Search Results (479)

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

Background/Objectives: Additive manufacturing (AM) technology has emerged as an innovative approach in dentistry. Recently, manufacturers have developed permanent resins engineered explicitly for the fabrication of definitive prostheses using AM techniques. This systematic review evaluated the mechanical and physical properties of 3D-printed permanent resins in comparison to milled resins and hybrid ceramics for the fabrication of indirect dental restorations. Methods: Three electronic databases—Scopus, Web of Science, and PubMed—were searched for English-language articles. Two independent researchers conducted study selection, data extraction, quality assessment, and the evaluation of the certainty of evidence. In vitro studies assessing the mechanical and physical properties of the permanent resins were included in this review. Results: A total of 1779 articles were identified through electronic databases. Following full-text screening and eligibility assessment, 13 studies published between 2023 and 2024 were included in this qualitative review. The investigated outcomes included physical properties (surface roughness, color changes, water sorption/solubility) and mechanical properties (flexural strength, elastic modulus, microhardness). Conclusions: Three-dimensionally printed permanent resins show promising potential for fabricating indirect dental restorations. However, the current evidence regarding their mechanical and physical properties remain limited and inconsistent, mainly due to variability in study methodologies. Full article

The innate immune response is an important defense against invading pathogens. Stimulator of interferon gene (STING) plays an important role in the cyclic GMP-AMP synthase (cGAS)-mediated activation of type I IFN responses. However, some viruses have evolved the ability to inhibit the function of STING and evade the host antiviral defenses. Understanding both the mechanism of action and the viruses targets of STING effector is important because of their importance to evade the host antiviral defenses. In this study, the STING (IpSTING) of Ictalurus punctatus was first identified and characterized. Subsequently, the yeast two-hybrid system (Y2HS) was used to screen for proteins from channel catfish virus (CCV, Ictalurid herpesvirus 1) that interact with IpSTING. The ORFs of the CCV were cloned into the pGBKT7 vector and expressed in the AH109 yeast strain. The bait protein expression was validated by autoactivation, and toxicity investigation compared with control (AH109 yeast strain transformed with empty pGBKT7 and pGADT7 vector). Two positive candidate proteins, ORF41 and ORF65, were identified through Y2HS screening as interacting with IpSTING. Their interactions were further validated using co-immunoprecipitation (Co-IP). This represented the first identification of interactions between IpSTING and the CCV proteins ORF41 and ORF65. The data advanced our understanding of the functions of ORF41 and ORF65 and suggested that they might contribute to the evasion of host antiviral defenses. However, the interaction mechanism between IpSTING, and CCV proteins ORF41 and ORF65 still needs to be further explored. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

This study proposes an interpretable hybrid deep learning framework for classifying attention deficit hyperactivity disorder (ADHD) using EEG signals recorded during cognitively demanding tasks. The core architecture integrates convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) layers to jointly capture spatial and temporal dynamics. In addition to the final hybrid architecture, the CNN–GRU–LSTM model alone demonstrates excellent accuracy (99.63%) with minimal variance, making it a strong baseline for clinical applications. To evaluate the role of global attention mechanisms, transformer encoder models with two and three attention blocks, along with a spatiotemporal transformer employing 2D positional encoding, are benchmarked. A hybrid CNN–RNN–transformer model is introduced, combining convolutional, recurrent, and transformer-based modules into a unified architecture. To enhance interpretability, SHapley Additive exPlanations (SHAP) are employed to identify key EEG channels contributing to classification outcomes. Experimental evaluation using stratified five-fold cross-validation demonstrates that the proposed hybrid model achieves superior performance, with average accuracy exceeding 99.98%, F1-scores above 0.9999, and near-perfect AUC and Matthews correlation coefficients. In contrast, transformer-only models, despite high training accuracy, exhibit reduced generalization. SHAP-based analysis confirms the hybrid model’s clinical relevance. This work advances the development of transparent and reliable EEG-based tools for pediatric ADHD screening. Full article

As a pivotal model organism in Lepidoptera research, the silkworm (Bombyx mori) holds significant importance in life science due to its economic value and biotechnological applications. Advancements in proteomics and bioinformatics have enabled substantial progress in characterizing the B. mori proteome. Systematic screening and identification of protein–protein interactions (PPIs) have progressively elucidated the molecular mechanisms governing key biological processes, including viral infection, immune regulation, and growth development. This review comprehensively summarizes traditional PPI detection techniques, such as yeast two-hybrid (Y2H) and immunoprecipitation (IP), alongside emerging methodologies such as mass spectrometry-based interactomics and artificial intelligence (AI)-driven PPI prediction. We critically analyze the strengths, limitations, and technological integration strategies for each approach, highlighting current field challenges. Furthermore, we elaborate on the molecular regulatory networks of Bombyx mori nucleopolyhedrovirus (BmNPV) from multiple perspectives: apoptosis and cell cycle regulation; viral protein invasion and trafficking; non-coding RNA-mediated modulation; metabolic reprogramming; and host immune evasion. These insights reveal the dynamic interplay between viral replication and host defense mechanisms. Collectively, this synthesis aims to provide a robust theoretical foundation and technical guidance for silkworm genetic improvement, infectious disease management, and the advancement of related biotechnological applications. Full article

This study aims to comparatively analyze and evaluate the postharvest quality of tomatoes, and to further screen and utilize the excellent tomato germplasm resources. Correlation analysis, principal component analysis (PCA), and cluster analysis were performed on 18 appearance and nutritional quality indicators of 57 tomato F1 hybrids (labeled accession 1# to 57#). The results show that the variation coefficients of the tested quality indicators among tomato accessions ranged from 3.77% to 42.92%. Among them, 11 indicators had variation coefficients greater than 10%. The soluble protein content had the highest variation coefficient. Six principal components were extracted through PCA, accounting for 78.696% of the variability. The appearance indicators (size, single fruit weight, and a* value) and soluble solid content played key roles in tomato quality evaluation. According to the calculated comprehensive scores, the top 10 accessions with superior overall quality were selected from the tested tomato accessions. Cluster analysis divided the 57 tomato accessions into two major groups and seven subgroups. Notably, accession 6# showed the best flavor and nutritional quality, which could be a focus for future tomato breeding. These results provide a theoretical basis for comprehensive evaluation of tomato and quality improvement in tomato breeding. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article

The 14-3-3 proteins play crucial roles in regulating plant growth, development, signal transduction and abiotic stress responses. However, there exists a scarcity of research on the role of 14-3-3 proteins in responding to abiotic stress in apples. In this study, we isolated the MdGRF22 gene from the apple 14-3-3 family. Through the screening of interacting proteins and genetic transformation of Arabidopsis thaliana and apple callus tissues, the function of the MdGRF22 gene under drought stress was verified. The coding sequence (CDS) of MdGRF22 consists of 786 bp and encodes for 261 amino acids. Through sequence alignment, the conserved 14-3-3 domain was identified in MdGRF22 and its homologous genes, which also share similar gene structures and conserved motifs. Subcellular localization revealed that the MdGRF22 protein was predominantly located in the cytoplasm and cell membrane. The yeast two-hybrid (Y2H) analysis demonstrated a possible interaction between MdGRF22 and MdSK. In addition, MdGRF22 transgenic plants generally exhibited lower superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) activities, higher malondialdehyde (MDA) levels and relative electrolyte leakage under drought conditions compared with wild-type (WT) plants. Our study suggests that MdGRF22 may reduce the drought resistance of transgenic A. thaliana and callus tissues by interacting with MdSK. This study provides a theoretical basis for further exploring the function of 14-3-3 family genes. Full article

Potato common scab is one of the major diseases posing a threat to potato production on a global scale. No chemical agents have been found to effectively control the occurrence of this disease, and research on the identification of resistance genes and the development of molecular markers remains relatively limited. In this study, a diploid potato variety H535, which exhibits resistance to the predominant pathogen Streptomyces scabies, was utilized as the male parent, whereas the susceptible diploid potato variety H012 served as the female parent. Building upon the resistance QTL intervals pinpointed through a genome-wide association study, two potential resistance loci were localized on chromosome 2 of the potato genome, spanning the regions between 38–38.6 Mb and 41.3–42.7 Mb. These intervals accounted for 18.03% of the total phenotypic variance and are presumed to be the primary QTLs underlying scab resistance. Building upon this foundation, we expanded the hybrid progeny population, conducted resistance assessments, selected individuals with extreme phenotypes, developed molecular markers, and conducted fine mapping of the resistance gene. A phenotypic evaluation of scab resistance was carried out using a pot-based inoculation test on 175 potato hybrid progenies to characterize the F1 generation population. Twenty lines exhibiting high resistance and thirty lines displaying high susceptibility were selected for investigations. Within the preliminary mapping interval on potato chromosome 2 (spanning 38–43 Mb), a total of 214 SSR (Simple Sequence Repeat) and 133 InDel (Insertion/Deletion) primer pairs were designed. Initial screening with parental lines identified 18 polymorphic markers (8 SSR and 10 InDel) that demonstrated stable segregation patterns. Validation using bulked segregant analysis revealed that 3 SSR markers (with 70–90% linkage) and 6 InDel markers (with 70–90% linkage) exhibited significant co-segregation with the resistance trait. A high-density genetic linkage map spanning 104.59 cm was constructed using 18 polymorphic markers, with an average marker spacing of 5.81 cm. Through linkage analysis, the resistance locus was precisely mapped to a 767 kb interval (41.33–42.09 Mb) on potato chromosome 2, flanked by SSR-2-9 and InDel-3-9. Within this refined interval, four candidate disease resistance genes were identified: RHC02H2G2507, RHC02H2G2515, PGSC0003DMG400030643, and PGSC0003DMG400030661. This study offers novel insights into the genetic architecture underlying scab resistance in potato. The high-resolution mapping results and characterized markers will facilitate marker-assisted selection (MAS) in disease resistance breeding programs, providing an efficient strategy for developing cultivars with enhanced resistance to Streptomyces scabies. Full article

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

BEL1-like homeodomain protein 3 (BLH3) plays a crucial role in plant development. However, its involvement in the salt stress response has not been studied. In this study, we investigated the molecular mechanism underlying the response of LpBLH3 to salt stress in Lilium pumilum (L. pumilum) using various techniques, including quantitative PCR (RT-qPCR), determination of physiological indices of plant after Saline-Alkali stress, yeast two-hybrid screening, luciferase complementation imaging (LCI), and chromosome walking to obtain the promoter sequence, analyzed by PlantCARE, electrophoretic mobility shift assay (EMSA), and then dual-luciferase reporter assay(LUC). RT-qPCR analysis revealed that LpBLH3 is most highly expressed in the leaves of L. pumilum. The expression of LpBLH3 peaks at 24 or 36 h in the leaves under different saline stress. Under various treatments, compared to the wild type (WT), the LpBLH3 overexpression lines exhibited less chlorosis and leaf curling and stronger photosynthesis. The overexpression of LpBLH3 can enhance lignin accumulation in root and stem by positively modulating the expression of crucial genes within the lignin biosynthesis pathway. Y2H and LCI analyses demonstrated that LpBLH3 interacts with LpKNAT3. Additionally, EMSA and LUC analyses confirmed that LpBLH3 can bind to the promoter of LpABI5 and upregulate the expression of ABI5 downstream genes (LpCAT1/LpATEM/LpRD29B). In summary, LpBLH3 enhances the plant’s salt tolerance through the ABA pathway and lignin synthesis. This study can enrich the functional network of the BLH transcription factor family, obtain Lilium pumilum lines with good saline-alkali resistance, expand the planting area of Lilium pumilum, and improve its medicinal and ornamental values. Additionally, the functional analysis of the BLH transcription factor family provides new insights into how crops adapt to the extreme growth environment of saline-alkali soils. Full article

Background/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures—DeiT3, MaxViT, and Swin—are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts. Full article

Carbon Capture and Storage (CCS) technology presents a practical solution for reducing industrial carbon dioxide (CO₂) emissions through underground anthropogenic CO₂ storage in depleted hydrocarbon reservoirs. The long-term storage efficiency faces several CO₂ leakage challenges that need to be addressed in the planning phase of the CCS project. Thus, effective risk assessment (RA) methodologies are crucial for ensuring safety, regulatory compliance, and public acceptance of CCS projects. This review examines RA parts and their corresponding technical and non-technical challenges. The analysis critically compares over 20 qualitative, semi-quantitative, quantitative, and hybrid RA techniques employed throughout GCS operations. Available quantitative RA tools do not deliver dependable results because they require technical data that become available late in the CCS project development process. Qualitative approaches work well for the initial screening of storage sites with limited data available, yet quantitative methods enable quantification of CO₂ leakage. For the first time, a comparative analysis of two integrated assessment tools is presented in this paper. The techniques achieve success based on high-quality data and analysis of existing technical and non-technical challenges which this paper examines. The comparative analysis outlines the limitations and advantages of every methodology studied and emphasizes the need for integrated hybrid frameworks to boost decision-making in the RA process. Future research should focus on creating or improving existing hybrid frameworks for late-stage RA while utilizing qualitative frameworks in the initial site screening stage to advance GSC’s safe and effective implementation. Full article