RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect.

In this study, we investigate appropriate machine learning methods for predicting agricultural commodity prices. Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time series dataset combining wholesale prices of four agricultural commodities in South Korea, six weather variables, and week numbers. We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network.

On-site microfluidic aptasensor for rapid and highly selective detection of microcystin-LR.

To detect microcystin-LR (MC-LR), a potent cyanotoxin harmful to human health, fast and precise monitoring tools are essential, particularly for on-site applications. In this study, we developed a novel on-site microfluidic aptasensor system that utilizes a fluorescence-tagged aptamer for the rapid and selective detection of MC-LR. By employing a target non-immobilized aptamer selection technique, high-affinity DNA aptamers for MC-LR were identified, and then further optimized through sequence truncation to enhance detection efficiency.

Plasma-catalytic remediation of pharmaceutical-contaminated wastewater: Catalyst design and mechanistic insights from DFT.

Contaminants of emerging concern (CECs), characterized by persistence, poor biodegradability, and high toxicity, pose significant risks to aquatic ecosystems and human health. Plasma-catalytic processes may eliminate CECs, particularly micropollutants, from wastewater. However, discharge-zone thermalization and heavy-metal leaching from metal-based catalysts pose challenges for industrial adoption of cold plasma technology.

Evaluation of emission factors for resuspended tire-wear particles in urban road dust using empirical model-based methods.

Tire and road wear particles, major contributors to non-exhaust particulate matter emissions, are frequently resuspended into the atmosphere from road dust, posing significant environmental and health challenges. Conventional approaches to estimating emission factors (EFs) often rely on variables such as road dust loading, vehicle types, and road classifications; however, these methods typically neglect the critical influence of wind speed on resuspension dynamics. This study introduces a methodology that incorporates wind speed as a fundamental parameter to improve the accuracy of EF estimations for resuspended tire-wear particles (TWPs).

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects.

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions.

Intrinsic interaction inferred oxidative stress and apoptosis by Biosurfactant-microplastic hybrid reduces coordinated biotoxicity in zebrafish ().

The proliferation of microplastics (μP) in aquatic environments poses a significant threat to ecosystem health, with repercussions extending to aquatic organisms and potentially to human health. In this study, we investigated the efficacy of a novel biosurfactant-microplastic (BSμP) hybrid in reducing green bio-toxicity of microplastics (μP) induced by oxidative stress and apoptosis in zebrafish (). Microplastics, ubiquitous in aquatic environments, were hybridised with Biosurfactant to evaluate their potential mitigating effects.

Wide Bandgap Perovskites: A Comprehensive Review of Recent Developments and Innovations.

Recent advances in wide-bandgap (WBG) perovskite solar cells (PSCs) demonstrate a burgeoning potential to significantly enhance photovoltaic efficiencies beyond the Shockley-Queisser limit for single-junction cells. This review explores the multifaceted improvements in WBG PSCs, focusing on novel compositions, halide substitution strategies, and innovative device architectures. The substitution of iodine with bromine and organic ions such as FA and MA with Cs in the perovskite lattice is emphasized for its effectiveness in achieving higher open-circuit voltages and reduced thermalization losses.

Efficient Ultrasound-Assisted Extraction of Bioactive Molecules from Brown Macroalga : Optimal Extraction, Antioxidant and Cytotoxicity Evaluation.

(SH) is a promising marine bioresource for producing bioactive compounds. Recently, the biological functions (including anti-inflammatory, antioxidant, and anticancer activities) of SH extracts have been revealed; however, efficient extraction processes to produce bioactive molecules (such as tannin and phenol) have not been carefully designed. In this study, the ultrasound-assisted extraction process was optimized based on the response surface methodology (RSM) to efficiently produce tannin and phenol from SH.

Random Terpolymerization as a Design Strategy to Modulate Aggregation in NDI-Based Polymer Acceptors for All-Polymer Solar Cells.

Naphthalene diimide (NDI)-based conjugated polymers are prominent polymer acceptors in all-polymer solar cells (all-PSCs). However, these polymers tend to exhibit strong self-aggregation, which can cause excessive phase separation and hinder optimal donor-acceptor mixing in the bulk heterojunction blend. To address this issue, random terpolymerization was employed to modulate the aggregation of NDI-based polymer acceptors, aiming to enhance the corresponding device performance of all-PSCs.

Recent Advances in Electrochemical Biosensors for Neurodegenerative Disease Biomarkers.

Neurodegenerative diseases, such as Parkinson's disease (PD) and Alzheimer's disease (AD), represent a growing global health challenge with overlapping biomarkers. Key biomarkers, including α-synucleins, amyloid-β, and Tau proteins, are critical for accurate detection but are often assessed using conventional methods like enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), which are invasive, costly, and time-intensive. Electrochemical biosensors have emerged as promising tools for biomarker detection due to their high sensitivity, rapid response, and potential for miniaturization.

Emerging Dual-Gate FET Sensor Paradigm for Ultra-Low Concentration Cortisol Detection in Complex Bioenvironments.

Cortisol is a pivotal hormone regulating stress responses and is linked to various health conditions, making precise and continuous monitoring essential. Despite their non-invasive nature, conventional cortisol detection methods often suffer from inadequate sensitivity and reliability at low concentrations, limiting their diagnostic utility. To address these limitations, this study introduces a novel paradigm for high sensitivity cortisol detection using field-effect transistor (FET) sensors with dual-gate (DG) structures.

Wearable Standalone Sensing Systems for Smart Agriculture.

Monitoring crops' biotic and abiotic responses through sensors is crucial for conserving resources and maintaining crop production. Existing sensors often have technical limitations, measuring only specific parameters with limited reliability and spatial or temporal resolution. Wearable sensing systems are emerging as viable alternatives for plant health monitoring.

Reagentless aptamer based on the ultrasensitive and fast response electrochemical capacitive biosensor for EGFR detection in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is still the leading cause of lung cancer-related deaths globally, affecting both men and women. Mutations in the epidermal growth factor receptor (EGFR) are most common among patients with NSCLC, especially Asian patients. Here, we introduce an electrochemical capacitive biosensor for the early detection of NSCLC through specific identification of EGFR.

Impact of Electrostatic Discharge on the Degradation from Pixel to Panel Level of Quantum-Dot Light-Emitting Diode.

Recent advances in quantum dot light emitting diode (QLED) technology have enhanced their stability and efficiency. Studies have demonstrated that QLEDs are robust against oxygen, moisture, and low-voltage stress. However, the impact of instantaneous high-voltage exposure on QLEDs, which can occur during manufacturing due to electrostatic discharge (ESD) from friction between non-conductive components, remains unclear.

Amplifying visible signals in lateral flow assays using integrated nanoelectrokinetics.

In this study, we developed a tangential nanoelectrokinetic force-enhanced lateral flow assay by integrating a Nafion-cellulose nanofibrils hybrid gel to improve conventional lateral flow assay performance without external instrumentation. Our assay was designed by embedding the Nafion-cellulose nanofibrils gel beneath the test line, generating a tangential nanoelectrokinetic force that enhances the migration and accumulation of antigen-AuNP probe complexes within the visible depth of the nitrocellulose surface. The proposed assay exhibited a 32.

Various Topological Poly(tert-butyl acrylate)s and Their Impacts on Thermal and Solution Properties.

This study reports the synthesis of acrylate-based polymers with diverse topologies, including cyclic and 8-shaped structures, using a combination of atom transfer radical polymerization (ATRP) and click coupling reactions. The linear and tetra-arm poly(tert-butyl acrylate) (PtBA) polymers with similar molar masses are synthesized via the activators regenerated by electron transfer atom transfer radical polymerization. These polymers are further transformed into cyclic and 8-shaped topologies, respectively, through a click reaction.

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

Background: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning algorithm in detecting unruptured intracranial aneurysms (UIAs) using time-of-flight (TOF) magnetic resonance angiography (MRA).

Rapid and Efficient Polymer/Contaminant Removal from Single-Layer Graphene via Aqueous Sodium Nitrite Rinsing for Enhanced Electronic Applications.

The removal of surface residues from single-layer graphene (SLG), including poly(methyl methacrylate) (PMMA) polymers and Cl ions, during the transfer process remains a significant challenge with regard to preserving the intrinsic properties of SLG, with the process often leading to unintended doping and reduced electronic performance capabilities. This study presents a rapid and efficient surface treatment method that relies on an aqueous sodium nitrite (NaNO) solution to remove such contaminants effectively. The NaNO solution rinse leverages reactive nitric oxide (NO) species to neutralize ionic contaminants (e.

Construction of Chitosan Oligosaccharide-Coated Nanostructured Lipid Carriers for the Sustained Release of Strontium Ranelate.

Background: Strontium ranelate (SR) is an effective bone regeneration drug; however, its low bioavailability and strong hydrophilicity cause a strong cytotoxicity, venous thrombosis, and allergic reactions when administered in its free form. This study aims to enhance the SR bioavailability by utilizing nanostructured lipid carriers (NLC) as a drug delivery system (DDS).

Methods: To improve the drug delivery efficiency and sustained release of the NLC, their surfaces were coated with chitosan oligosaccharide (COS), a natural polymer.

Upstream-binding protein-1 promotes breast tumorigenesis by inducing NRG2-mediated metastasis, plasticity, and macrophage polarization.

Upstream binding protein 1 (UBP1) is a transcription factor (TF) of the CP2/grainyhead family involved in various biological processes, including cancer cell proliferation, differentiation, and embryonic development. While other mammalian grainyhead-like TFs have been linked to different cancers, including breast cancer (BC), the role of UBP1 in BC remains unexplored. In this study, we provide a preliminary investigation into the novel functions of UBP1 in BC.

A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocardiogram signals, utilizing the stockwell transform for feature extraction. As ECG signals are time-series data, they are transformed into the frequency domain to extract relevant features. Subsequently, a CNN is employed to capture local patterns, while a transformer architecture learns long-term dependencies.

Nanotechnology-Based Wearable Electrochemical Biosensor for Disease Diagnosis.

Recently, flexible electronics have significantly transformed information and communications technology (ICT). In particular, wearable devices, via integration with attachable biosensors, have driven the development of new types of biosensors and diagnostic devices for point-of-care testing (POCT). Moreover, wearable electrochemical biosensors can be applied to diagnose diseases in real time based on the synergistic effect generated from the incorporation of the electrochemical technique.

Novel enzymatic synthesis of 3-Hydroxybutyryl Naringin and its molecular identification and bioactive characterization.

In this study, we synthesized 6''-O-((±)-3-hydroxybutyryl)naringin (3HBN) for the first time through enzymatic esterification of naringin with 3-hydroxybutyric acid (3HB), achieving a high conversion of 80.19 % under optimized conditions within 8 h. Structural analysis via FT-IR and H NMR confirmed esterification at the C-6'' hydroxyl position on the glucose moiety of naringin.