Search Results (785)

Molecular surveillance of FMD epidemiology is a fundamental tool for advancing our understanding of virus biology, monitoring virus evolution, and guiding vaccine design. The accessibility of genetic data will facilitate a more comprehensive delineation of FMDV phylogeny on a global scale. In this study, we investigated the FMDV strains circulating in Russia during the 2013–2014 period in geographically distant regions utilizing whole genome sequencing followed by maximum-likelihood phylogenetic reconstruction of whole genome and VP1 gene sequences. Phylogenetic analysis showed congruence in the topology of the phylogenetic trees constructed using the complete genome and VP1 gene sequence, clearly demonstrating that the isolates analyzed belong to two distinct genetic lineages: A/SEA97 in the Far East and Iran-05 in the North Caucasus. The A/SEA97 isolates exhibited a close genetic identity to those from China and Mongolia, whereas the Iran-05 isolates demonstrated clusterization with those from Turkey. The vaccine-matching studies with isolates from the Far East and North Caucasus revealed no antigenic homology with A/SEA-97 (r₁ = 0.015–0.29) and A/Iran 05 (r₁ = 0.009–0.17). The close genetic relationship of FMDV in the reported outbreak waves to those from neighboring countries indicates that animal movement could contribute to spillover and virus dispersal. The phylogenetic data reported here provide insight into the molecular epidemiology of FMD in the Eurasia region, elucidating the circulation pattern, molecular evolution, and genetic diversity, which is highly valuable for guiding vaccine designs and improving regional eradication policies. Full article

Vector road networks are vital components of intelligent transportation systems and electronic navigation maps. There is a pressing need for efficient and rapid dynamic updates for road network data. In this paper, we propose a series of methods designed specifically for geometric change detection and the topological consistency updating of multi-source vector road networks without relying on complicated road network matching. For geometric change detection, we employ buffer analysis to compare various sources of vector road networks, differentiating between newly added, deleted, and unchanged road features. Furthermore, we utilize road shape similarity analysis to detect and recognize partial matching relationships between different road network sources. For incremental updates, we define topology consistency and propose three distinct methods for merging road nodes, aiming to preserve the topological integrity of the road network to the greatest extent possible. To address geometric conflicts and topological inconsistencies, we present a fusion and update method specifically tailored for partially matched road features. In order to verify the proposed methods, a road central line network with a scale of 1:10000 from the official institution is employed to geometrically update the commercial navigation road network of a similar scale in the remote area. The experiment results indicate that our method achieves an impressive 91.7% automation rate in detecting geometric changes for road features. For the remaining 8.3% of road features, our method provides suggestions on potential geometric changes, albeit necessitating manual verification and assessment. In terms of the incremental updating of the road network, approximately 89.2% of the data can be seamlessly updated automatically using our methods, while a minor 10.8% requires manual intervention for road updates. Collectively, our methods expedite the updating cycle of vector road network data and facilitate the seamless sharing and integrated utilization of multi-source road network data. Full article

The NAC gene family plays a crucial role in plant growth, development, and responses to biotic and abiotic stresses. Paspalum Vaginatum, a warm-season turfgrass with exceptional salt tolerance, can be irrigated with seawater. However, the NAC gene family in seashore paspalum remains poorly understood. In this study, genome-wide screening and identification were conducted based on the NAC (NAM) domain hidden Markov model in seashore paspalum, resulting in the identification of 168 PvNAC genes. A phylogenetic tree was constructed, and the genes were classified into 18 groups according to their topological structure. The physicochemical properties of the PvNAC gene family proteins, their conserved motifs and structural domains, cis-acting elements, intraspecific collinearity analysis, GO annotation analysis, and protein–protein interaction networks were analyzed. The results indicated that the majority of PvNAC proteins are hydrophilic and predominantly localized in the nucleus. The promoter regions of PvNACs are primarily enriched with light-responsive elements, ABRE motifs, MYB motifs, and others. Intraspecific collinearity analysis suggests that PvNACs may have experienced a large-scale gene duplication event. GO annotation indicated that PvNAC genes were essential for transcriptional regulation, organ development, and responses to environmental stimuli. Furthermore, the protein interaction network predicted that PvNAC73 interacts with proteins such as BZIP8 and DREB2A to form a major regulatory hub. The transcriptomic analysis investigates the expression patterns of NAC genes in both leaves and roots under varying durations of salt stress. The expression levels of 8 PvNACs in roots and leaves under salt stress were examined and increased to varying degrees under salt stress. The qRT-PCR results demonstrated that the expression levels of the selected genes were consistent with the FPKM value trends observed in the RNA-seq data. This study established a theoretical basis for understanding the molecular functions and regulatory mechanisms of the NAC gene family in seashore paspalum under salt stress. Full article

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann–Gibbs, von Neumann, Shannon, Kolmogorov–Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. In particular, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review nicely illustrate the power and versatility of entropy in data analysis and machine learning. Full article

The failure probability of a pipeline is a quantification of the likelihood of an accident occurring in the pipeline, which is an indispensable part of the pipeline risk assessment process. To solve the problems of strong subjectivity, low feasibility, and low accuracy in the existing pipeline failure probability calculation methods, a three-layer Bayesian network topology model of “pipeline failure–failure cause–influencing factor” is proposed, with the pipeline failure as the subnode, the type of pipeline failure as the intermediate node, and the factors affecting the pipeline failure as the parent node of the network. Based on data fitting and fuzzy theory analysis methods, the functional relationship between the impact factor and the failure frequency of various pipelines is quantified. Using the mean value theorems for definite integrals and the analytic hierarchy process, the conditional probability of the directed edge in the network is calculated. The proposed function relationship provides a method to calculate the prior probability according to the parameters of the pipeline and its surroundings and a new idea to train the network model even without sufficient data. Full article

Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC. Methods: Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The “Climbing senecio-Hepatocellular Carcinoma Targets” network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio’s bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio’s primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization. Results: Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC. Conclusions: This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation. Full article

The influence of Artificial Intelligence in our society is becoming important due to the possibility of carrying out analysis of the large amount of data that the increasing number of interconnected devices capture and send as well as making autonomous and instant decisions from the information that machines are now able to extract, saving time and efforts in some determined tasks, specially in the cyberspace. One of the key issues concerns security of this cyberspace that is controlled by machines, so the system can run properly. A particular situation, given the heterogeneous and special nature of the environment, is the case of IoT. The limited resources of some components in such a network and the distributed nature of the topology make these types of environments vulnerable to many different attacks and information leakages. The capability of Generative Artificial Intelligence to generate contents and to autonomously learn and predict situations can be very useful for making decisions automatically and instantly, significantly enhancing the security of IoT systems. Our aim in this work is to provide an overview of Generative Artificial Intelligence-based existing solutions for the very diverse set of security issues in IoT environments and to try to anticipate future research lines in the field to delve deeper. Full article

In this paper, we report a regiospecific 1,3-dipolar cycloaddition (1,3-DC) reaction of nitrilimines with thioaurone derivatives that afforded the hitherto unreported spiropyrazolines. Spectroscopic and spectrometric data were utilized to confirm the structure of all products and elucidate the reaction’s regiochemistry. A mechanistic study was performed within the Molecular Electron Density Theory (MEDT) at the B3LYP/6-311G(d,p) computational level to explain the regioselectivity observed. The electron localization function (ELF) topological analysis confirms the carbenoid-type (cb-type) mechanism of the cycloaddition reactions between nitrilimines and thioaurones. The intermolecular interactions between reagents in this reaction account for the regioselectivity observed experimentally. Full article

We present a mathematical framework for analyzing fractal patterns in AI-generated images using persistent homology. Given a text-to-image mapping $M:T\to I$, we demonstrate that the persistent homology groups $H_k\left(t\right)$ of sublevel set filtrations ${\left\{f^{-1}\left((-\infty ,t]\right)\right\}}_{t\in \mathbb{R}}$ characterize multi-scale geometric structures, where $f:M\left(p\right)\to \mathbb{R}$ is the grayscale intensity function of a generated image. The primary challenge lies in quantifying self-similarity in scales, which we address by analyzing birth–death pairs $(b_i,d_i)$ in the persistence diagram $PD\left(M\right(p\left)\right)$. Our contribution extends beyond applying the stability theorem to AI-generated fractals; we establish how the self-similarity inherent in fractal patterns manifests in the persistence diagrams of generated images. We validate our approach using the Stable Diffusion 3.5 model for four fractal categories: ferns, trees, spirals, and crystals. An analysis of guidance scale effects $\gamma \in [4.0,8.0]$ reveals monotonic relationships between model parameters and topological features. Stability testing confirms robustness under noise perturbations $\eta \le 0.2$, with feature count variations $\Delta {\mu }_f<0.5$. Our framework provides a foundation for enhancing generative models and evaluating their geometric fidelity in fractal pattern synthesis. Full article

With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%. Full article

The family Apataniidae consists of two subfamilies, Apataniinae and Moropsychinae. Currently, there are 204 valid species of Apataniidae, which are widely distributed throughout the northern hemisphere. The larvae typically inhabit cold-water environments, and they serve as biological indicators for monitoring the health of freshwater ecosystems. The phylogenetic relationships within Apataniidae are not fully understood. Moreover, the available molecular data of Apataniidae are still limited. Herein, we provided the mitochondrial genomes of eight apataniid species and compared them with the published mitochondrial genomes of Apataniidae. The nine newly obtained sequences ranged from 15,070 bp to 16,737 bp in length. The results of the nonsynonymous with synonymous substitution rates displayed that ATP8 had the highest evolutionary rate, while COXI exhibited the lowest. The ND4L may be an effective molecular marker for the classification of the Apataniidae. Based on the published mitogenomes, we constructed a phylogenetic tree for Limnephiloidea and conducted a preliminary analysis of its advanced phylogeny. The ML and BI analyses recover the monophyly of Apataniidae and Limnephilidae. Except for PCG, BI tree based on other matrices consistently showed the topology: (Apataniana + (Moropsyche + (Apatidelia + Apatania))). The taxonomic status of Apatania and Apatidelia were also preliminarily explored. The mitochondrial genome of Apataniidae provides critical genomic resources for understanding the phylogenetic relationships of Apataniidae. Full article

The monitoring technology for targets such as aircraft and vehicles has rapidly developed in recent years and is widely used in national airspace security supervision, urban traffic supervision, and the tracking of special targets. However, the sparse trajectories of targets, primarily caused by the insufficient density of monitoring points, significantly reduce their usability. Therefore, it is important to reconstruct the target trajectories. Existing methods for the reconstruction of target trajectories often rely on topological data and convert trajectory reconstruction into a trajectory matching problem. Such methods heavily rely on topological data and cannot reconstruct trajectories in free space. To address this issue, we proposed a trajectory reconstruction method, named Prob-Attn, which does not rely on topological data and can accurately reconstruct target trajectories in free space. This method can be divided into two steps: first, a spatial trajectory construction module is proposed to determine the spatial trajectories of targets. Then, based on the reconstructed spatial trajectory of the target, this paper proposes a time series prediction model based on historical trajectories and an attention mechanism, which considers the impact of the target’s activity cycle and the surrounding status to predict the time series inside the trajectory. Finally, the proposed method is evaluated on real automatic vehicle detection datasets collected in Chongqing, China. The experimental results show that, compared with traditional methods, the proposed method can reconstruct the spatiotemporal trajectory of the target more accurately. The reconstructed trajectory data can be used for critical applications such as the intent and behavior analysis of key targets in national airspace and ground areas, providing valuable insights into security and safety. Full article

Due to the increasing integration of new energy sources, the power system now exhibits low inertia, in which the broadband oscillation problem is increasingly significant in the face of the strong coupling of complex and variable power systems, and the current lack of uniform and effective mathematical models and analysis methods. To solve this major problem, a broadband oscillation localization method based on the combination of compressed perception and graph attention network (GAT) is proposed. The method firstly uses the principle of compression perception to compress and transmit the oscillation time series data of the sub-station, reconstructs the compressed signal at the master station and aggregates the grid topology and node characteristic information to effectively reduce the redundancy of the oscillation data; reconstruction error is only 0.031, takes into account the balance of the samples and the effectiveness of the computation, and adopts the multi-attention mechanism and the cross-entropy loss function to improve the performance of the model training. Finally, the offline training and online evaluation model based on the GAT algorithm is constructed, and the accuracy of the model is up to 98.5%; and the results show that the method has a high positioning accuracy and a certain anti-noise ability at the same time. Full article

In radiation tumor therapy, irradiation, on one hand, should cause cell death to the tumor. On the other hand, the surrounding non-tumor tissue should be maintained unaffected. Therefore, methods of local dose enhancements are highly interesting. Gold nanoparticles, which are preferentially uptaken by very-fast-proliferating tumor cells, may enhance damaging. However, the results in the literature obtained from cell culture and animal tissue experiments are very contradictory, i.e., only some experiments reveal increased cell killing but others do not. Thus, a better understanding of cellular mechanisms is required. Using the breast cancer cell model SkBr3, the effects of gold nanoparticles in combination with ionizing radiation on chromatin network organization were investigated by Single-Molecule Localization Microscopy (SMLM) and applications of mathematical topology calculations (e.g., Persistent Homology, Principal Component Analysis, etc.). The data reveal a dose and nanoparticle dependent re-organization of chromatin, although colony forming assays do not show a significant reduction of cell survival after the application of gold nanoparticles to the cells. In addition, the spatial organization of γH2AX clusters was elucidated, and characteristic changes were obtained depending on dose and gold nanoparticle application. The results indicate a complex response of ALU-related chromatin and heterochromatin organization correlating to ionizing radiation and gold nanoparticle incorporation. Such complex whole chromatin re-organization is usually associated with changes in genome function and supports the hypothesis that, with the application of gold nanoparticles, not only is DNA damage increasing but also the efficiency of DNA repair may be increased. The understanding of complex chromatin responses might help to improve the gold nanoparticle efficiency in radiation treatment. Full article

In this study, an urban airspace assessment mechanism is proposed and validated using the actual urban building data, offering a systematic approach to airspace selection for unmanned aerial vehicle (UAV) operations. Two metrics are involved to assess the urban airspace accurately, which are the airspace availability and risk to ground population. The former is measured by analyzing the connectivity of the urban airspace which particularly emphasizes the impact of urban features like buildings and obstacles. The latter is quantized by using a previously proposed risk estimation model, with which an urban risk map can be generated. Quadrant analysis and Pareto ranking are then employed to evaluate the available airspace for UAVs. Quadrant analysis maps the urban airspace availability and risk to ground population onto a two-dimensional space. Additionally, Pareto ranking determines a set of Pareto-optimal solutions wherein no objective can be improved without compromising at least one other objective. The topology of urban airspace could be constructed by using the top 50% of grids ranked by Pareto ranking based on the actual building data. A case study is conducted in a densely populated urban area in Changqing District, Jinan, Shandong Province, China. The connectivity of the airspace topology is verified by employing the A-star algorithm to generate a feasible path for UAVs. Full article