Recognition Of Structures Research Articles

The crystalline quartz-rich raw material from Olduvai Gorge (Tanzania): why is it called quartzite when it should be called quartz?

The major raw material documented in the archeological sites of Olduvai Gorge (Tanzania) is a geological material with crystalline appearance, white or colorless, foliated or seemingly massive only at the outcrop scale, with a very high quartz-rich composition, and apparently of metamorphic origin, named by us in this paper: Crystalline Quartz-rich Raw Material (CQRM). Since the early days of research in Olduvai Gorge, a long-lasting terminological imprecision has allowed defining this material in a confused way as quartz or quartzite. Stubbornness in terminological imprecision reflects the complexity and specificity of CQRM related to a protracted and complex geological history composed by quartz-bearing metamorphic rocks of varied types and origins from recycling and/or tectonic reworking of much older Precambrian orogens and cratons. Currently the term quartzite is preferred by most researchers, despite being materials that have an appearance macro- and microscopic similar to quartz and show a response to fracture mechanics, and cutting-edge functional response is closer to quartz. In our view, it is crucial to undertake a comprehensive analysis of the CQRM from the structural, metamorphic, and petrological perspectives. Bearing this in mind, the main objective of the present study is to build a robust and conclusive petrological background that will enable an accurate identification and classification of this quartz-rich mineral resource. This geological material should be identified as “quartz.” The most diagnostic features supporting this interpretation can be summarized as some of the microstructural relics identified concur undoubtedly with a hydrothermal origin of the quartz and the recognition of special deformational structures at macro and micro scale point to tectono-metamorphic overprint of the hydrothermal quartz under granulite-facies conditions during the Panafrican orogenesis about 640 Ma ago.

Domain adaptive learning for document layout analysis and object detection using classifier alignment mechanism

Document structure analysis and object detection is the new research interest, which involves differentiation between distinctive semantic regions of textual and non-textual objects. Document layout analysis remains a major challenge as document elements pose diversified layout structures, shapes, and appearances. Lack of annotated training datasets and domain shifts between datasets further increases the intricacy. The paper proposes an adaptive detection model for cross-domain learning ‘XDOD’ for document structure recognition and object detection. The detection model overcomes the domain shift between documents using, (1) Document Object Attention (DA) module, which learns coarse-to-refined features, and (2) Classifier Alignment (CA) module, which reduces the object misclassification. The XDOD model has been evaluated on various publicly available document datasets belonging to different domains. Extensive experimental results portray that the proposed XDOD model performs significantly better than the existing benchmark model giving more than 97% of detection rate on all datasets.

A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Machine learning analysis predicts a person’s sex based on mechanical but not thermal pain thresholds

Sex differences in pain perception have been extensively studied, but precision medicine applications such as sex-specific pain pharmacology have barely progressed beyond proof-of-concept. A data set of pain thresholds to mechanical (blunt and punctate pressure) and thermal (heat and cold) stimuli applied to non-sensitized and sensitized (capsaicin, menthol) forearm skin of 69 male and 56 female healthy volunteers was analyzed for data structures contingent with the prior sex structure using unsupervised and supervised approaches. A working hypothesis that the relevance of sex differences could be approached via reversibility of the association, i.e., sex should be identifiable from pain thresholds, was verified with trained machine learning algorithms that could infer a person's sex in a 20% validation sample not seen to the algorithms during training, with balanced accuracy of up to 79%. This was only possible with thresholds for mechanical stimuli, but not for thermal stimuli or sensitization responses, which were not sufficient to train an algorithm that could assign sex better than by guessing or when trained with nonsense (permuted) information. This enabled the translation to the molecular level of nociceptive targets that convert mechanical but not thermal information into signals interpreted as pain, which could eventually be used for pharmacological precision medicine approaches to pain. By exploiting a key feature of machine learning, which allows for the recognition of data structures and the reduction of information to the minimum relevant, experimental human pain data could be characterized in a way that incorporates "non" logic that could be translated directly to the molecular pharmacological level, pointing toward sex-specific precision medicine for pain.

Structural characterisation of hemagglutinin from seven Influenza A H1N1 strains reveal diversity in the C05 antibody recognition site

Influenza virus (IV) causes several outbreaks of the flu each year resulting in an economic burden to the healthcare system in the billions of dollars. Several influenza pandemics have occurred during the last century and estimated to have caused 100 million deaths. There are four genera of IV, A (IVA), B (IVB), C (IVC), and D (IVD), with IVA being the most virulent to the human population. Hemagglutinin (HA) is an IVA surface protein that allows the virus to attach to host cell receptors and enter the cell. Here we have characterised the high-resolution structures of seven IVA HAs, with one in complex with the anti-influenza head-binding antibody C05. Our analysis revealed conserved receptor binding residues in all structures, as seen in previously characterised IV HAs. Amino acid conservation is more prevalent on the stalk than the receptor binding domain (RBD; also called the head domain), allowing the virus to escape from antibodies targeting the RBD. The equivalent site of C05 antibody binding to A/Denver/57 HA appears hypervariable in the other H1N1 IV HAs. Modifications within this region appear to disrupt binding of the C05 antibody, as these HAs no longer bind the C05 antibody by analytical SEC. Our study brings new insights into the structural and functional recognition of IV HA proteins and can contribute to further development of anti-influenza vaccines.

Investigation of chemical structure recognition by encoder–decoder models in learning progress

Descriptor generation methods using latent representations of encoder–decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input–output substructure similarity using substructure-based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time-consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals.Graphical

Pre-polymerization process simulation, synthesis and investigation the properties of dipicolinic acid molecularly imprinted polymers.

Molecularly imprinted polymers (MIPs) have attracted much attention in recent years due to their structure predictability, recognition specificity, and universal application, as well as robustness, simplicity, and cheapness. In this study, firstly, the pre-polymerization process of molecularly imprinted polymer of dipicolinic acid (DPA) was simulated by molecular dynamics. Then, the appropriate functional monomer molecule for printing was selected and its intermolecular bond with the DPA molecule was evaluated. The monomers 2-vinyl pyridine, acrylic acid (AA), and methacrylic acid (MAA) were selected with potential energies of 3.93kcal/mol, 3.15kcal/mol, and 2.78kcal/mol, respectively. Finally, the ability of functional groups to form hydrogen bonds was estimated, and molecularly imprinted polymers (MIPs) and non-imprinted polymers (NIPs) were synthesized by bulk polymerization. MAA and AA were used as functional monomers to identify DPA molecules. The morphology of MIP and NIP was investigated using a scanning electron microscope (SEM). Their performance was evaluated in the absorption of DPA molecules and picolinic acid (PA) molecules and the printing factor of synthesis polymers. The results showed that fabricated MIPs can be used in the structure of sensors, and the synthesis process is a key factor that significantly affects the polymer properties. The MIP based on the AA monomer showed a higher adsorption rate/capacity and maximum printing factor than MAA monomer-based MIP.

π‐π Catalysis Made Asymmetric—Enantiomerization Catalysis Mediated by the Chiral π‐System of a Perylene Bisimide Cyclophane

AbstractEnzymes actuate catalysis through a combination of transition state stabilization and ground state destabilization, inducing enantioselectivity through chiral binding sites. Here, we present a supramolecular model system which employs these basic principles to catalyze the enantiomerization of [5]helicene. Catalysis is hereby mediated not through a network of functional groups but through π‐π catalysis exerted from the curved aromatic framework of a chiral perylene bisimide (PBI) cyclophane offering a binding pocket that is intricately complementary with the enantiomerization transition structure. Although transition state stabilization originates simply from dispersion and electrostatic interactions, enantiomerization kinetics are accelerated by a factor of ca. 700 at 295 K. Comparison with the meso‐congener of the catalytically active cyclophane shows that upon configurational inversion in only one PBI moiety the catalytic effect is lost, highlighting the importance of precise transition structure recognition in supramolecular enzyme mimics.

π-π Catalysis Made Asymmetric-Enantiomerization Catalysis Mediated by the Chiral π-System of a Perylene Bisimide Cyclophane.

Enzymes actuate catalysis through a combination of transition state stabilization and ground state destabilization, inducing enantioselectivity through chiral binding sites. Here, we present a supramolecular model system which employs these basic principles to catalyze the enantiomerization of [5]helicene. Catalysis is hereby mediated not through a network of functional groups but through π-π catalysis exerted from the curved aromatic framework of a chiral perylene bisimide (PBI) cyclophane offering a binding pocket that is intricately complementary with the enantiomerization transition structure. Although transition state stabilization originates simply from dispersion and electrostatic interactions, enantiomerization kinetics are accelerated by a factor of ca. 700 at 295 K. Comparison with the meso-congener of the catalytically active cyclophane shows that upon configurational inversion in only one PBI moiety the catalytic effect is lost, highlighting the importance of precise transition structure recognition in supramolecular enzyme mimics.

Rapid, simple, and effective strategy to produce monoclonal antibodies targeting protein structures using hybridoma technology

BackgroundMonoclonal antibodies are essential in life science research and developing antibody drugs and test drugs. Various methods have been developed to obtain monoclonal antibodies, among which hybridoma technology continues to be widely used. However, developing a rapid and efficient method for obtaining conformation-specific antibodies using hybridoma technology remains challenging. We previously developed the membrane-type immunoglobulin-directed hybridoma screening (MIHS) method, which is a flow cytometry-based screening technique based on the interaction between the B-cell receptor expressed on the hybridoma cell surface and the antigen protein, to obtain conformation-specific antibodies.ResultsIn this study, we proposed a streptavidin-anchored ELISA screening technology (SAST) as a secondary screening method that retains the advantages of the MIHS method. Anti-enhanced green fluorescent protein monoclonal antibodies were generated as a model experiment, and their structural recognition abilities were examined. Examination of the reaction profiles showed that all monoclonal antibodies obtained in this study recognize the conformational epitopes of the protein antigen. Furthermore, these monoclonal antibodies were classified into two groups: those with binding activities against partially denatured proteins and those with complete loss of binding activities. Next, when screening monoclonal antibodies by the MIHS method as the first screening, we found that monoclonal antibodies with stronger binding constants may be selected by double-staining for hybridomas with fluorescently labeled target antigens and fluorescently labeled B cell receptor antibodies.ConclusionsThe proposed two-step screening method, which incorporates MIHS and SAST, constitutes a rapid, simple, and effective strategy to obtain conformation-specific monoclonal antibodies generated through hybridoma technology. The novel monoclonal antibody screening strategy reported herein could accelerate the development of antibody drugs and antibody tests.

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling (MWD) technology is developed, using the binary pattern of structural rock characteristics obtained from in-hole images for calibration. Data from two underground operations with different drilling technology and different rock mass characteristics are considered, which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis. Two approaches are followed for site-specific structural model building: a discontinuity index (DI) built from variations in MWD parameters, and a machine learning (ML) classifier as function of the drilling parameters and their variability. The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs. Differences between the parameters involved in the models for each site, and differences in their weights, highlight the site-dependence of the resulting models. The ML approach offers better performance than the classical DI, with recognition rates in the range 89% to 96%. However, the simpler DI still yields fairly accurate results, with recognition rates 70% to 90%. These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

Dehydroxylation of smectites from Nudged Elastic Band and Born-Oppenheimer Molecular Dynamics simulations

High–temperature treatment of smectite minerals leads to the dehydroxylation process. One of the consequences of this process is the loss of sorption ability, which is an interesting, but not fully understood, phenomenon. A combination of static and dynamic quantum–chemical methods was used to analyze structural changes associated with the dehydroxylation of smectite layers. The work aims in the determination of multi–path reaction mechanism. The Nudged Elastic Band (NEB) method allowed for the recognition of intermediate structures, transition states and activation energies in the studied reaction mechanisms. Born–Oppenheimer Molecular Dynamics (BOMD) was used to investigate the interatomic distances time–evolution of atoms involved in the dehydroxylation process at different temperatures. The simulations were carried out in the crystalline phase, for which the computational model was prepared based on the dioctahedral structure of smectites with the chemical formula: Ca2+Al4Si8O22(OH)2. It was found, that the process requires a proton transfer to the oxygen atom of the hydroxyl group located in the octahedral sheet, which results in the release of water molecule from the layer. In addition, it has been shown that the process of a water molecule formation on the surface is also possible. The process occurring inside the layer is more energetically favoured than that on the surface. However, high activation energies were obtained in both cases, what proves that the dehydroxylation reaction takes place only at a relatively high temperatures. It has also been demonstrated that this process can follow several different mechanisms.

MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation.

Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and bonds, along with their geometric layouts, to construct the molecular structure. Our model flexibly incorporates symbolic chemistry constraints to recognize chirality and expand abbreviated structures. We further develop data augmentation strategies to enhance the model robustness against domain shifts. In experiments on both synthetic and realistic molecular images, MolScribe significantly outperforms previous models, achieving 76-93% accuracy on public benchmarks. Chemists can also easily verify MolScribe's prediction, informed by its confidence estimation and atom-level alignment with the input image. MolScribe is publicly available through Python and web interfaces: https://github.com/thomas0809/MolScribe.

NETosis and Calcium influx in Dromedary Camel Neutrophils after in vitro Toll-like Receptor Stimulation

Neutrophilic granulocytes are vital immune cells of the early response to pathogens. They contribute to the antimicrobial response through phagocytosis, production of reactive oxygen species, cytokine production, degranulation, and NET-formation. Neutrophil extracellular traps (NETs), also known as NETosis, are a critical antibacterial effector mechanism of cells of myeloid effector cells, including neutrophils and macrophages. Toll-like receptors (TLRs) are pattern recognition receptors (PRRs) that mediate pathogen sensing through the recognition of microbial structures known as pathogen-associated molecular patterns (PAMPs). The present study aimed to investigate the potential of several TLR ligands that mimic the sensing of bacterial and viral pathogens to stimulate NET-formation or Ca2+ influx in camel neutrophils. Neutrophils were purified from blood and were stimulated in vitro with ligands to TLR4, TLR2/1, TLR7/8, or TLR3. Net-formation was analyzed using the DNA-sensitive dye SYTOX™ Green and staining with antibodies to the neutrophil's granular enzyme myeloperoxidase. Real-time stimulation-induced Ca2+ influx was measured using the Ca2+-sensitive dye Flou-4 and flow cytometry. Only the TLR4-ligand lipopolysaccharide (LPS) could induce NET-formation in camel neutrophils, while none of the investigated TLR agonists showed a Ca2+ influx-inducing effect in camel neutrophils. The current study represents the first report on the impact of direct activation of TLR on NET-formation and Ca2+ influx in camel neutrophils with a selective effect of LPS on NET-formation induction. Future studies may investigate the molecular mechanisms behind the different responsiveness of bovine and camel neutrophils to TLR stimulation.

The Performance of Students in the Practical Evaluation of Muscle Recognition in Natural and Artificial Anatomical Parts

Learning human anatomy can be challenging for Physical Education and Medicine students. Large volume of information, technical terminology, difficulty in visualizing 3D structures, lack of practice and emotional pressure are some challenges faced by students. However, the study of muscle anatomy is as important for physicians as it is for physical educators, albeit for different main reasons. In this study, the objective was to verify if there are differences in the performance of medical and physical education students in practical tests of recognition of muscular structures in natural and artificial anatomical parts. From approximately 126 muscles of the upper and lower limbs and their girdles, 20 structures were drawn (10 in natural pieces and 10 in artificial pieces). The notes and class notebook records of 86 physical education students and 103 medical students from two public universities were compared, considering that there were no differences between the evaluation methods and the professors in charge. It was found that, despite substantial differences in the socioeconomic profile of each course, physical education students were significantly better at recognizing muscle structures. However, this result was dependent on the better performance of physical education students in the evaluation of artificial parts. The students' performance was lower, much more variable and similar between the groups in the evaluation of the natural pieces. Such results suggest the need for greater contact and manipulation, especially of medicine, in cadaver pieces.

New Xanthone Derivatives as Potent G-Quadruplex Binders for Developing Anti-Cancer Therapeutics.

Xanthone is an important scaffold for various medicinally relevant compounds. However, it has received scant attention in the design of agents that are cytotoxic to cancer cells via targeting the stabilization of G-quadruplex (G4) nucleic acids. Specific G4 DNA recognition against double-stranded (ds) DNA is receiving epoch-making interest for the development of G4-mediated anticancer agents. Toward this goal, we have synthesized xanthone-based derivatives with various functionalized side-arm substituents that exhibited significant selectivity for G4 DNA as compared to dsDNA. The specific interaction has been demonstrated by performing various biophysical experiments. Based on the computational study as well as the competitive ligand binding assay, it is inferred that the potent compounds exhibit an end-stacking mode of binding with G4 DNA. Additionally, compound-induced conformational changes in the flanking nucleotides form the binding pocket for effective interaction. Selective action of the compounds on cancer cells suggests their effectiveness as potent anti-cancer agents. This study promotes the importance of structure-based screening approaches to get molecular insights for new scaffolds toward desired specific recognition of non-canonical G4 DNA structures.

The southern IOCG and hydrothermal nickel mineralization trend of the Carajás Mineral Province: Airborne geophysical and remote sensing evidences for structural controls and hydrothermal signature

The iron oxide‑gold‑copper deposit (IOCG) and hydrothermal nickel deposit are globally important targets for mineral exploration due to their notable role in the clean energy transition. The Carajás Mineral Province stands out for having the highest known concentration of world-class IOCG deposits, especially within the regional Canaã shear system, which exerts a critical control in polymetallic deposit distribution. The dense regolith cover and rainforest vegetation hinder the structural mapping and recognition of host rocks and metasomatic facies. In this context, we integrated remote sensing and geological data to evaluate the influence of the structural framework on the IOCG and hydrothermal nickel mineralization location and the metasomatic footprints that envelope these mineral deposits. These deposits are spatially correlated to anomalies that have values greater than the average and standard deviation (⩾ x̄ + sx) of the gamma-spectrometry and geomorphological features (K/eTh, eU/eTh, F-parameter, K and eU deviations, and morpholineament density). The regional control is exerted by NW–SE- and N–S-trending structures, whereas NE–SW- and E–W-oriented shallow structures present the most significant mineralization control at the local scale. Structure intersections express the Carajás deep to the shallow crustal architecture of the region and the potential pathways for the hydrothermal and ore-forming fluids in multiple metallogenetic events.

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

ObjectiveTo develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning.BackgroundRAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking.MethodsEighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy.ResultsThe median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively.ConclusionThis study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.

Novel merocyanine-derived receptor: Synthesis, crystal structure and picric acid recognition, spectroscopic and theoretical study

A novel bis(benzo[d]thiazol-2-yl)methane functionalized merocyanine chromophore (MC-BTH-2) was synthesized and characterized using modern spectroscopic techniques. Structure of MC-BTH-2 was established by means of single crystal-structure techniques. MC-BTH-2 showed turn-off fluorescence response towards picric acid (PA) (A11) among the examined nitroaromatics (NACs) i.e. picric acid with a limit of detection of about 7.287 × 10−8 M. Moreover, MC-BTH-2 exhibited colorimetric response from orange to red by naked-eye after interaction with picric acid. The complexation of MC-BTH-2 with picric acid was investigated by means of theoretical study.

Biochemical and biophysical characterization of the nucleic acid binding properties of the RNA/DNA binding protein EWS.

EWS is a member of the FET family of RNA/DNA binding proteins that regulate crucial phases of nucleic acid metabolism. EWS comprises an N-terminal low-complexity domain (LCD) and a C-terminal RNA-binding domain (RBD). The RBD is further divided into three RG-rich regions, which flank an RNA-recognition motif (RRM) and a zinc finger (ZnF) domain. Recently, EWS was shown to regulate R-loops in Ewing sarcoma, a pediatric bone and soft-tissue cancer in which a chromosomal translocation fuses the N-terminal LCD of EWS to the C-terminal DNA binding domain of the transcription factor FLI1. Though EWS was shown to directly bind R-loops, the binding mechanism was not elucidated. In the current study, the RBD of EWS was divided into several constructs, which were subsequently assayed for binding to various nucleic acid structures expected to form at R-loops, including RNA stem-loops, DNA G-quadruplexes, and RNA:DNA hybrids. EWS interacted with all three nucleic acid structures with varying affinities and multiple domains contributed to binding each substrate. The RRM and RG2 region appear to bind nucleic acids promiscuously while the ZnF displayed more selectivity for single-stranded structures. With these results, the structural underpinnings of EWS recognition and binding of R-loops and other nucleic acid structures is better understood.