Structural Information Research Articles

Multi-view 3D reconstruction of seedling using 2D image contour

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R2 = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R2 = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R2 = 0.66, RMSE = 1.05 cm2, rRMSE = 7.63%; Leaf inclination angle: R2 = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R2 = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

Variation in structural motifs within SARS-related coronavirus spike proteins.

SARS-CoV-2 is the third known coronavirus (CoV) that has crossed the animal-human barrier in the last two decades. However, little structural information exists related to the close genetic species within the SARS-related coronaviruses. Here, we present three novel SARS-related CoV spike protein structures solved by single particle cryo-electron microscopy analysis derived from bat (bat SL-CoV WIV1) and civet (cCoV-SZ3, cCoV-007) hosts. We report complex glycan trees that decorate the glycoproteins and density for water molecules which facilitated modeling of the water molecule coordination networks within structurally important regions. We note structural conservation of the fatty acid binding pocket and presence of a linoleic acid molecule which are associated with stabilization of the receptor binding domains in the "down" conformation. Additionally, the N-terminal biliverdin binding pocket is occupied by a density in all the structures. Finally, we analyzed structural differences in a loop of the receptor binding motif between coronaviruses known to infect humans and the animal coronaviruses described in this study, which regulate binding to the human angiotensin converting enzyme 2 receptor. This study offers a structural framework to evaluate the close relatives of SARS-CoV-2, the ability to inform pandemic prevention, and aid in the development of pan-neutralizing treatments.

Ring artifacts suppression for X-ray CT images by fusion of dual-domain images based on improved UNet

The ring artifact is a typical artifact in X-ray Computed Tomography (CT) images, the existence of which can reduce the image's quality, change the structure and details and affects the interpretation of image information. This paper conducts research from two aspects: tomographic images and sinograms, and designs a dual-domain artifact suppression network. By introducing the Inception module and the residual module, the network's ability to extract features is improved, and the gradient diffusion is suppressed. By customizing the loss function, the network's recognition of ring artifacts is improved and the network's convergence is accelerated. Finally, the outputs of the dual-domain are fused based on the Nonsubsampled Contourlet Transform (NSCT) fusion theory to improve the quality of the image. In addition, three experiments of CT scanning are designed based on microfocus cone-beam CT system to verify the effectiveness. The model is trained and tested using real datasets. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are employed as evaluation indicators of image quality. Finally, the proposed method is compared with typical algorithms for artifact suppression. The experimental results show that the method effectively protects the structure information in CT images while suppressing ring artifacts to the greatest extent. The PSNR, SSIM, and MSE of the processed images can respectively reach 36.167 dB, 0.9847 and 6.87 × e-4.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank.

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Structural Insights of a cis-Epoxysuccinate Hydrolase Facilitate the Development of Robust Biocatalysts for the Production of l-(+)-Tartrate.

l-(+)-Tartaric acid plays important roles in various industries, including pharmaceuticals, foods, and chemicals. cis-Epoxysuccinate hydrolases (CESHs) are crucial for converting cis-epoxysuccinate to l-(+)-tartrate in the industrial production process. There is, however, a lack of detailed structural and mechanistic information on CESHs, limiting the discovery and engineering of these industrially relevant enzymes. In this study, we report the crystal structures of RoCESH and KoCESH-l-(+)-tartrate complex. These structures reveal the key amino acids of the active pocket and the catalytic triad residues and elucidate a dynamic catalytic process involving conformational changes of the active site. Leveraging the structural insights, we identified a robust BmCESH (550 ± 20 U·mg-1) with sustained catalytic activity even at a 3 M substrate concentration. After six batches of transformation, immobilized cells with overexpressed BmCESH maintained 69% of their initial activity, affording an overall productivity of 200 g/L/h. These results provide valuable insights into the development of high-efficiency CESHs and the optimization of biotransformation processes for industrial uses.

Unlocking the power of AI models: exploring protein folding prediction through comparative analysis.

Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study's main objective is to assess the accuracy of the model's predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.

Identification of Key Classes in Software Systems Based on Static Analysis and Voting Mechanism

Identifying key classes of a software system can help developers understand the system quickly, reduce the time for system maintenance, and prevent security risks caused by defects in key classes. So far, many approaches have been proposed to identify key classes from software systems. However, some approaches select too many key class candidates, making it inconvenient and difficult for developers to start understanding the system from these classes. For the other approaches, although the number of key class candidates is not large, their effectiveness needs to be further improved. To this end, in this paper, we propose a new model, named SAVM, to detect key classes by combining static analysis and a voting mechanism. First, we extract structural information from the source codes of a software system and construct a class coupling network (CCN) using this information. Then, we present the VRWD method that iteratively identifies important nodes in CCN based on a voting mechanism. Specifically, in each iteration, a node votes for its outgoing neighbors and in the meantime receives votes from its incoming neighbors. Afterward, the node that attains the highest voting score is elected as the important node in this turn. Finally, the corresponding classes of the selected important nodes are the key class candidates. The effectiveness of the proposed model and eight other baselines is evaluated in eight open-source Java projects. The experimental results show that although no method performs the best in all projects, according to the average ranking of the Friedman test, our method overall performs better compared to the baselines. In addition, this paper also proves through experiments that our approach can be applied to large-scale software projects. These indicate that our approach is a valuable technique for developers.

Invariant point message passing for protein side chain packing.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

Efficient Segmental Isotope Labeling of Integral Membrane Proteins for High-Resolution NMR Studies.

High-resolution structural NMR analyses of membrane proteins are challenging due to their large size, resulting in broad resonances and strong signal overlap. Among the isotope labeling methods that can remedy this situation, segmental isotope labeling is a suitable strategy to simplify NMR spectra and retain high-resolution structural information. However, protein ligation within integral membrane proteins is complicated since the hydrophobic protein fragments are insoluble, and the removal of ligation side-products is elaborate. Here, we show that a stabilized split-intein system can be used for rapid and high-yield protein trans-splicing of integral membrane proteins under denaturing conditions. This setup enables segmental isotope labeling experiments within folded protein domains for NMR studies. We show that high-quality NMR spectra of markedly reduced complexity can be obtained in detergent micelles and lipid nanodiscs. Of note, the nanodisc insertion step specifically selects for the ligated and correctly folded membrane protein and simultaneously removes ligation byproducts. Using this tailored workflow, we show that high-resolution NMR structure determination is strongly facilitated with just two segmentally isotope-labeled membrane protein samples. The presented method will be broadly applicable to structural and dynamical investigations of (membrane-) proteins and their complexes by solution and solid-state NMR but also other structural methods where segmental labeling is beneficial.

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.

False Discovery Rate Control For Structured Multiple Testing: Asymmetric Rules And Conformal Q-values

The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides rigorous theory for grounding complex machine learning methods without relying on strong assumptions or highly idealized models. However, existing conformal methods have limitations in handling structured multiple testing, as their validity often requires the deployment of symmetric decision rules, which assume the exchangeability of data points and permutation-invariance of fitting algorithms. To overcome these limitations, we introduce the pseudo local index of significance (PLIS) procedure, which is capable of accommodating asymmetric rules and requires only pairwise exchangeability between the null conformity scores. We demonstrate that PLIS offers finite-sample guarantees in FDR control and the ability to assign higher weights to relevant data points. Numerical results confirm the effectiveness and robustness of PLIS and demonstrate improvements in power compared to existing model-free methods in various scenarios.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides.

Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.

Multi-filed data fusion through attention-based networks for readiness prediction in aircraft maintenance: natural language processing (NLP) approach

ABSTRACT Military aircraft data is analyzed from a readiness perspective to pursue sustainability. Aircraft readiness can be described as the percentage of fighting force available to perform a mission given a fixed period. It is critical to predict the readiness length of Non-Mission Capable (NMC) to prepare for alternative strategies to achieve mission success before a failure occurs. NMC also affects the maintenance process of an aircraft. In existing readiness state analysis, domain experts must manually assess significant amounts of data and identify the frequency and severity of failure modes, which is time-consuming, subjective, and merely descriptive analytics. This paper proposes a multi-filed data fusion framework through an attention-based network to predict aircraft mission capability. The model employs and investigates structured categorical information and manually inputs textual notes. The attention-based method is applied to retain and identify critical textual details, integrated with the dense representation of various categorical features. We demonstrate that the proposed model framework can contribute to capturing and analyzing essential features related to mission capability. The proposed method’s detailed performance is compared with existing approaches.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Review of isomers in the $$A ~\approx ~135$$ region and nuclear shape evolution

AbstractThe measurements of lifetimes and electromagnetic moments of the isomeric states in some of the nuclei around $$A\approx$$ A ≈ 135 carried out at BARC-TIFR Pelletron Linac Facility at TIFR, Mumbai, are highlighted. The results from these measurements, along with similar studies reported in the literature, provide a wealth of nuclear structure information in Ba, La, and Ce isotopes in the $$A \approx$$ A ≈ 135 region. Comparison of the measured moments with the calculations gives a unique testing ground for the predictive power of the nuclear models. Plans for similar studies in other mass regions are also described at the end.

APSSF: Adaptive CNN Pruning Based on Structural Similarity of Filters

Convolutional neural network (CNN) pruning is a technique used to remove redundant parameters from the network. By doing so, it aims to greatly reduce the computational complexity and scale of the network while still preserving its accuracy. In the CNN, the majority of parameters are weights that form filters. When it comes to pruning, it is more effective to focus on removing redundant filters rather than insignificant weights within filters. The essence of filter pruning lies in determining the significance or contribution of each filter. Filters that have a significant contribution are kept, while others are pruned. Current methods for calculating contribution in pruning often rely on weight magnitude or filter similarity. However, approaches based solely on assume that small weights are unimportant and ignore correlation between filters, which leads to a significant loss of network accuracy. Those based on filter similarity flatten filter tensors into a vector when calculating filter similarity, and lose the important structural information of filters, or the superposition information of the weight convolution in the corresponding space position. These limitations can compromise the accuracy and effectiveness of the pruning process. This paper proposes an adaptive CNN pruning method based on the structural similarity of filters (APSSF) by taking both the structural characteristics of and the correlation between filters into the consideration for pruning filters. APSSF efficiently calculates the distance between the filters by factoring in information from all the dimensions of filters, and clusters the filters according to the distance threshold determined adaptively according to the compression rate, and deletes a certain number of filters from each category. On the CIFAR10 and ImageNet datasets, APSSF outperforms several state-of-the-art methods. On the CIFAR100, APSSF reduces parameters of networks by 91.71% and 74.80% on VGG-16 and ResNet-34, respectively. The accuracy was decreased only by 0.03 on VGG-16, while on ResNet-34, it was increased by 0.04.

Desvendando a influência das heterogeneidades e da extração no fluxo de água subterrânea e no transporte de soluto em um aquífero de carbonato fraturado, na Sicília, Itália

AbstractThe migration of a groundwater contaminant plume consisting of light nonaqueous phase liquids (LNAPLs) along the Ionian coastline of Sicily, Italy, has been reported to follow a trajectory that is inconsistent with the regional hydraulic gradient. The influence of several faults affecting the fractured carbonate bedrock aquifer, and groundwater abstraction from a well, were hypothesized to be responsible for the anomalous trajectory of the contaminant plume. A conceptual hydrogeological model was developed for the study area that incorporated structural information derived from geophysical surveys and the mapping of fractures in bedrock outcrops. This conceptual model was incorporated into numerical groundwater flow and solute transport models to simulate the groundwater transport of the light nonaqueous phase liquids. Four model scenarios representing different levels of complexity were tested to assess the relative influence of geological heterogeneity and groundwater abstraction on the migration of the contaminant plume. Results show that underground major discontinuity systems, invoking the presence of the faults in the model domain accounted for the observed migration of the contaminant plume, act as conduits for groundwater flow. Conversely, groundwater abstraction from a well was found to result in relatively minor, localized impacts to the migration of the contamination plume. This study demonstrates the importance of incorporating geological heterogeneity into groundwater modelling and environmental risk assessments associated with the storage of LNAPLs.

Waveform inversion with structural regularizing constraint based on gradient decomposition

Abstract Full waveform inversion (FWI) can simultaneously update low-to-medium wavenumber velocity components and high-wavenumber velocity components. However, if seismic data lack large-offset data and effective low-frequency components, FWI updates will be dominated by high-wavenumber velocity perturbation. Meanwhile, providing that the initial model is inaccurate, inversion will have the problem of local minima. In this study, FWI is developed with structural regularizing constraint based on gradient decomposition (RGDFWI). By correlating the separated forward wavefield and backward wavefield with specific propagating direction, FWI gradient is decomposed into tomography-mode gradient and migration-mode gradient. We propose an optimized strategy taking full advantage of the two modes of FWI gradient. On the one hand, we use tomography-mode gradient to enhance low-to-medium wavenumber updates. On the other hand, we use migration-mode gradient to apply structural regularizing constraint by estimating structure dip and adding sparsity constraint in Seislet domain. During the inversion process, high-wavenumber structural information constrains and guides low-wavenumber model updates. The results of two numerical tests, Marmousi model test and Overthrust model test, validate the optimized strategy, which can produce a better initial velocity model for FWI. The inversion finally generates a high-precision and high-resolution velocity model.

Real-time coordinated control strategy for the hybrid electric propulsion system of a flying car with model adaptation and game theory

Coordination among the multiple power components is essential for the hybrid electric propulsion systems since the decision made by one impacts the state and decisions of others due to the coupling mechanical and electrical dynamics. Traditional model-based approaches with weight-sum objectives may result in misleading optimization or even unstable situations because of the inherent poor scalability and synergy. To overcome this issue, this paper proposes a novel game theory-based control strategy with model adaptation mechanism for the hybrid electric flying car to enhance the control performance, system stability, and robustness. Firstly, a control-oriented model of the system is derived with the utilization of the recursive least square parameter estimation method. The non-cooperative game framework is then established with the turboshaft engine subsystem and electric supply subsystem treated as two independent players. The performance of Nash equilibrium solutions with closed-loop and open-loop information structures are investigated where the problem is iteratively solved by exploiting Pontryagin’s Minimum Principle and dynamic programming, respectively. The simulation results demonstrate that the game theory-based controllers can outperform MPC with the weight-sum objectives in terms of control efficiency and robustness improvement and the game theory-based controller with open-loop information structure shows excellent computation efficiency. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.