids and organophosphates, have led to increasing resistance and environmental contamination, highlighting the need and urgency for alternative strategies. This review explores contemporary approaches to tick control, emphasizing plant-derived acaricides and their integration with nanotechnology. Plant extracts, known for their acaricidal properties, disrupt several biological processes in ticks, reducing reproduction and survival rates. The advent of nanotechnology offers promising advances in increasing the efficacy of these natural extracts. Nanoparticles add properties to the systems where they act by improving the stability, bioavailability, and targeted delivery of plant-derived compounds, potentially overcoming the limitations of traditional acaricides. This synthesis of current knowledge highlights the potential of combining plant extracts with nanotechnology to develop sustainable and effective tick control solutions, addressing issues of acaricide resistance as well as environmental concerns. The review also identifies research gaps and suggests directions for future studies to optimize the application of nanotechnology in tick management. Full article

In the rapidly evolving landscape of telecommunications, the integration of commercial 5G solutions and the rise of edge computing have reshaped service delivery, emphasizing the customization of requirements through network slices. However, the heterogeneity of devices and technologies in 5G and beyond networks poses significant challenges, particularly in terms of security management. Addressing this complexity, our work adopts the Zero-touch network and Service Management (ZSM) reference architecture to enable end-to-end automation of security and service management in Beyond 5G networks. This paper introduces the ZSM-based framework, which harnesses software-defined networking, network function virtualization, end-to-end slicing, and orchestration paradigms to autonomously enforce and preserve security service level agreements (SSLAs) across multiple domains that make up a 5G network. The framework autonomously manages end-to-end security slices through intent-driven closed loops at various logical levels, ensuring compliance with ETSI end-to-end network slice management standards for 5G communication services. The paper elaborates with an SSLA-triggered use case comprising two phases: proactive, wherein the framework deploys and configures an end-to-end security slice tailored to the security service level agreement specifications, and reactive, where machine learning-trained security mechanisms autonomously detect and mitigate novel beyond 5G attacks exploiting open-sourced 5G core threat vectors. Finally, the results of the implementation and validation are presented, demonstrating the practical application of this research. Interestingly, these research results have been integrated into the ETSI ZSM Proof of Concept #6: ’Security SLA Assurance in 5G Network Slices’, highlighting the relevance and impact of the study in the real world. Full article

X-ray diffractograms of high-speed steels are analyzed using machine learning algorithms to accurately classify various heat treatments. These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingly. Both agglomerative hierarchical clustering and t-distributed stochastic neighbor embedding are employed to automatically classify preprocessed X-ray datasets. The clusters obtained by this procedure agree well with the labeled data. By supervised learning via a support vector machine, hyperplanes are constructed that allow separating the clusters from each other based on the X-ray measurements. The exactness of these hyperplanes is analyzed by cross-validation. The machine learning algorithms used in this work are valuable tools to separate different microstructures based on their diffractograms. It is demonstrated that the separation of martensitic, bainitic, and pearlitic microstructures is possible based on the diffractograms only by means of machine learning algorithms, while the same problem is error-prone when looking at the diffractograms only. Full article

In the digital era, cybersecurity is integral for preserving national security, digital privacy, and social sustainability. This research emphasizes the role of non-functional equirements (NFRs) in developing secure software systems that enhance societal wellbeing by ensuring data protection, user privacy, and system robustness. Specifically, this study introduces a proof-of-concept approach by leveraging machine learning (ML) models to classify NFRs and identify security-related issues early in the software development lifecycle. Two experiments were conducted to assess the effectiveness of different models for binary and multi-class classification tasks. In Experiment 1, BERT-based models and artificial neural networks (ANNs) were fine-tuned to classify NFRs into security and non-security categories using a dataset of 803 statements. BERT-based models outperformed ANNs, achieving higher accuracy, precision, recall, and ROC-AUC scores, with hyperparameter tuning further enhancing the results. Experiment 2 assessed logistic regression (LR), a support vector machine (SVM), and XGBoost for the multi-class classification of security-related NFRs into seven categories. The SVM and XGBoost showed strong performance, achieving high precision and recall in specific categories. The findings demonstrate the effectiveness of advanced ML models in automating NFR classification, improving software security, and supporting social sustainability. Future work will explore hybrid approaches to enhance scalability and accuracy. Full article

The design of conventional digital predistortion (DPD) requires an analogue-to-digital converter (ADC) with a sampling frequency that is multiple times the signal bandwidth, which is extremely challenging for sub-Nyquist sampling systems with undersampled signals. To address this, this paper proposes a neural network (NN)-assisted wideband power amplifier (PA) DPD method for sub-Nyquist sampling systems, wherein a dual-stage architecture is designed to handle the ambiguity caused by subsampled communications signals. In the first stage, the time-delayed polynomial reconstruction method is employed to estimate the wideband DPD nonlinearity coarsely with the undersampled signals with limited pilots. In the second stage, an NN-based DPD method is proposed for the virtual training of the DPD, which learns the up-sampled DPD behavior by taking advantage of the pre-estimated DPD model and the input data signals, which reduces the length of the training sequence significantly and refines the DPD behavior efficiently. Simulation results demonstrate the efficacy of the proposed method in tackling the wideband PA nonlinearity and its ability to outperform the conventional method in terms of power spectrum, error vector magnitude, and bit error rate. Full article

The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. Model performance was evaluated using key metrics, including precision, recall, accuracy, and F1-Score. The analysis also identified significant indicators of fraud, such as Accounts Receivable Turnover, Days Outstanding Accounts Receivable, Days Payables Outstanding, Logarithm of Gross Profit, Gross Profit Margin, Inventory to Sales Ratio, and Total Asset Turnover. Among the models, Random Forest emerged as the most effective algorithm, consistently outperforming others on both training and testing datasets. Logistic Regression and SVM demonstrated strong reliability, whereas KNN and Decision Tree faced overfitting challenges, limiting their practical application. These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. The study highlights the importance of strengthening internal controls, implementing targeted fraud detection measures, and promoting regulatory improvements to enhance transparency and financial accountability. Full article

Background/Objective: World Health Organization latest statistics state that 17% of infectious diseases are transmitted by vectors, causing more than 700,000 deaths each year. Particularly, dengue (DENV), Zika (ZIKV) and yellow fever (YFV) viral infections have generated international awareness due to their epidemic proportion and risks of international spread. In this framework, the repositioning strategy of Efavirenz (EFV) represents a key clinical feature to improve different antiviral therapies. Therefore, the development of Soluplus^®-based nanomicelles (NMs) loaded with EFV (10 mg/mL) for optimized oral pharmacotherapy against ZIKV, DENV and YFV infections was investigated. Methods: EFV-NMs were obtained by an acetone diffusion technique. Micellar size and in vitro micellar interaction with mucin were assessed by dynamic light scattering. In vitro cytocompatibility was investigated in A549 and Vero cells and micellar in vitro antiviral activity against ZIKV, DENV and YFV was evaluated. In vivo oral bioavailability and histological studies were assessed in Wistar rats. Results: EFV encapsulation within Soluplus^® NMs increased the drug’s apparent aqueous solubility up to 4803-fold with a unimodal micellar size distribution and a micellar size of ~90 nm at 25 and 37 °C. Micellar in vitro interaction with mucin was also assessed in a pH range of 1.2–7.5 and its storage micellar physicochemical stability at 4 °C was confirmed over 2 years. In vitro cytocompatibility assays in A549 and Vero cells confirmed that EFV micellar dispersions resulted in safe nanoformulations. Interestingly, EFV-loaded NMs exhibited significantly higher in vitro antiviral activity compared with EFV solution for all the tested flaviviruses. In addition, the selectivity index (SI) values reveal that EFV-loaded NMs exhibited considerably more biological efficacy compared to EFV solution in A549 and Vero cell lines and for each viral infection (SI > 10). Further, the drug pharmacokinetics parameters were enhanced after the oral administration of EFV-loaded NMs, being biocompatible by not causing damage in the gastrointestinal segments. Conclusions: Overall, our EFV nanoformulation highlighted its potential as a novel drug delivery platform for optimized ZIKV, DENV and YFV antiviral therapy. Full article

Source number detection and Direction-of-Arrival (DOA) estimation are usually addressed in two stages, leading to high computational load. This paper proposes a simple solution to efficiently estimate the source number and DOAs using deep neural network (DNN) and clustering, named DNN-C. By observing that sources in space are usually few, DNN-C uses a simple fully connected DNN to obtain a spatial spectrum. Then, the K2-means clustering is specially designed to extract the source information from the obtained spatial spectrum. In particular, to enable the proposed DNN-C with the ability to detect the mixed sources, we first develop a new strategy for training data generation, and provide a guideline for data balance setting. We then explore the prior knowledge of array signal processing and spatial spectrum to obtain a peak vector and propose to add a virtual peak into the peak vector, and thus transform the task of source detection as a binary clustering problem of noise and sources. Overall, DNN-C provides a lightweight solution to implement source number detection and DOA estimation simultaneously and efficiently. Its testing time is about 2 times less than the classical solution (i.e., minimum descriptive length and multiple signal classification, shortened as MDL-MUSIC) when the grid step is 1° Importantly, it is robust to nonuniform noise by nature and can identify the absence of sources. The effectiveness of DNN-C is verified by simulation results. Furthermore, the DNN-C model trained by simulated data shows its generalization to real data measured by a circular array of eight sensors. Full article