Recognition Of Structures Research Articles

N-methyl mesoporphyrin IX (NMM) as electrochemical probe for detection of guanine quadruplexes

G-quadruplexes (G4) are stable alternative secondary structures of nucleic acids. With increasing understanding of their roles in biological processes and their application in bio- and nanotechnology, the exploration of novel methods for the analysis of these structures is becoming important. In this work, N-methyl mesoporphyrin IX (NMM) was used as a voltammetric probe for an easy electrochemical detection of G4s. Cyclic voltammetry on a hanging mercury drop electrode (HMDE) was used to detect NMM with a limit of detection (LOD) of 40 nM. Characteristic reduction signal of NMM was found to be substantially higher in the presence of G4 oligodeoxynucleotides (ODNs) than in the presence of single- or double-stranded ODNs and even ODNs susceptible to form G4s but in their unfolded, single-stranded forms. Gradual transition from unstructured single strand to G4, induced by increasing concentrations of the G4 stabilizing K+ ions, was detected by an electrochemical method for the first time. All obtained results were supported by circular dichroism spectroscopy. This work expands on the concept of electrochemical probes utilization in DNA secondary structure recognition and offers a proof of principle that can be potentially employed in the development of novel electroanalytical methods for nucleic acid structure studies.

Unsteady Flow Structure of Corner Separation in a Highly Loaded Compressor Cascade

Abstract Corner separation is an inherently unsteady flow feature in an axial compressor cascade, and it significantly affects the aerodynamic performance of compressors. The flow field of a highly loaded compressor cascade at the Mach number of 0.59 under the moderate separation condition is simulated based on delayed detached eddy simulation. Comparisons of averaged flow field and transient flow field show that the three-dimensional corner separation flow is highly unsteady and composed of fine-scale vortex structures. The classical recognition of corner separation structures is a consequence of time-averaging. To better understand the contribution of unsteady structures to the averaged flow structures, the evolutions of flow fields in time series and the power spectrums are analyzed. A dominant periodic flow fluctuation is caused by the development of separating vortices with a characteristic frequency around 3500 Hz or at a Strouhal number of 0.75. Further, energy scales and spatiotemporal features of these dominant unsteady behaviors are analyzed using proper orthogonal decomposition and dynamic mode decomposition methods. Results show that the low-frequency behaviors mainly caused by the passage vortex at lower-span regions govern large-scale changes of separation flow in size and intensity and act with certain intermittency. The vortex developing mode around 3500 Hz prevails at higher regions affected by the concentrated shedding vortex. As the separating vortices dissipate approaching the midspan, the effect of the vortex developing mode on axial velocity fluctuation is reduced, although it dominates the pressure fluctuation with good stability in the whole passage.

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

Zircon Microstructures in Large, Deeply Eroded Impact Structures and Terrestrial Seismites

Abstract Extraterrestrial cratering was a critical component in the evolution of the early Earth, but discovery of very ancient and deeply exhumed impact structures remains elusive, as identification tools are in short supply. The recognition of such structures is hindered by post-cratering geological processes, whereby impact-induced features common in younger, upper-crustal craters may be lost. In this study, we make a detailed analysis of planar microstructures in zircons from four large, confirmed impact structures (Manicouagan, Rochechouart, Sudbury, Vredefort) preserved at different crustal levels, from two previously described non-impact seismites in SW Norway and northern Italy, and from the 3.0 Ga Maniitsoq structure in West Greenland. A total of ~3400 zircon grains were studied using exterior and interior scanning and transmission electron microscopy. We show for the first time that shocked zircons contain two successive, principally different types of planar microstructures, only one of which is diagnostic of impact. Closely spaced, contiguous microplanes (CP) are formed first, presumably by the shock wave. In Manicouagan and Rochechouart zircons the exterior CPs have ultrathin interior counterparts of straight dislocation arrays, as identified in Manicouagan zircon using transmission electron microscopy. They have the same close spacing and orientations as the exterior CPs and are abundantly decorated with tiny pores down to less than 50 nm across. These interior CPs are identical to shock-induced decorated, partly annealed amorphous planar microstructures in quartz (planar deformation features, PDFs) and are interpreted as such. The second type is open planar fractures (PF). They are widely and irregularly spaced and texturally younger than the CPs. They re-use and displace the CP orientations, which they cut in stepwise fashion. We interpret these PFs as formed by impact-induced seismic shaking in the wake of the shock wave. We confirm two previous reports of isolated planar fractures in zircons from non-impact seismites, showing that PFs per se are not impact-diagnostic. There are no CPs in any of these zircons. Zircons from different parts of the Maniitsoq structure contain CPs in various states of preservation besides PFs, corroborating that this very large and very deeply exhumed structure resulted from an extraterrestrial impact.

Advances in methods and applications of single-cell Hi-C data analysis

Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.

Medical image recognition based on VGGNet19

Gastrointestinal diseases are one of the common clinical diseases, which often require medical imaging for diagnosis and treatment. Recently, the development of deep learning technology has promoted the development of medical image recognition, which provides new ideas and methods for the automatic recognition and analysis of medical images. VGGNet19 is a convolutional neural network model that has attracted much attention because of its simple structure, easy training and better recognition effect. For this reason, this paper proposes an improved VGGNet19 model for medical image recognition of gastrointestinal diseases. Specifically, the project adds an additional fully connected layer and Dropout layer on top of the built VGGNet19 to achieve the recognition of medical images of stomach diseases. Extensive experiments on standard medical stomach images show that the proposed method improves the recognition performance to a certain extent.

Cheminformatics Microservice: unifying access to open cheminformatics toolkits

In recent years, cheminformatics has experienced significant advancements through the development of new open-source software tools based on various cheminformatics programming toolkits. However, adopting these toolkits presents challenges, including proper installation, setup, deployment, and compatibility management. In this work, we present the Cheminformatics Microservice. This open-source solution provides a unified interface for accessing commonly used functionalities of multiple cheminformatics toolkits, namely RDKit, Chemistry Development Kit (CDK), and Open Babel. In addition, more advanced functionalities like structure generation and Optical Chemical Structure Recognition (OCSR) are made available through the Cheminformatics Microservice based on pre-existing tools. The software service also enables developers to extend the functionalities easily and to seamlessly integrate them with existing workflows and applications. It is built on FastAPI and containerized using Docker, making it highly scalable. An instance of the microservice is publicly available at https://api.naturalproducts.net. The source code is publicly accessible on GitHub, accompanied by comprehensive documentation, version control, and continuous integration and deployment workflows. All resources can be found at the following link: https://github.com/Steinbeck-Lab/cheminformatics-microservice.Graphical

Adamantane-containing drug delivery systems

Adamantane is a weakly functional hydrocarbon widely used to develop new drug molecules to improve their pharmacokinetic and pharmacodynamic parameters. The compound has an affinity for the lipid bilayer of liposomes, enabling its application in targeted drug delivery and surface recognition of target structures. This review presents the available data on developed liposomes, cyclodextrin complexes, and adamantane-based dendrimers. Adamantane has been used in two ways – as a building block to which various functional groups are covalently attached (adamantane-based dendrimers) or as a part of self-aggregating supramolecular systems, where it is incorporated based on its lipophilicity (liposomes) and strong interaction with the host molecule (cyclodextrins). Adamantane represents a suitable structural basis for the development of drug delivery systems. The study of adamantane derivatives is a current topic in designing safe and selective drug delivery systems and molecular carriers.

Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos)

EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object detection. We developed 2 EUS-based CNN models for normal anatomic structure recognition during real-time linear- and radial-array EUS evaluations. The study was performed from February 2020 to June 2022. Consecutive patient videos of linear- and radial-array EUS videos were recorded. Expert endosonographers identified and labeled 20 normal anatomic structures within the videos for training and validation of the CNN models. Initial CNN models (CNNv1) were developed from 45 videos and the improved models (CNNv2) from an additional 102 videos. CNN model performance was compared with that of 2 expert endosonographers. CNNv1 used 45,034 linear-array EUS frames and 21,063 radial-array EUS frames. CNNv2 used 148,980 linear-array EUS frames and 128,871 radial-array EUS frames. Linear-array CNNv1 and radial-array CNNv1 achieved a 75.65% and 71.36% mean average precision (mAP) with a total loss of .19 and .18, respectively. Linear-array CNNv2 obtained an 88.7% mAP with a .06 total loss, whereas radial-array CNNv2 achieved an 83.5% mAP with a .07 total loss. CNNv2 accurately detected all studied normal anatomic structures with a >98% observed agreement during clinical validation. The proposed CNN models accurately recognize the normal anatomic structures in prerecorded videos and real-time EUS. Prospective trials are needed to evaluate the impact of these models on the learning curves of EUS trainees.

Detection of fetal facial anatomy in standard ultrasonographic sections based on real-time target detection network.

At present, prenatal ultrasound is one of the important means for screening fetal malformations. In the process of prenatal ultrasound diagnosis, the accurate recognition of fetal facial ultrasound standard plane is crucial for facial malformation detection and disease screening. Due to the dense distribution of fetal facial images, no obvious structure contour boundary, small structure area, and large area overlap in the middle of the structure detection frame, this paper regards the fetal facial standard plane and its structure recognition as a universal target detection task for the first time, and applies real-time YOLO v5s to the fetal facial ultrasound standard plane structure detection and classification task. First, we detect the structure of a single slice, and take the structure of a slice class as the recognition object. Second, we carry out structural detection experiments on three standard planes; then, on the basis of the previous stage, the images of all parts included in the ultrasound examination of multiple fetuses were collected. In the single-class structure detection experiment and the structure detection and classification experiment of three types of standard planes, the overall recognition effect of Precision and Recall index data is better, with Precision being 98.3% and 98.1%, and Recall being 99.3% and 98.2%, respectively. The experimental results show that the model has the ability to identify fetal facial anatomy and standard sections in different data, which can help the physician to automatically and quickly screen out the standard sections of each fetal facial ultrasound.

High‐Resolution Mapping Nanoscale Hydrophobicity for Fine Structures and Dynamic Evolution of Nanomaterial Surface

Nanomaterial hydrophobicity plays a critical role in interfacial phenomena ranging from biological toxicity to chemical reactions. However, it is difficult to figure out the high‐resolution surface hydrophobicity at the nanoscale. Herein, a chemical force microscopy is demonstrated to profile in situ hydrophobicity images with the nanoscale resolution, exhibiting order‐of‐magnitude gain than the traditional methods. This method is utilized to rapidly recognize the spatial fine structure hydrophobicity on Au, graphite, mica, and graphene oxides (GO), enabling the recognition of complicated substances and structures. It is found that the hydrophobicity of GO is opaque and is independent of stacking thickness, which is entirely different from the original graphene. Especially, the regions of wrinkles/edges are first proved to be generally less attractive to the hydrophobic probe than flat areas. This method is used to observe the dynamic evolution of GO hydrophobicity in different aqueous conditions, and is capable of detecting local oxidation variation during interfacial reactions.

Novel Multi-Feature Fusion Facial Aesthetic Analysis Framework

Machine learning has been used in facial beauty prediction studies. However, the integrity of facial geometric information is not considered in facial aesthetic feature extraction, and the impact of other facial attributes (expression) on aesthetics. We propose a novel multi-feature fusion facial aesthetic analysis framework (NMFA) to overcome this problem. First, we designed a facial shape feature, which is an intuitive, visual quantitative description, based on B-spline. Second, we designed a representative low-dimensional facial structural feature to establish the theoretical basis of the facial structure, based on facial aesthetic structure and expression recognition theory. Next, we designed texture and holistic features based on Gabor and VGG-face network. Finally, we used a multi-feature fusion strategy to fuse them for aesthetic evaluation. Experiments were conducted on four databases. The results revealed that the proposed method realizes the visualization of facial shape features, enriches geometric information, solves the problem of lack of facial geometric information and difficulty to understand, and achieves excellent performance with fewer parameters.

EP24.09: Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart

EP24.09: Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart

From blocks to cities: Morphology structure rooted in 3D patterns and forming clusters at the block level

This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks. Utilizing the T-distributed stochastic neighbor (TSNE) embedding algorithm for cluster analysis, the visually complex and disordered urban 3D texture is translated into distinct form clusters, enabling the recognition of the overall urban form structure from the block perspective. The research methodology includes experiments conducted in the central area of Nanjing and comparative analysis in three neighboring cities: Shanghai, Hangzhou, and Suzhou. Results demonstrate the efficacy of form parameters and cluster analysis in achieving sound recognition. The four cities differ remarkably in the number and distribution structure of clusters. Shanghai has the fewest types of clusters with a compact distribution, Suzhou has the most types with a dispersed distribution, and Hangzhou and Nanjing exhibit similar characteristics, located between Shanghai and Suzhou. Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas. This research implies that governments and urban planners can exploit neighborhood morphological types to devise customized spatial management and renewal strategies. The overall urban structure can be improved by strategically minimizing the quantity and distribution of neighborhood morphological types, fostering socioeconomic development.

Structural brain changes inemotion recognition across the adult lifespan.

Emotion recognition (ER) declines with increasing age, yet little is known whether this observation is based on structural brain changes conveyed by differential atrophy. To investigate whether age-related ER decline correlates with reduced grey matter (GM) volume in emotion-related brain regions, we conducted a voxel-based morphometry analysis using data of the Human Connectome Project-Aging (N = 238, aged 36-87) in which facial ER was tested. We expected to find brain regions that show an additive or super-additive age-related change in GM volume indicating atrophic processes that reduce ER in older adults. The data did not support our hypotheses after correction for multiple comparisons. Exploratory analyses with a threshold of P < 0.001 (uncorrected), however, suggested that relationships between GM volume and age-related general ER may be widely distributed across the cortex. Yet, small effect sizes imply that only a small fraction of the decline of ER in older adults can be attributed to local GM volume changes in single voxels or their multivariate patterns.

Synthesis, structural recognition, and biological activities of mono-, di-, tri-, and tetracationic triazolium sulfonamide ionic liquids

Synthesis, structural recognition, and biological activities of mono-, di-, tri-, and tetracationic triazolium sulfonamide ionic liquids

Recognition of airfoil wake structure based on deep learning

Inspired by the perception ability of flows by organs of aquatic organisms, an identification method of airfoil wake structure based on deep learning is proposed. The wake structure of a NACA0012 airfoil at different Reynolds numbers and angles of attack collected by vertical soap film experimental device is used as the training object. A convolutional neural network is established to extract the features of wake structure of the airfoil. Bivariate identification, including the Reynolds number and angle of attack, is realized with high accuracy. Firstly, the variation law of airfoil wake structure is analyzed, and the loss function and the accuracy of verification set of three convolution neural networks with different structures are compared under the condition of small samples. The network with higher recognition ability is selected as the training model. Then, the data set is divided into training set, verification set and testing set with the ratio 3:1:1. The test set that does not participate in the training is only used for the evaluation, and the test results are expressed by a confusion matrix. The results show that the overall recognition accuracy of the model is above 92%.

Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with Brain-inspired computing pattern recognition in Industry 4.0

ABSTRACT Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.

TableStrRec: framework for table structure recognition in data sheet images

TableStrRec: framework for table structure recognition in data sheet images

Unveiling the interaction between DNA G-quadruplexes and RG-rich peptides

G-quadruplexes are non-canonical DNA secondary structures formed within guanine-rich strands that play important roles in various biological processes, including gene regulation, telomere maintenance and DNA replication. The biological functions and formation of these DNA structures are strictly controlled by several proteins that bind and stabilize or resolve them. Many G-quadruplex-binding proteins feature an arginine and glycine-rich motif known as the RGG or RG-rich motif. Although this motif plays a crucial role in the recognition of such non-canonical structures, their interaction is still poorly understood. Here, we employed a combination of several biophysical techniques to provide valuable insights into the interaction between a peptide containing an RGG motif shared by numerous human G-quadruplex-binding proteins (NIQI) and various biologically relevant G-quadruplex DNA structures with different topologies. We also shed light on the key amino acids involved in the binding process. Our findings contribute to lay the basis for the development of a new class of peptide-based G-quadruplex ligands as an alternative to small molecules. These ligands may serve as valid tools for interfering in DNA-protein interactions, with potential therapeutic applications.