Correlation Structure Research Articles

Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.

To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spectralis). DL-based algorithms quantified ellipsoid zone (EZ)-thickness, hyperreflective foci (HRF) and drusen volume. Outer nuclear layer (ONL)-thickness and subretinal drusenoid deposits (SDD) were quantified by human experts. All patients completed four MP examinations using an identical custom 45 stimuli grid on MP-3 (NIDEK) and MAIA (CenterVue). MP stimuli were co-registered with corresponding OCT using image registration algorithms. Multivariable mixed-effect models were calculated. 3.600 PWS from 20 eyes of 20 patients were analyzed. Decreased EZthickness, decreased ONLthickness, increased HRF and increased drusen volume had a significant negative effect on PWS (all p < 0.001) with significant interaction with eccentricity (p < 0.001). Mean PWS was 26.25 ± 3.43dB on MP3 and 22.63 ± 3.69dB on MAIA. Univariate analyses revealed a negative association of PWS and SDD (p < 0.001). Subclinical changes in EZintegrity, HRF and drusen volume are quantifiable structural biomarkers associated with reduced retinal function. Topographic co-registration between structure on OCT volumes and sensitivity in MP broadens the understanding of pathognomonic biomarkers with potential for evaluation of quantifiable functional endpoints.

Heschl's gyrus and the temporal pole: The cortical lateralization of language.

The left lateralization of language has been attributed to hemispheric specialization for processing rapidly changing information. While interhemispheric differences in auditory cortex organization support this view, the macrostructure of the entire cerebral cortex has not been thoroughly examined from this perspective. This study investigated hemispheric asymmetries in cortical surface area and thickness and their relationship to pronunciation scores from oral reading using the Human Connectome Project Young Adult dataset (N=1113). Heschl's gyrus had the most left-lateralized surface area, while the temporal pole showed the strongest right-lateralization in thickness. These areas correspond to the core components of speech: sound and meaning. Notably, their structural features were the only ones also yielding a significant correlation with pronunciation scores. Additionally, Broca's area's posterior region (pars opercularis), involved in articulatory phonological processing, showed leftward lateralization, contrasting with the right-lateralized anterior portions. Left-hemisphere language areas were largely thinner and more extended than their right-sided homologs with a larger white-to-gray matter ratio. Cortical thickness was inversely related to surface area. The lateralization of auditory-related language areas and their structure's correlation with pronunciation in oral reading supports a genetically based auditory foundation for language. A thinner, more efficient cortex with larger surface areas and increased myelination likely underlies the left-hemispheric dominance of language. Thinner, more extended brain areas have been linked to more myelination and wider cortical columns and intercolumnar space. This provides the potential for a fast network of interconnected, discrete information units able to support language's demands of rapid categorical processing.

Radially polarized partially coherent beams with prescribed sinh-Gauss non-uniform correlation structure

Radially polarized partially coherent beams with prescribed sinh-Gauss non-uniform correlation structure

Scaling Correlation Analysis of Particulate Matter Concentrations of Three South Indian Cities

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques.

The Impact of NO2 on Epithelial Barrier Integrity of a Primary Cell-Based Air-Liquid Interface Model of the Nasal Respiratory Epithelium.

Nitrogen dioxide (NO2) is a pervasive gaseous air pollutant with well-documented hazardous effects on health, necessitating precise toxicological characterization. While prior research has primarily focused on lower airway structures, the upper airways, serving as the first line of defense against airborne substances, remain understudied. This study aimed to investigate the functional effects of NO2 exposure alone or in combination with hypoxia as a secondary stimulus on nasal epithelium and elucidate its molecular mechanisms because hypoxia is considered a pathophysiological factor in the onset and persistence of chronic rhinosinusitis, a disease of the upper airways. Air-liquid interface cell cultures derived from primary nasal mucosa cells were utilized as an invitro model, offering a high invitro-in vivo correlation. Our findings demonstrate that NO2 exposure induces malfunction of the epithelial barrier, as evidenced by decreased transepithelial electrical resistance and increased fluorescein isothiocyanate (FITC)-dextran permeability. mRNA expression analysis revealed a significant increase in IL-6 and IL-8 expressions following NO2. Reduced mRNA expression of the tight junction component occludin was identified as a structural correlate of the damaged epithelial barrier. Notably, hypoxic conditions alone did not alter epithelial barrier integrity. These findings provide information on the harmful effects of NO2 exposure on the human nasal epithelium, including compromised barrier integrity and induction of inflammatory responses. Overall, this study contributes to our understanding of pathophysiological mechanisms underlying also upper airway respiratory diseases associated with air pollution exposure and emphasizes the importance of mitigating NO2 emissions to safeguard respiratory health.

Understanding complex systems through differential causal networks

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients’ sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks.

Psychometric Properties of Social Climate in the Classroom Scale for University Students in Chile

Background: Classroom social climate is a significant phenomenon within educational contexts; however, it has predominantly been studied among high school students, with limited evidence available at the university level and, consequently, in the psychometric properties of measurement instruments. This study aimed to analyze the psychometric properties of the University Classroom Social Climate Scale (ECSA-U) among Chilean students from the La Araucanía region. Method: 422 students participated, responding to the adapted version of the ECSA-U and the Motivation subscale of the Motivation and Learning Strategies Questionnaire. Exploratory structural equation modeling (ESEM), reliability analyses, and correlation analyses were conducted to provide valid evidence for the Chilean University Classroom Social Climate Scale (ECSA-UCL). Results: The scale demonstrated a three-factor structure with good fit indicators, excellent reliability indices, and significant positive associations between the ECSA-UCL and the Motivation Subscale. Conclusions: The ECSA-UCL has proven valid and reliable for measuring the perception of Classroom Social Climate among Chilean university students, making it suitable for use as a measurement tool in studies or interventions that include this variable.

A re-sampling statistics based imprecise moment independent global sensitivity analysis methodology with limited data of uncorrelated and correlated geotechnical properties

Traditional Global Sensitivity Analysis (GSA) is often performed using variance-based Sobol’s GSA, considering statistics of inputs estimated from limited data to be precise. This can lead to inaccuracies in the importance ranking of inputs due to negligence of – i) epistemic uncertainties in their statistics, and ii) role of other moments of outputs in Sensitivity Indices (SIs). This study overcomes these limitations by proposing an imprecise moment independent GSA methodology. The methodology couples re-sampling statistical techniques, i.e., Jackknifing and Bootstrapping with Monte-Carlo Simulations based Borgonovo’s moment independent GSA to estimate distributions of SIs. The re-sampling techniques estimate epistemic uncertainties in the statistics of inputs by generating re-constituted samples. These epistemic uncertainties are then propagated via Borgonovo’s GSA to estimate their imprecise moment independent SIs. The methodology is further coupled with Nataf’s transformation and Moving Least Square-Response Surface Methods to deal with problems of correlated inputs and lack of explicit input-output relationships. The generalized nature of methodology is demonstrated for three real case studies (one tunnel and two slopes), with different correlation structures between inputs, input-outputs relationships (explicit/implicit), and failure mechanisms. The methodology estimated the distributions of SIs of inputs instead of their point estimates, indicating the propagation of epistemic uncertainties truthfully. The Jackknife-GSA was significantly efficient (∼99 %) with negligible differences in statistics of SIs as compared to Bootstrap-GSA. The correlation between inputs significantly affects the statistics of SIs. Further, a reduction in the imprecision of SIs of inputs with their increasing sample sizes was observed, which is consistent with the general understanding.

Correlation of structure and luminescence in Ca₃La₂W₂O₁₂: Eu³⁺ phosphors for advanced display technologies validated using Judd-Ofelt theory

Correlation of structure and luminescence in Ca₃La₂W₂O₁₂: Eu³⁺ phosphors for advanced display technologies validated using Judd-Ofelt theory

Characterization of anti-EBNA-1 antibodies and exploration of their molecular mimicry potential in an EBV-infected Sjögren's syndrome patient

There is a potential link between autoimmune diseases and Epstein-Barr virus (EBV) infections, with EBV playing a substantial role in the onset of Sjögren's syndrome (SjS). Some EBV proteins could mimic host self-antigens post-infection, leading to molecular mimicry. This similarity may cause the immune system to attack its tissues mistakenly. Among the various proteins associated with EBV, nuclear antigen 1 (EBNA-1) is essential for the latent replication of infected cells and is prevalent in all EBV-related diseases. In the study, single-chain variable fragment (scFv) antibodies targeting EBNA-1 were isolated using phage display technology from a primary SjS patient who also had a chronic active EBV infection. The specific clones were enriched after panning, and the binding activity of selected scFvs targeting EBNA-1 was confirmed. Sequence analysis indicated that the scFvs exhibiting positive signals could be grouped into five clones, all of which used homologous heavy chain V regions derived from germline Vh4-39, and two types of light chain V regions stemming from germline Vλ1-44 and Vλ3-15. These scFvs were found to exhibit a high degree of somatic mutations, likely indicative of antigen selection. Of the scFvs, P1-3 demonstrated the strongest binding affinity to EBNA-1, exhibiting a determined value of 7.3 x 10-8 M, and showed cross-reactivity to the SjS associated La/SSB self-antigen. The experimental results combined with AlphaFold 3 predictions revealed a potential epitope for scFv P1-3 binding to EBNA-1. Additionally, scFv P1-3 could also cross-bind to the modeled structure of La/SSB. We inferred a possible structural correlation between EBNA-1 and La/SSB involving an X2AX6PG epitope motif. This research contributes to our understanding of the structural basis of the interactions between antibodies and EBNA-1, shedding light on the VH and VL gene usage of anti-EBNA-1 antibodies in EBV-infected SjS patients and the potential origins of autoantibodies.

Na4+x[Sn1-xYxSi3.8P0.2O12]glass-ceramic electrolyte: Structure correlation with Interfacial resistance and Electrochemical Performance

Na4+x[Sn1-xYxSi3.8P0.2O12]glass-ceramic electrolyte: Structure correlation with Interfacial resistance and Electrochemical Performance

SecKG2vec: A Novel Security Knowledge Graph Relational Reasoning Method based on Semantic and Structural Fusion Embedding

Knowledge graph technology is widely used in network security design, analysis, and detection. By collecting, organizing, and mining various security knowledge, it provides scientific support for security decisions. Some public Security Knowledge Repositories (SKRs) are frequently used to construct security knowledge graphs. The quality of SKRs affects the efficiency and effectiveness of security analysis. However, the current situation is that the identification of relational information among security knowledge elements is not sufficient and timely, and a large number of key relational information is missing. In view of this, we propose a security knowledge graph relational reasoning method, based on the fusion embedding of semantic correlation and structure correlation, named SecKG2vec. By SecKG2vec, the embedded vector simultaneously presents both semantic and structural characteristics, and it can exhibit better relational reasoning performance. In qualitative evaluation and quantitative experiments with baseline methods, SecKG2vec has better performance in relationship reasoning task and entity reasoning task, and potential capability of 0-shot scenario prediction.

Transitions Into Freezing Environments Linked With Shifts in Phylogenetic Integration Between Vitaceae Leaf Traits.

Understanding how the intrinsic ability of populations and species to meet shifting selective demands shapes evolutionary patterns over both short and long timescales is a major question in biology. One major axis of evolutionary flexibility can be measured by phenotypic integration and modularity. The strength, scale, and structure of integration may constrain or catalyze evolution in the face of new selective pressures. We analyze a dataset of seven leaf measurements across Vitaceae to examine how correlations in trait divergence are linked to transitions between freezing and nonfreezing habitats. We assess this by applying a custom algorithm to compare the timing of habitat shifts to changes in the structure of evolutionary trait correlation at discrete points along a phylogeny. We also explore these patterns in relation to lineage diversification rates to understand how and whether patterns in the evolvability of complex multivariate phenotypes are linked to higher-level macroevolutionary dynamics. We found that shifts in the structure, but not the overall strength, of phylogenetic integration of leaves precipitate colonization of freezing climates. Lineages that underwent associated shifts in leaf trait integration and subsequent movement into freezing habitats also displayed lower turnover and higher net diversification, suggesting a link among shifting vectors of selection, internal constraint, and lineage persistence in the face of changing environments.

Correlation of interface structure and optical properties of Ga(N,As) and Ga(As,Bi) based type-II hetero structures

The type-II band alignment of particular III/V heterostructures is a promising route towards achieving certain wavelengths on specific substrates, which would not be possible with type-I structures. One example is telecommunication lasers on GaAs substrates. This study reports on the progress in combining dilute nitrides and dilute bismides in W-type hetero structures to improve the luminescence intensity, which is a fundamental prerequisite for future incorporation in a laser structure. Increasing the emission wavelength of these structures is challenging and the interface formation is critical, especially as two metastable materials are combined. Here, we investigate the impact of different interface configurations by growing Ga(N,As)/Ga(As,Bi) and Ga(As,Bi)/Ga(N,As) type-II structures. We employ metal–organic vapor phase epitaxy (MOVPE) to grow Ga(N,As) and Ga(As,Bi) layers and investigate the effects of interlayer thicknesses on the structural and optical properties of the hetero structures. The results indicate that − while the introduction of a GaAs interlayer can affect the direct and indirect transitions intensities − it does not significantly improve the interface quality. However, this is strongly influenced by the order in which the materials are grown. The growth of Ga(N,As) on Ga(As,Bi) shows no peculiarities, while the type II transition energy is shifted to lower energies when Ga(As,Bi) is grown on Ga(N,As). High-resolution X-ray diffraction (HR-XRD), photoluminescence (PL) spectroscopy, atomic force microscopy (AFM), and scanning transmission electron microscopy (STEM) were used to characterize the samples.

Computational exploration of naturally derived peptides inhibitory mechanisms against ACE enzyme, from interactions to structural-dynamics

Naturally derived peptides have gained significant attention because of their potential to reduce blood pressure. These peptides can be derived from various sources, including snake venom, marine organisms, cow’s milk, seahorses, and plants. Investigating the underlying mechanisms of these peptides in lowering blood pressure is crucial for replacing synthetic drugs currently in use. In this regard, we conducted in silico studies, such as molecular docking, classical molecular dynamics (MD) simulations, and QM/MM simulation methods, on two naturally derived peptides (PAGPRGPA and WALKGYK) and Angiotensin II (ANGII) as the enzyme substrates for binding to sACE. The results of 500 ns MD calculations showed that the WALKGYK peptide occupies the ACE binding site, similar to the ANGII and BPPb peptides, two distinguished C-domain-specific inhibitors. Furthermore, QM/MM calculations demonstrated that no peptide bond cleavage was mediated by Zn2+ at the catalytic site of the peptides. However, during the 500 ns MD simulation, the backbone oxygens of LYS4 and GLY5 of the WALKGYK peptides were tightly coordinated to the Zn2+ ion. Free energy calculations also confirmed the higher affinity of the WALKGYK peptides for binding to sACE. In addition, structural analysis correlation showed a different pattern in PAGPRGPA compared to WALKGYK and ANGII. Despite the similarity of the peptide PAGPRGPA to typical ACE peptide inhibitors with hydrophobic ends, the electrostatic composition of the WALKGYK peptide showed a higher tendency towards ACE inhibition. Therefore, peptide residue compositions, such as WALKGYK, can be considered for designing new inhibitors with fewer side effects for sACE C-terminal inhibitors.

A Systematic Approach for Estimating Colloidal Particle Adsorption Model Parameters

The estimation of ion- exchange chromatography model parameters is crucial to enable efficient model-assisted biopharmaceutical downstream process development. Model calibration methods can be hindered by model limitations combined with parameter correlations, leading to time-consuming repeated parameter estimations. While Steric Mass Action isotherm estimation methods exist, there is a need for a systematic approach to estimate model parameters for an emerging Colloidal Particle Adsorption (CPA) model proposed by Briskot et al. This study presents a novel strategy that addresses this challenge, offering significant improvements.Through a parameter sensitivity analysis, we identified key levers for improved CPA parameter estimation, enabling the prediction of elution behavior for low and high load densities in gradient and step elution mode. This analysis also revealed the correlation structure of parameters, allowing the establishment of a minimalized experimental data set for parameter estimation, by using one breakthrough, a high load and three low load density gradient elution experiments. Our workflow leverages a surrogate-assisted global-optimization tool, minimizing computationally expensive function evaluations during parameter fitting. Furthermore, we employed a customized objective function, specifically adapted to the model structure and sensitivity results, to enhance the solver's performance.Our strategy was tested on three model proteins with molecular weights of approximately 50, 150 and 200 kDa using a strong cation exchange Poros 50 HS resin. Our final approach enabled high throughput CPA model calibration for single components. The resulting CPA models were able to describe non-binding protein-pulses, low and high-loaded gradient elution, break through, as well as isocratic elution experiments.

Brain structural associations of syntactic complexity and diversity across schizophrenia spectrum and major depressive disorders, and healthy controls

Deviations in syntax production have been well documented in schizophrenia spectrum disorders (SSD). Recently, we have shown evidence for transdiagnostic subtypes of syntactic complexity and diversity. However, there is a lack of studies exploring brain structural correlates of syntax across diagnoses. We assessed syntactic complexity and diversity of oral language production using four Thematic Apperception Test pictures in a sample of N = 87 subjects (n = 24 major depressive disorder (MDD), n = 30 SSD patients both diagnosed according to DSM-IV-TR, and n = 33 healthy controls (HC)). General linear models were used to investigate the association of syntax with gray matter volume (GMV), fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD). Age, sex, total intracranial volume, group, interaction of group and syntax were covariates of no interest. Syntactic diversity was positively correlated with the GMV of the right medial pre- and postcentral gyri and with the FA of the left superior-longitudinal fasciculus (temporal part). Conversely, the AD of the left cingulum bundle and the forceps minor were negatively correlated with syntactic diversity. The AD of the right inferior-longitudinal fasciculus was positively correlated with syntactic complexity. Negative associations were observed between syntactic complexity and the FA of the left cingulum bundle, the right superior-longitudinal fasciculus, and the AD of the forceps minor and the left uncinate fasciculus. Our study showed brain structural correlates of syntactic complexity and diversity across diagnoses and HC. This contributes to a comprehensive understanding of the interplay between linguistic and neural substrates in syntax production in psychiatric disorders and HC.

Spatial-Spectral Middle Cross-Attention Fusion Network for Hyperspectral Image Superresolution

The spatial and spectral features of hyperspectral images exhibit complementarity, and neglecting them prevents the full exploitation of useful information for superresolution. This article proposes a spatial-spectral middle cross-attention fusion network to explore the spatial-spectral structure correlation. Initially, we learn spatial and spectral features through spatial and spectral branches instead of single ones to reduce information compression. Then, a novel middle-cross attention fusion block that includes middle features fusion strategy and cross-attention is proposed to fuse spatial-spectral features to enhance their mutual effects, which aims to explore the spatial-spectral structural correlations. Finally, we propose a spectral feature compensation mechanism to provide complementary information for adjacent band groups. The experimental results show that the proposed method outperforms state-of-the-art algorithms in object values and visual quality.

Link between permanent shear-banding and local concentration fluctuations in suspensions of compressible microgels

We uncover the occurrence of shear banding in dense suspensions of compressible microgels. Velocimetry measurements evidence the presence of permanent but unsteady shear-banding for sufficiently small Peclet numbers, with the formation of a central plug-like flow. Small-angle neutron scattering experiments under shear link the observed banding phenomenon to structural variations along the velocity gradient, providing a connection between the arrested band and the increase in structural correlations associated with changes in the local packing fraction. This provides unique evidence of a shear–concentration coupling mechanism in jammed suspensions of compressible particles.

Accelerating computation: A pairwise fitting technique for multivariate probit models

Fitting multivariate probit models via maximum likelihood presents considerable computational challenges, particularly in terms of computation time and convergence difficulties, even for small numbers of responses. These issues are exacerbated when dealing with ordinal data. An efficient computational approach is introduced, based on a pairwise fitting technique within a pseudo-likelihood framework. This methodology is applied to clinical case studies, specifically using a trivariate probit model. Additionally, the correlation structure among outcomes is allowed to depend on covariates, enhancing both the flexibility and interpretability of the model. By way of simulation and real data applications, the proposed approach demonstrates superior computational efficiency as the dimension of the outcome vector increases. The method's ability to capture covariate-dependent correlations makes it particularly useful in medical research, where understanding complex associations among health outcomes is of scientific importance.