Substructure Analysis Research Articles

Integration of MALDI glycotyping and NMR analysis to uncover an O-antigen substructure from pathogenic Escherichia coli O111

Escherichia coli O111 is a critical pathogenic E. coli serotype that causes severe, potentially fatal complications. Despite its reported variation, only one structure of the O-antigen polysaccharide from E. coli O111 has been reported. Here, a substructure of the O-antigen from E. coli O111 was characterized using matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry and NMR analysis. MALDI glycotyping revealed differing O-antigen repeating unit masses of Δm/z 787 and 828 in the E. coli strains and lipopolysaccharides from the O111 serogroup. This variation was caused by the replacement of the hexose residue with hexosamine in the repeating units, which was further confirmed by LIFT-TOF/TOF analysis. Structural elucidation of the O111 substructure by NMR analysis further demonstrated replacement of the hydroxyl group with an N-acetyl group on the terminal glucose residue of the O-antigen pentasaccharide repeating unit. To our knowledge, this study is the first to provide a detailed structural analysis of a new O-antigen substructure from the E. coli O111 serogroup.

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits

BackgroundThe hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus’s high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential.MethodsWe analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits.ResultsOur analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10−9 within the 24 HASV traits, located across 93 chromosomal regions. Notably, the regions 12q14.3, 17q21.31, 12q24.22, 6q21, 9q33.1, 6q25.1, and 2q24.2 were found to influence multiple HASVs. Gene set analysis revealed enrichment of neural differentiation and signaling pathways, as well as protein binding and degradation. Of 240 HASV-neuropsychiatric trait pairs, 75 demonstrated significant genetic correlations (P < 0.05/240), revealing 433 pleiotropic loci. Particularly, genes like ACBD4, ARHGAP27, KANSL1, MAPT, ARL17A, and ARL17B were involved in over 50 HASV-neuropsychiatric pairs. Leveraging Mendelian randomization analysis, we further confirmed that atrophy in the left hippocampus, right hippocampus, right hippocampal body, and right CA1-3 region were associated with an increased risk of developing Parkinson’s disease (PD). Furthermore, PRS for all four HASVs were significantly linked to a higher risk of Parkinson’s disease (PD), with the highest hazard ratio (HR) of 1.30 (95% CI 1.18–1.43, P = 6.15 × 10⁻⁸) for right hippocampal volume.ConclusionsThese findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages.

Correction: Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

Correction: Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina.

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cell's receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge for further investigating identified subunits. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within only a few tens of minutes of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

Surrogate-assisted optimization of floating wind turbine substructure

This paper presents a surrogate-assisted optimisation approach to speed up the substructure analysis in the preliminary design phase. The approach consists of replacing the radiation-diffraction analysis in a frequency domain analysis model for floating wind turbines with a data-driven surrogate model predicting the hydrodynamic coefficients for parameterised substructure geometries. This procedure is compared with the reference approach of estimating the hydrodynamic coefficients via radiation-diffraction analysis. A representative use case of assessing the trade-off between minimising the capital cost and reducing the wave-induced nacelle acceleration standard deviation for a semi-submersible substructure is presented. The accuracy of the surrogate model is found to increase significantly up to training datasets consisting of 400 designs and less noticeably afterwards. For a dataset consisting of 400 designs, the mean error on the prediction of the hydrodynamic coefficients and the error at one standard deviation from the mean are generally below 7% and 10%, respectively. For the same dataset size, the mean error on the most probable maximum wave-induced pitch over a 3h storm period is below 17%, while the error at one standard deviation from the mean is lower than 27%. The same values for the most probable maximum nacelle acceleration are under 7% and 12%, respectively. The surrogate model can capture the trade-off between the two objective functions, and the optimal designs identified with the surrogate model generally follow the same trend as those obtained with the reference model. However, relying on the surrogate model for performing the analysis of the substructure introduces local minima in the objective function that cause a discrepancy between the optimal designs identified with the surrogate model and those identified with the reference model.

Distribution of Merging and Post-merger Galaxies in Nearby Galaxy Clusters

We study the incidence and spatial distribution of galaxies that are currently undergoing gravitational merging (M) or that have signs of being post-merger (PM) in six galaxy clusters (A754, A2399, A2670, A3558, A3562, and A3716) within the redshift range of 0.05 ≲ z ≲0.08. To this aim, we obtained Dark Energy Camera mosaics in the u′ , g′ , and r′ bands covering up to 3 × R 200 of the clusters, reaching 28 mag arcsec−2 surface brightness limits. We visually inspect u′ g′ r′ color-composite images of volume-limited (M r < −20) cluster member galaxies to identify whether galaxies are of M or PM type. We find 4% M-type and 7% PM-type galaxies in the galaxy clusters studied. By adding spectroscopic data and studying the projected phase-space diagram (PPSD) of the projected clustocentric radius and the line-of-sight velocity, we find that PM-type galaxies are more virialized than M-type galaxies, having a 1%–5% higher fraction within the escape-velocity region, while the fraction of M-type was ∼10% higher than the PM type in the intermediate environment. Similarly, on a substructure analysis, M types were found in groups in the outskirts, while PM-type populated groups were found in ubiquitous regions of the PPSD. Adopting literature-derived dynamical state indicator values, we observed a higher abundance of M types in dynamically relaxed clusters. This finding suggests that galaxies displaying post-merger features within clusters likely merged in low-velocity environments, including clusters outskirts and dynamically relaxed clusters.

Prediction of pose- and position-dependent tool-tip dynamics in high-speed dry gear hobbing

High-speed dry gear hobbing is an advanced and efficient machining technology for automotive gears. Frequent changes in the hob mounting angle (tilt angle) and shifting position result in variations in the tool-tip frequency response function (FRF), which inevitably modify the hobbing stability boundary. To construct an accurate stability boundary for high-speed dry gear hobbing, the primary objective is to predict the tool-tip FRF related to different combinations of hob tilt angles and shifting positions, that is, pose- and position-dependent tool-tip FRF. This study proposes a novel tool-tip FRF prediction strategy that combines multi-output Gaussian Process regression (MOGPR) and Receptance Coupling Substructure Analysis (RCSA). First, the FRFs at the tips of the hob-mounting base were estimated through a series of impact tests under a limited combination of hob tilt angles and shifting positions, and the corresponding modal parameters were identified. The MOGPR model was then leveraged to reveal the mapping relationships between the modal parameters, hob tilt angles and shifting positions. Based on this, the hob-mounting base pose- and position-dependent FRFs can be obtained using the modal fitting technique. Subsequently, the pose-and position-dependent tool-tip FRFs were obtained by coupling the receptances of the hob-toolbar assembly with the fitted hob-mounting base FRFs through the RCSA. Finally, a series of impact tests were conducted to verify the effectiveness of the proposed method. The prediction of pose- and position-dependent tool-tip dynamics can provide a foundation for accurately modeling hobbing stability.

TESTING AN ENTROPY ESTIMATOR RELATED TO THE DYNAMICAL STATE OF GALAXY CLUSTERS

We propose the entropy estimator HZ, calculated from global dynamical parameters, in an attempt to capture the degree of evolution of galaxy systems. We assume that the observed (spatial and velocity) distributions of member galaxies in these systems evolve over time towards states of higher dynamical relaxation (higher entropy), becoming more random and homogeneous in virial equilibrium. Thus, the HZ-entropy should correspond to the gravitacional assembly state of the systems. This was probed in a sample of 70 well sampled clusters in the Local Universe whose gravitational assembly state, classified from optical and X-ray analysis of substructures, shows clear statistical correlation with HZ. This estimator was also tested on a sample of clusters (halos) from the IllustrisTNG simulations, obtaining results in agreement with the observational ones.

Investigation of a cutting state-independent method for identifying in-process frequency response functions of the micro milling system

Conveniently obtaining the in-process frequency response functions (FRFs) of micro milling tool is a very difficult task due to the small diameter. This article presents a method to identify the in-process FRFs of micro milling tool based on actual cutting tests, which can be at either stable or chatter cutting state. According to mode superposition principle, the in-process FRFs are predicted by integrating the mode shape with the pole, which includes damping ratio and natural frequency. The in-process mode shape of tool is modeled as being consistent with that corresponding to static state, and thus, it is calculated from the tool’s static state by combining receptance coupling substructure analysis (RCSA) with Timoshenko beam theory. The poles of in-process FRFs are acquired by modal analysis of the response signals from actual micro milling tests. To do this, cutting forces are used as excitation signals, and the velocities of tool are selected as response signals and they are measured from actual micro milling tests. With the aid of editing cepstrum and hampel outlier processing methods, a filtering method is established to remove the harmonic and useless chatter components involved in the response signals. As a result, the finally obtained FRFs are not influenced by the noises in the measured response signals, and the effectiveness of this method is verified by a series of modal tests and micro milling tests.

NEURAL NETWORK-BASED ADVERSE DRUG REACTION PREDICTION USING MOLECULAR SUBSTRUCTURE ANALYSES

Objective: This study aims to enhance early detection and prediction by exploiting drug molecular substructures, overcoming challenges posed by limited authentic patient data in the medical domain. Methods: The study implemented a neural network approach to optimize molecular fingerprint algorithms and employed various machine learning algorithms for predictions. Additionally, the study identified and extracted substructures associated with severe Adverse Drug Reactions (ADRs), validating their presence within drug structures through a comparison with a random set of drug structures. Predictions were made for specific molecular structures, and results were validated using clinical evidence from the literature. Results: Optimized molecular fingerprint algorithms and diverse machine-learning models yielded promising outcomes. The Area Under Curve (AUC) value for the fingerprint dataset was obtained at approximately 65%, and integrating it with patient data significantly improved the performance by about 30%. Substructure analysis pinpointed key components linked to severe ADRs, reinforcing the predictive prowess of the model. Predictions for specific molecular structures were corroborated using clinical evidence from the literature, fortifying the credibility of the proposed approach. Conclusion: In conclusion, this research effectively tackles challenges in the early detection and prediction of ADRs by leveraging machine learning algorithms, focusing on drug molecular substructures. The optimized model, incorporating both fingerprint and patient datasets, demonstrated significant improvements in predictive performance. Identifying and validating substructures linked to severe ADRs contribute to the model's reliability. The study's findings are vital for advancing drug safety and laying the groundwork for further strides in predictive modeling within the medical domain.

Chemical Multiverse and Diversity of Food Chemicals.

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Statistical learning in epilepsy: Behavioral and anatomical mechanisms in the human brain.

Statistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe (MTL) in the healthy brain. This leads to the hypothesis that statistical learning (SL) may be impaired in patients with epilepsy (PWE) involving the temporal lobe, and that this impairment could contribute to their varied memory deficits. In turn, studies done in collaboration with PWE, that evaluate the necessity of MTL circuitry through disease and causal perturbations, provide an opportunity to advance basic understanding of SL. We implemented behavioral testing, volumetric analysis of the MTL substructures, and direct electrical brain stimulation to examine SL across a cohort of 61 PWE and 28 healthy controls. We found that behavioral performance in an SL task was negatively associated with seizure frequency irrespective of seizure origin. The volume of hippocampal subfields CA1 and CA2/3 correlated with SL performance, suggesting a more specific role of the hippocampus. Transient direct electrical stimulation of the hippocampus disrupted SL. Furthermore, the relationship between SL and seizure frequency was selective, as behavioral performance in an episodic memory task was not impacted by seizure frequency. Overall, these results suggest that SL may be hippocampally dependent and that the SL task could serve as a clinically useful behavioral assay of seizure frequency that may complement existing approaches such as seizure diaries. Simple and short SL tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.

Design, quality and validation of the EU-OPENSCREEN fragment library poised to a high-throughput screening collection.

The EU-OPENSCREEN (EU-OS) European Research Infrastructure Consortium (ERIC) is a multinational, not-for-profit initiative that integrates high-capacity screening platforms and chemistry groups across Europe to facilitate research in chemical biology and early drug discovery. Over the years, the EU-OS has assembled a high-throughput screening compound collection, the European Chemical Biology Library (ECBL), that contains approximately 100 000 commercially available small molecules and a growing number of thousands of academic compounds crowdsourced through our network of European and non-European chemists. As an extension of the ECBL, here we describe the computational design, quality control and use case screenings of the European Fragment Screening Library (EFSL) composed of 1056 mini and small chemical fragments selected from a substructure analysis of the ECBL. Access to the EFSL is open to researchers from both academia and industry. Using EFSL, eight fragment screening campaigns using different structural and biophysical methods have successfully identified fragment hits in the last two years. As one of the highlighted projects for antibiotics, we describe the screening by Bio-Layer Interferometry (BLI) of the EFSL, the identification of a 35 μM fragment hit targeting the beta-ketoacyl-ACP synthase 2 (FabF), its binding confirmation to the protein by X-ray crystallography (PDB 8PJ0), its subsequent rapid exploration of its surrounding chemical space through hit-picking of ECBL compounds that contain the fragment hit as a core substructure, and the final binding confirmation of two follow-up hits by X-ray crystallography (PDB 8R0I and 8R1V).

Adhesion evaluation of dental ceramics sintered on novel titanium alloys

Metal-ceramics dental prosthetic restorations are clinically recommended for their advantages related to good aesthetic features along with strength and long-term clinical use. Given the importance of the quality of the metal-ceramic (M-C) bond, the metal substructure analysis has a major importance to the quality of the manufactured dental prosthesis. In the present study was analyzed the behavior of experimentally developed novel titanium alloys (TiZr, TiZrNbTa) comparatively with other frequently used titanium alloys (e.g., Ti Cp, Ti6Al4V) designated for the technology of mixed prosthetic restorations. All samples were been plated, simultaneously in the same conditions, with the same titanium specific compatible ceramic plating material (Ti22 Noritake). The examination of the surface of both components was performed by scanning electron microscopy SEM) analysis and EDS analysis, which showed differences in the characteristics of the oxide layer formed, depending on the composition of the metal substructure. The evaluation of the alloy-ceramic adhesion was performed by mechanical tests, which attest to the fact that the adhesion and the quality of the bond between metal and ceramic depend on the thickness of the oxide films formed. The results showed the superiority of titanium novel titanium alloys (TiZr, TiZrNbTa), having highest hardness values, highest values of shear strength, a continuous thin oxide films, and consequently, a better metal-ceramic adhesion. Comparatively, less ductile titanium alloys (TiZrNbTa/Ti3) are generating lower adhesion forces with values above 30MPa in some samples justifying less predictable clinical results.

Sub-Structure Characteristic Mode Analysis of Microstrip Antennas Using a Global Multitrace Formulation

Sub-Structure Characteristic Mode Analysis of Microstrip Antennas Using a Global Multitrace Formulation

Identification and Dosimetric Analysis of Axillary Substructures Associated with Breast Cancer Related Lymphedema

It has been hypothesized that substructures in the axillary region may be involved in the development of upper limb lymphedema in breast cancer patients. We aimed to compare dosimetric parameters of different substructures to identify risk regions and dose indicators related to the development of lymphedema. A total of 486 patients with breast cancer treated with axillary node dissection (ALND) and regional node irradiation (RNI) between 2017 and 2020 were analyzed. Ten axillary substructures were retrospectively contoured, and dose-volume histogram (DVH) parameters (maximum dose [Dmax], Dmean, Dmin, V5 Gy, V10 Gy, V15 Gy, V20 Gy, V25 Gy, V30 Gy, V35 Gy, V40 Gy, V45 Gy and V50 Gy) were evaluated. EQD2 using α/β = 3 was applied for hypofractionation. The cumulative incidence of lymphedema was 32.5% during the median follow-up of 25 month (IQR 15-35). Higher BMI (≥21.77 kg/m2, hazard ratio [HR]1.6, 95% CI 1.1-2.3, p = 0.019) and higher number of dissected lymph nodes (≥13, HR 1.8, 95% CI 1.1-3.0, p = 0.026) were baseline risk factors associated with breast cancer related lymphedema (BCRL). A cohort of 196 patients was obtained by 1:1 propensity score matching based on the above two factors (154 treated with 50 Gy/25 fractions [Fx] and 42 with 40.05 Gy/15Fx). The optimal metric was axillary-lateral thoracic vessel juncture (ALTJ) Dmean ≥36.84 Gy (HR 3.7, 95% CI 1.6-8.6, p = 0.002). Other significant risk factors are Medial of Axillary Nodes level I(ALN-I-M) Dmin <9.19 Gy (HR 2.3, 95% CI 1.4-3.7, p = 0.001) and Posterior of Axillary Nodes level II(ALN-II-P) Dmax <52.75 Gy (HR 2.0, 95% CI 1.2-3.5, p = 0.015). A nomogram incorporated the above three parameters was created to predict the risk of lymphedema with reasonable accuracy confirmed by both self-training (area under curve [AUC], 0.76; 95% CI, 0.67-0.84) and internal validation (AUC 0.66; 95% CI, 0.50-0.80). A mean dose of 36.84 Gy of ALTJ may be a suggested dose limit for patients indicated RNI after ALND, especially when patients with higher BMI and higher dissected nodes. Ideally, avoidance of ALTJ may be realized without compromising dose coverage to high-risk nodal region. Confirmation of this finding in future prospective studies is needed.

Potato Wart Isolates from Europe and North America Form Distinct Clusters of Genetic Variation.

We have extended previously published sets of simple sequence repeat markers for Synchytrium endobioticum, selected to be polymorphic for the German-standard isolates of pathotypes P1, P2, P6, P8, and P18. These markers also complement the extensive published information on DNA polymorphisms for the mitogenomes of Synchytrium endobioticum. This extended set of 35 markers representing 73 alleles differentiated 51 isolates from Europe and North America into three large, well-separated clusters and subclusters using dendrogram analysis, principal coordinates analysis (PCoA), and population substructure analysis using STRUCTURE 2.3.4 software. This suggests a limited number of introgressions of the wart disease pathogen into current potato growing areas, followed by recombination and admixture of populations through human activities. The new markers extend the published marker sets and are useful tools for future analyses of population structure and dynamics in Synchytrium endobioticum, which are necessary to understand the biology of the interaction between the pathogen and its potato host and to develop future control strategies.

A state-of-the-art review of progressive collapse research and guidelines for single-layer lattice shell structures

The importance of progressive collapse analysis of structures has increased significantly due to various structural collapses. Single-layer lattice shell structures are susceptible to progressive collapse, as the failure or instability of a localised region can result in a global collapse due to their lightness and wide span. The geometry of these structures plays a crucial role in the load resistance, and any damage that alters the overall geometry can initiate different instabilities, which may also result in progressive collapse. Therefore, the possibility of progressive collapse is high among these lighter structural forms. However, research on the progressive collapse of single-layer lattice shell structures is limited compared to framed structures, highlighting the need for further investigation. Therefore, this manuscript aims to provide a comprehensive review of research conducted on the progressive collapse of single-layer lattice shell structures, focusing on experimental, numerical, and theoretical investigations. Earlier studies have explored the propagation of local instabilities leading to overall failure. More recent research has primarily focused on two approaches: (1) alternate path analysis, which examines the structure’s response after removing a member, and (2) substructure analysis, which aims to identify collapse-preventive mechanisms. Based on these analyses, various methods to enhance progressive collapse resistance have been discussed, including specific local resistance, alternate load paths, compartmentalisation, and tie forces. This review also highlights the limitations present in the current literature and suggests future research directions, which helps to develop design guidelines that can effectively increase the progressive collapse resistance of single-layer lattice shell structures, making them more reliable for various structural applications.

A nonlinear substructure method based on a computational plasticity framework for efficient analysis of structural assemblies

Substructure analysis reduces the computational order of a discretized structural domain from the full set of degrees of freedom needed to solve a boundary value problem (e.g., the displacements of all nodes in an FEA mesh) to a predefined and much smaller set of retained degrees of freedom. Given only one initial analysis considering all degrees of freedom, this technique reduces the computational cost associated with subsequent analyses of the same domain by eliminating degrees of freedom, usually internal to the domain, which are not essential for interfacing the domain with a larger system/assembly. For large multiscale applications, such as aircraft and automotive assemblies, substructure analysis enables efficient computation of both static and dynamic responses of larger assemblies consisting of one or more of these previously analyzed and dimensionally reduced domains. However, while existing methods are exact for linear problems (e.g., small-deformation linear elasticity), they are insufficient when considering large deformations or material nonlinearities. In this work, we develop a new nonlinear substructure method to consider general nonlinear responses by leveraging the mathematical framework developed for computational plasticity, including a decomposition of deformations, criteria for nonlinearity initiation, and evolution equations. While computational plasticity provides nonlinear constitutive relationships between six independent stress and strain components, we show that the same mathematical formulation can capture similar relations between an arbitrary number of forces and displacements (i.e., the retained degrees of freedom), and thus, can be implemented with the same numerical solution algorithms. As a notional example to emphasize both the generality of the aforementioned method and the application to design frameworks, we model an infilled lattice structure exhibiting local plasticity and internal nonlinear geometric effects.