Structural Features Research Articles

DeepFusion: A Deep Bimodal Information Fusion Network for Unraveling Protein-RNA Interactions Using In Vivo RNA Structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion’s performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion’s code and data are available at http://bioinfo.org/deepfusion/.

3D and 2D-QSAR Studies on Natural Flavonoids for Nitric Oxide Production Inhibitory Activity

Background: Nitric oxide (NO), an important second messenger molecule, regulates numerous physiological responses, while excessive NO generates negative effects on the circulatory, nervous and immune systems. Recently, some natural flavonoids were reported to possess the capability of inhibiting LPS-induced NO production. To fully understand the nature of their own NO inhibitory activity, it is necessary to address the structural requirements of flavonoids as NO inhibitors. Objective: The objective of this work was to develop efficient QSAR models for predicting the NOinhibitory activity of new flavonoids and improving insights into the critical properties of the chemical structures that were required for the ideal NO production inhibitory activities. Methods: To provide insights into the structural basis of flavonoids as NO inhibitors, 3D quantitative structure-activity relationship (3D-QSAR) and 2D-QSAR models were developed on a dataset of 55 flavonoids using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) approaches. Results: The statistically significant models for CoMFA, CoMSIA and HQSAR resulted in crossvalidated coefficient (q2) values of 0.523, 0.572 and 0.639, non-cross-validated coefficient (r2) values of 0.793, 0.828 and 0.852, respectively. The robustness of these models was further affirmed using a test set of 18 compounds, which resulted in predictive correlation coefficients (r2 pred) of 0.968, 0.954 and 0.906. Furthermore, the models-derived contour maps were appraised for activity trends for the molecules analyzed. Conclusion: The 3D and 2D-QSAR models constructed in this paper were efficient in estimating the NO inhibitory activities of flavonoids and facilitating the design of flavonoid-derived NO production inhibitors.

Predicting Antitumor Activity of Anthrapyrazole Derivatives using Advanced Machine Learning Techniques.

Anthrapyrazoles are a new class of antitumor agents and successors to anthracyclines possessing a broad range of antitumor activity in various model tumors. The present study introduces novel QSAR models for the prediction of antitumor activity of anthrapyrazole analogues. The predictive performance of four machine learning algorithms, namely artificial neural networks, boosted trees, multivariate adaptive regression splines, and random forest, was studied in terms of variation of the observed and predicted data, internal validation, predictability, precision, and accuracy. ANN and boosted trees algorithms met the validation criteria. It means that these procedures may be able to forecast the anticancer effects of the anthrapyrazoles studied. Evaluation of validation metrics, calculated for each approach, indicated the artificial neural network (ANN) procedure as the algorithm of choice, especially with regard to the obtained predictability as well as the lowest value of mean absolute error. The designed multilayer perceptron (MLP)-15-7-1 network displayed a high correlation between the predicted and the experimental pIC50 value for the training, test, and validation set. A conducted sensitivity analysis enabled an indication of the most important structural features of the studied activity. The ANN strategy combines topographical and topological information and can be used for the design and development of novel anthrapyrazole analogues as anticancer molecules.

Recent Advances in Pyrazole-based Protein Kinase Inhibitors as Emerging Therapeutic Targets.

Pyrazole-scaffold protein kinase inhibitors (PKIs) have emerged as promising therapeutic agents for the treatment of various diseases, such as cancer, inflammatory disorders, and neurological diseases. This review article provides an overview of the pharmacological properties of pyrazole-scaffold PKIs, including their mechanism of action, selectivity, potency, and toxicity. The article also summarizes the recent developments in the design and synthesis of pyrazole-scaffold PKIs, highlighting the structural features and modifications that contribute to their pharmacological activity. In addition, the article discusses the preclinical and clinical studies of pyrazole-scaffold PKIs, including their efficacy, safety, and pharmacokinetic properties. A comprehensive search has been conducted on several online patent databases, including the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), and the World Intellectual Property Organization (WIPO). The search was conducted using pyrazole as the keyword. The search was limited to patents filed between 2015 and 2022. Patents were included if they involved articles in the fields of protein kinase inhibitors, and included literature on some pyrazoles and their pharmacological activities. Data were extracted from each included patent on the following variables: patent title, patent number, inventors, assignee, filing date, publication date, patent type, and field of invention. Data were extracted from each patent using a standardized form to ensure consistency and accuracy. The design and pharmacological evaluation of organic compounds containing pyrazole structure as biologically active substances have been done, and the key structures from the pharmacological data obtained as protein kinase inhibitors have been addressed in detail. The review concludes with a discussion on the current challenges and future directions for the development of pyrazole-scaffold PKIs as therapeutic agents. Overall, this review article provides a comprehensive summary of the pharmacological properties of pyrazole-scaffold PKIs, which will be of interest to researchers and clinicians in the field of drug discovery and development.

Dilated dendritic learning of global–local feature representation for medical image segmentation

Medical image segmentation serves as an important tool in the treatment of various medical diseases. However, achieving precise and efficient segmentation remains challenging due to the intricate structures and variations. Although neural network methods based on U-shaped structures have shown impressive results, they often lack effective representation of global–local features, leading to insufficient extraction of multi-scale and contextual information in medical image segmentation tasks. To tackle these challenges, we propose a novel approach: a dilated dendritic module with deep supervision, namely 3DL-Net. It integrates the flexible dilated convolution mechanism into the segmentation architecture, aiming to expand the model’s receptive field and capture richer global features. Additionally, in contrast to other segmentation architectures, we innovatively introduce the processing of local feature of shallow structures through the dendritic neuron module in medical images into 3DL-Net. This is the first time dendritic learning has been employed at the channel level and represents a pioneering approach to the local feature process. During the training process, 3DL-Net incorporates a deep supervision mechanism that utilizes our designed loss function. Due to the intermediate supervision signals at various network stages, providing feedback at multiple levels, the model refines its predictions across various scales, contributing to further enhanced segmentation outcomes. To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three medical image datasets to demonstrate significant improvements in segmentation accuracy compared to state-of-the-art models. Our mDice metrics on three datasets achieved 86.61%, 87.87%, and 85.06%, surpassing the second-best models by 3.85%, 1.54%, and 0.95%.

Learning Optimizer-Based Visual Analytics Method to Detect Targets in Autonomous Unmanned Aerial Vehicles

Visual analytics is vital for identifying targets by differentiating their structure and texture from unmanned aerial vehicles (UAVs). Object sensing disturbance and swift texture differentiation are tedious due to the UAV displacements. For improving the accuracy of target detection, this article introduces a learning optimizer-based visual analytics method. The proposed method assimilates deep learning and a gradient descent algorithm for feature differentiation and error minimization concurrently. The captured images are identified using multiple structural feature variations and are correlated with similar stored images. The features are extracted at different displacement and structural changing instances for leveraging accuracy. The learning process trains the similarity features during different differentiation factors. In the feature extraction, the minimum slope points are identified using a gradient descent algorithm by assigning random weights. As the differentiation increases using similar features, the minimum similarity value is detection. Postdetection, the weights are incremental and linear across different feature slopes. Therefore, the accuracy increases under varying displacement instances, preventing target misdetection. The gradient function is invariable between the minimum and maximum values for identifying high-precision features. This ensures optimal detection of different buildings and structures with high accuracy.

Iron(III) Monoglycerolate as a New Biocompatible Precursor in the Synthesis of Bioactive Nanocomposite Glycerohydrogels.

Nanocomposite glycerohydrogels based on biocompatible elementcontaining glycerolates are of practicular interest for biomedical applications. Using two biocompatible precursors, silicon and iron glycerolates, a new bioactive nanocomposite silicon‒iron glycerolates hydrogel was obtained by sol-gel method. The composition and structural features of the hydrogel were studied using a complex of modern analytical techniques, including TEM, XRD, and AES. On the example of experimental animals hemostatic activity of the hydrogel was studied, as well as primary toxicological studies were carried out. The composition of dispersed phase and dispersion medium of silicon‒iron glycerolates hydrogel was determined. The structural features of hydrogel were revealed and its structure model was proposed. It was shown that silcon-iron glycerolates hydrogel is nontoxic, and exhibits pronounced hemostatic activity. Silicon-iron glycerolates hydrogel is a potential hemostatic agent for topical application in medical and veterinary practice.

GRASS: Learning Spatial-Temporal Properties From Chainlike Cascade Data for Microscopic Diffusion Prediction.

Information diffusion prediction captures diffusion dynamics of online messages in social networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral marketing. However, there are two thorny problems caused by the loss of spatial-temporal properties of cascade data: "position-hopping" and "branch-independency." The former means no exact propagation relationship between any two consecutive infected users. The latter indicates that not all previously infected users contribute to the prediction of the next infected user. This article proposes the GRU-like Attention Unit and Structural Spreading (GRASS) model for microscopic cascade prediction to overcome the above two problems. First, we introduce the attention mechanism into the gated recurrent unit (GRU) component to expand the restricted receptive field of the recurrent neural network (RNN)-type module, thus addressing the "position-hopping" problem. Second, the structural spreading (SS) mechanism leverages structural features to filter out related users and controls the generation of cascade hidden states, thereby solving the "branch-independency" problem. Experiments on multiple real-world datasets show that our model significantly outperforms state-of-the-art baseline models on both hits@κ and map@κ metrics. Furthermore, the visualization of latent representations by t-distributed stochastic neighbor embedding (t-SNE) indicates that our model makes different cascades more discriminative during the encoding process.

Morphological, structural, and ultrastructural key features of hyperhydricity in Beta vulgaris L. var. saccharifera

Abstract Hyperhydricity, as a phenomenon specific to in vitro plants, triggers a series of changes at the cell and tissue level, which modify the plants’ physiological processes. For a better understanding of this phenomenon, we found it useful to present here the fundamental research on establishing the steps of structural and ultrastructural degradation of cells and tissues, observed by light microscopy (LM) and transmission electron microscopy (TEM) analyses, in the leaves of Beta vulgaris var. saccharifera, at 30 days of secondary culture, and their correlation with viability in the third culture. Comparative with control (non-hyperhydric plants), three degradation steps of hyperhydricity were identified in plants regenerated on a culture medium with 2.5 mg·L-1 6-benzyladenine. The first step involved cell wall deformation, which lost its rigidity and became sinuous, causing the enlargement of the intercellular spaces. In the second step, these spaces formed gaps through their union, and the whole membrane system suffered: both chloroplasts and tonoplast were broken (the cytoplasm and vacuolar composition were mixed), the nuclear membrane presented undulations, just before the damage to the tonoplast, and the nucleus became pyknotic. In step 3, the cell showed the beginning of lysis, which leads to necrosis, the cell had nothing in common with a normal ultrastructure. For in vitro plants in this final step, there was no chance of surviving but in steps I and II, the viability was 55-75%. These features can be useful to producers to calculate the level of culture damage and start measures to prevent losses.

Post-disaster investigations of damage feature of buildings and structures due to several local strong winds in China during 2021–2024

Local strong winds such as tornadoes and downbursts are rare in terms of measured wind speed data due to their rapid formation and strong destructive power. Post-disaster investigations have become an important approach for rating the strength of these two kind of winds and analyzing structural failure mechanism. From 2021 to 2024, several tornadoes and downbursts occurred in China. This study delineated changes in the intensity levels of wind disasters along their paths based on on-site disaster investigations and established standards for the level of structural damage based on the extent of functional loss and repair. This study focused on the damage feature to light steel structures, masonry structures, steel-concrete structures, and pole structures, and discussed the causes of the damage from the perspectives of construction and force transmission mechanisms. In addition, this study estimated the equivalent wind speeds based on the damage feature of pole structures and metal roof claddings. To reproduce the damage process for a group of high-rise buildings, the computational fluid dynamic (CFD) technology is used to simulate the airflow distribution inside buildings under two conditions of open and closed interior doors. The investigation results can provide useful information for the wind intensity classification and rating standards in China. Meanwhile, compared with the interior doors being open or closed, closing indoor doors during strong winds can significantly reduce indoor wind speeds, which is beneficial for ensuring safety.

Single fluorogen imaging reveals distinct environmental and structural features of biomolecular condensates.

Biomolecular condensates are viscoelastic materials. Simulations predict that fluid-like condensations are defined by spatially inhomogeneous organization of the underlying molecules. Here, we test these predictions using single-fluorogen tracking and super-resolution imaging. Specifically, we leverage the localization and orientational preferences of freely diffusing fluorogens and the solvatochromic effect whereby specific fluorogens are turned on in response to condensate microenvironments. We deployed three different fluorogens to probe the microenvironments and molecular organization of different protein-based condensates. The spatiotemporal resolution and environmental sensitivity afforded by single-fluorogen imaging shows that the internal environments of condensates are more hydrophobic than coexisting dilute phases. Molecules within condensates are organized in a spatially inhomogeneous manner, and this gives rise to slow-moving nanoscale molecular clusters that coexist with fast-moving molecules. Fluorogens that localize preferentially to the interface help us map their distinct features. Our findings provide a structural and dynamical basis for the viscoelasticity of condensates.

Pyroptosis and chemical classification of pyroptotic agents.

Pyroptosis, as a lytic-inflammatory type of programmed cell death, has garnered considerable attention due to its role in cancer chemotherapy and many inflammatory diseases. This review will discuss the biochemical classification of pyroptotic inducers according to their chemical structure, pyroptotic mechanism, and cancer type of these targets. A structure-activity relationship study on pyroptotic inducers is revealed based on the surveyed pyroptotic inducer chemotherapeutics. The shared features in the chemical structures of current pyroptotic inducer agents were displayed, including an essential cyclic head, a vital linker, and a hydrophilic tail that is significant for π-π interactions and hydrogen bonding. The presented structural features will open the way to design new hybridized classes or scaffolds as potent pyroptotic inducers in the future, which may represent a solution to the apoptotic-resistance dilemma along with synergistic chemotherapeutic advantage.

Tailoring elastic bandgaps and moduli of triply periodic minimal surface structures by a hybrid technique

This work aimed to investigate elastic dispersion behaviors of triply periodic minimal surface (TPMS) lattice structures. The dispersion curves were established using the finite element (FE) Bloch reduction method, in which the Bloch periodic boundary condition was implied via the Bloch transform matrix. Four different types of both surface- and solid-based TPMS lattices with varying relative densities were considered. It was found that only the solid-based Neovius and Schwarz structures provided complete elastic bandgaps. Then, elastic stiffnesses of the structures obtained by representative volume element (RVE) models were evaluated with regard to their achieved band gaps. Reducing the volume fraction of lattices obviously enhanced the bandgap behaviors, while decreasing the specific moduli. Moreover, new hybrid phononic lattices were introduced for tailoring the elastic bandgap and mechanical properties. The results showed that incorporating the strut-based face-centered cubic (FCC) lattice in the TPMS structures could obviously improve the bandgap characteristics and simultaneously maintain reasonable specific elastic properties. Finally, wave suppression structure by using the dispersion curves was demonstrated by examining a simple beam infilled with the periodic lattices under excited loads. It was shown that the TPMS structures could be well applied as an effective wave suppression feature in load-bearing structures.

EGST: Enhanced Geometric Structure Transformer for Point Cloud Registration.

We explore the effect of geometric structure descriptors on extracting reliable correspondences and obtaining accurate registration for point cloud registration. The point cloud registration task involves the estimation of rigid transformation motion in unorganized point cloud, hence it is crucial to capture the contextual features of the geometric structure in point cloud. Recent coordinates-only methods ignore numerous geometric information in the point cloud which weaken ability to express the global context. We propose Enhanced Geometric Structure Transformer to learn enhanced contextual features of the geometric structure in point cloud and model the structure consistency between point clouds for extracting reliable correspondences, which encodes three explicit enhanced geometric structures and provides significant cues for point cloud registration. More importantly, we report empirical results that Enhanced Geometric Structure Transformer can learn meaningful geometric structure features using none of the following: (i) explicit positional embeddings, (ii) additional feature exchange module such as cross-attention, which can simplify network structure compared with plain Transformer. Extensive experiments on the synthetic dataset and real-world datasets illustrate that our method can achieve competitive results.

Numerical simulation of the influence of wave parameters on the horizontal cylinder in the nonlinear coupling of the wave–current

Considering that marine structures are frequently subjected to the combined effects of waves and currents, the wave–current coupling environment largely determines the structural load and the surrounding water–air mixed flow, which is a typical feature of offshore structures, such as ships and offshore platforms. This study focuses on the interaction between a horizontal cylinder and a free surface in a wave–current nonlinear coupled environment using numerical simulations. The numerical method is based on the incompressible Navier–Stokes equation, with the volume-of-fluid method used to capture the free surface. Based on the second-order Stokes wave theory, we studied the impact of wave height and steepness on the cylinder force, vorticity field, free-surface deformation, and spatial–temporal distribution characteristics of entrainment bubbles. The results showed that the wave height and wave steepness have opposite effects on the root mean square (RMS) value of the force and influence the amplitude and period of the force curve. The stretching of the negative vortex led to varying degrees of double-frequency oscillation modes in the force curves. The main sources of bubbles in the wake are the breaking of the free surface and the entrainment caused by the cylinder vortex, and the bubbles caused by the former account for the majority. Compared with the wave height, an increase in wave steepness can cause a more severe interface breaking, resulting in more entrainment bubbles.

Towards Inductive and Efficient Explanations for Graph Neural Networks.

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging and nascent problem. The leading method mainly considers the local explanations, i.e., important subgraph structure and node features, to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized at the instance level. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to the lack of generalizability and hindering it from being used in the inductive setting. Besides, training the explanation model explaining for each instance is time-consuming for large-scale real-life datasets. In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which renders PGExplainer a natural approach to multi-instance explanations. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting without training the model for new instances. Thus, PGExplainer is much more efficient than the leading method with significant speed-up. In addition, the explanation networks can also be utilized as a regularizer to improve the generalization power of existing GNNs when jointly trained with downstream tasks. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification over the leading baseline.

Private housing compliance with public seismic safety measures after 2015 Gorkha earthquake in Nepal

After the Gorkha earthquake in 2015, the Nepalese government introduced a series of seismic-resistant building designs to reconstruct and repair residential dwellings. The objective of this research was to explore the extent of compliance with the new prescribed or recommended seismic-resistant features in rebuilt residential structures in two Nepal districts that were severely damaged during the earthquake. The primary source of data was a field survey conducted with a structural questionnaire among 332 affected households during October and November 2022. The survey also utilized informal interviews at three study sites in August 2023, with results showing that 95%–97 % of total respondents reconstructed their houses according to earthquake-resistant standards. The same percentage of respondents withdrew the final payment from the National Reconstruction Authority (NRA). Most reconstructed houses were smaller than before the earthquake; many respondents rebuilt one-room, one-story structures. In addition, housing typologies at the study sites changed from brick/stone and mud-based masonry to cement-based construction. Key Words: Gorkha earthquake, seismic-resistant guidelines, compliance with government safety measures, reconstructed houses, NRA.

Minimal mass optimization of tensegrity torsional structures

The advantage of high strength-to-mass ratio renders tensegrity structures suitable for application as lightweight structures. Therefore, the minimal mass optimization problem is one of the focal points in the research of tensegrity structures. Various minimal mass optimization configurations have been proposed for different types of loads. In this paper, we will continue this topic to deal with the minimal mass optimization problem of the tensegrity structure under torsional loads. The inspiration for this study stems from the constant radial angle feature of Michell structures, which is also present in the logarithmic spiral. By employing inverse stereographic projection with conformality, the logarithmic spiral can be mapped onto the sphere , the angle between the curve and the lines of latitude remains constant. On this basis, a type of tensegrity sphere structure combining the spherical logarithmic spiral and DHT (Double Helix Tensegrity) topology is proposed. Compared to the other two spheres with identical topology, the proposed tensegrity sphere structure significantly reduces the minimal mass under torsional loads. Furthermore, we have observed that when logarithmic spiral curves with opposite chirality are orthogonal, the optimal solution with minimal mass occurs. These findings demonstrate the potential of the combination of logarithmic spirals and conformal transformations, and positive effect of the orthogonal arrangement of members for mass optimization of tensegrity structures.

Multistate nonpolar resistive switching in nickel embedded polyoxovanadate for high density data storage

The evolution of the electronic industry constantly relies on downscaling of electronic devices and integrating novel materials in active regions to accomplish ever-higher speeds and new features in device structures. Employing materials that display multistate switching for resistive-random-access-memory or simply resistive memory could be a simple and effective way to realize high density data storage. In this context, we report multistate “nonpolar” resistive switching in a nickel embedded polyoxovanadate cluster, (K2H5[NiV14O40]) – a molecule that belongs to the larger polyoxometalate family. We observed unique and distinctive nonpolar resistive switching behaviour for the first time in a multi-redox polyoxometalate cluster. The switching characteristics were repeatable for more than 200 cycles. Our two terminal Al/K2H5[NiV14O40])/ITO memory cells exhibited considerably high resistance window (105) and also long retention time (2000 s). This work holds promise for a novel strategy in order to achieve multilevel storage by exploiting different varieties of polyoxometalate molecules as active switching element that can possibly connect memory with neuromorphic computing.

Scaled boundary finite element based two-level learning approach for structural flaw identification

A comprehensive framework that combines classification and regression techniques through machine learning (ML) algorithms to address the inverse problem of flaw identification and quantification is proposed. The framework uses the scaled boundary finite element method (SBFEM) and quadtree (octree mesh in 3D) to compute displacements for various flaw shapes and specified boundary conditions. The flaw data, such as their numbers, location, and size, along with the displacement data, serve as the input for ML models. A random forest model is used to predict the number of flaws, while an artificial neural network (ANN) employing a fully connected multi-layer perceptron is utilized to predict flaw locations and sizes. The framework underwent rigorous testing to identify the number and location of multiple flaws (circular and elliptical) in 2D structures. The accuracy of the classification and regression models for flaw detection, ranging from one to four circular flaws, is observed to be 98%. As an extension of its capabilities, the proposed model successfully identifies multiple flaws and their features in 3D structures as well with 98% accuracy, demonstrating the robustness of the proposed approach.