Dependent Conditions Research Articles

Detecting departures from the conditional independence assumption in diagnostic latent class models: a simulation study

BackgroundLatent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios.MethodsWe generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G2 and χ2 statistics indicated lack-of-fit globally or for each test pair.ResultsAcross all settings, residual correlation plots, pairwise G2 and χ2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G2 and χ2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests.ConclusionsResidual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

Multiscale modeling of short pulse laser induced amorphization of silicon

Silicon surface amorphization by short pulse laser irradiation is a phenomenon of high importance for device manufacturing and surface functionalization. To provide insights into the processes responsible for laser-induced amorphization, a multiscale computational study combining atomistic molecular dynamics simulations of nonequilibrium phase transformations with continuum-level modeling of laser-induced melting and resolidification is performed. Atomistic modeling provides the temperature dependence of the melting/solidification front velocity, predicts the conditions for the transformation of the undercooled liquid to the amorphous state, and enables the parametrization of the continuum model. Continuum modeling, performed for laser pulse durations from 30 ps to 1.5 ns, beam diameters from 5 to 70 μm, and wavelengths of 532, 355, and 1064 nm, reveals the existence of two threshold fluences for the generation and disappearance of an amorphous surface region, with the kinetically stable amorphous phase generated at fluences between the lower and upper thresholds. The existence of the two threshold fluences defines the spatial distribution of the amorphous phase within the laser spot irradiated by a pulse with a Gaussian spatial profile. Depending on the irradiation conditions, the formation of a central amorphous spot, an amorphous ring pattern, and the complete recovery of the crystalline structure are predicted in the simulations. The decrease in the pulse duration or spot diameter leads to an accelerated cooling at the crystal–liquid interface and contributes to the broadening of the range of fluences that produce the amorphous region at the center of the laser spot. The dependence of the amorphization conditions on laser fluence, pulse duration, wavelength, and spot diameter, revealed in the simulations, provides guidance for the development of new applications based on controlled, spatially resolved amorphization of the silicon surface.

Assessing the Association Between Animal Color and Behavior: A Meta-Analysis of Experimental Studies.

Color varies in pattern and degree across the tree of life. In animals, genetic variation in color is hypothesized to have pleiotropic effects on a variety of behaviors due to shared dependence on underlying biochemical pathways. Such pleiotropy can constrain the independent evolution of color and behavior. Although associations between color and behavior have been reported, this relationship has not yet been addressed across a broad taxonomic scale with a formal meta-analysis. We used a phylogenetic meta-analytic approach to examine the relationship between individual variation in aggressive behavior and variation in multiple colors. Seventy-four studies met our inclusion criteria (vertebrates = 70; invertebrates = 4). After accounting for phylogeny and correcting for publication bias, there was a positive association between measures of aggression and degree or area of coloration (mean = 0.248, 95% CI = (0.044, 0.477)). Because this positive association was not restricted to melanin-based coloration, we conclude that this pattern does not strongly support the melanin pleiotropy hypothesis. Because the association was also not affected by moderators accounting for individual condition, social rank, or age, the results do not strongly support hypotheses that condition dependence accounts for relationships between color and aggressive behavior. The badge of status hypothesis predicts that arbitrary traits can evolve to signal aggression or social dominance. We propose that this is the most parsimonious explanation for the patterns we observe. Because of the lack of evidence for condition dependence in the association between color and aggression, we further propose that the genetic covariation between traits contributes to the evolution of the badges of status.

Unsupervised Modelling of E-Customers’ Profiles: Multiple Correspondence Analysis with Hierarchical Clustering of Principal Components and Machine Learning Classifiers

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies.

The structure of adult thinking: A network approach to (meta)cognitive processing

Complex cognitive processes have been broadly categorized into three general domains: first-order cognition (i.e., thinking directed to solve problems), metacognition (i.e., thinking about one's thinking during problem-solving), and epistemic cognition (i.e., thinking about the epistemic nature of problems and beliefs about criteria for knowledge justification). Few, if any studies, have empirically examined the conditional dependencies between a large inventory of components simultaneously. This paper aims to contribute the first set of preliminary explorations into the interrelationships between different thinking and reasoning components that represent key aspects of emerging adult cognitive processing using a psychological network approach. In two cross-sectional studies (combined N = 1496), data was collected from undergraduate students enrolled at a large public university. Scrutiny of the networks suggests that thinking dispositions and competency with probability are key bridges between metacognitive abilities and epistemic beliefs. Implications for instruction are discussed. Educational relevance statementIt remains a perennial aim of all education systems to improve the thinking and reasoning of students. But which complex cognitive processes are worthwhile targets, and how do they fit among the plethora of metacognitive, self-regulatory, and epistemological belief aspects of students? The present set of studies is the first to apply a network approach to a broad array of cognitive components to uncover the central student-level variables that can be targeted with instruction. Based on the findings of the two studies presented, instruction aimed at epistemic dispositions could potentially assist in the development of complex cognition because of their centrality to networks of effective reasoning processes.

ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.

The Gaussian graphical model (GGM) is a statistical network approach that represents conditional dependencies among components, enabling a comprehensive exploration of disease mechanisms using high-throughput multi-omics data. Analyzing differential and similar structures in biological networks across multiple clinical conditions can reveal significant biological pathways and interactions associated with disease onset and progression. However, most existing methods for estimating group differences in sparse GGMs only apply to comparisons between two groups, and the challenging problem of multiple testing across multiple GGMs persists. This limitation hinders the ability to uncover complex biological insights that arise from comparing multiple conditions simultaneously. To address these challenges, we propose the Omics Networks Differential and Similarity Analysis (ONDSA) framework, specifically designed for continuous omics data. ONDSA tests for structural differences and similarities across multiple groups, effectively controlling the false discovery rate (FDR) at a desired level. Our approach focuses on entry-wise comparisons of precision matrices across groups, introducing two test statistics to sequentially estimate structural differences and similarities while adjusting for correlated effects in FDR control procedures. We show via comprehensive simulations that ONDSA outperforms existing methods under a range of graph structures and is a valuable tool for joint comparisons of multiple GGMs. We also illustrate our method through the detection of neuroinflammatory pathways in a multi-omics dataset from the Framingham Heart Study Offspring cohort, involving three apolipoprotein E genotype groups. It highlights ONDSA's ability to provide a more holistic view of biological interactions and disease mechanisms through multi-omics data integration.

Nitrogen Regime of the Agro-Grey Soil of a Cherry Orchard and the Yield of Trees when Applying Fertilizers

2018–2022 in order to estimate the nitrogen dozes essential for this fruit crop in the first decade after planting. The field experiment was carried out at an orchard located in the forest-steppe zone of the Central Russian upland (Orel region). Urea and potassium sulfate were applied to the soil once a year in early spring with doses arising from N30K40 to N120K160 kg/ha. The dynamics of N-NH4 in the soil of fertilized and unfertilized plots were similar, the lowest level of the indicator was in July during fruit ripening. The content of N-NO3 in unfertilized soil fluctuated slightly through the vegetation period. The fertilizer treatments led to more intensive seasonal fluctuations of N-NO3 in dependence of weather conditions. Annual fertilization led to annual increase of N-NH4 content by 2–12 and N-NO3 by 18–70 times, depending on the dose of nitrogen. But the accumulation of mineral nitrogen in root zone over the years was not revealed. The fertilizers did not affect sour cherry yield when it was lower than 15 t/ha. Still in the high-yielding season the fertilizers application in dose of N60K80 was necessary to reach the yield 20 t/ha.

Model selection for Markov random fields on graphs under a mixing condition

We propose a global model selection criterion to estimate the graph of conditional dependencies of a random vector. By global criterion, we mean optimizing a function over the set of possible graphs, eliminating the need to estimate individual neighborhoods and subsequently combine them to estimate the graph. We prove the almost sure convergence of the graph estimator. This convergence holds, provided the data is a realization of a multivariate stochastic process that satisfies a polynomial mixing condition. These are the first results to show the consistency of a model selection criterion for Markov random fields on graphs under non-independent data.

Quasi-Diabatization Based on Minimizing Derivative Couplings in a Limited Configuration Space: Elimination of Boundary Condition Dependence.

Due to the cuspidal ridges of adiabatic potential energy surfaces (PESs) and singularities of nonadiabatic couplings (NACs), obtaining an analytical expression for the adiabatic Hamiltonian is difficult. Thereby, nonadiabatic dynamics simulations are often carried out on-the-fly, which is time-consuming. This motivates us to construct quasi-diabatic representations, which have smooth PESs and diabatic couplings. In this study, we propose a new quasi-diabatization method based on minimizing derivative couplings (MDC) in a limited configuration space. The boundary conditions are first considered and finally released to obtain the adiabatic-to-diabatic rotation angles and transformation matrices. As demonstrated in representative one- and two-dimensional models and the widely studied linear H3 molecule, MDC performs significantly better than the direct integration quasi-diabatization approach. In particular, accurate diabatic potential energy matrices have been successfully obtained even when the NACs of all configurations in the considered space are nonnegligible.

Median and quantile conditional copulas

Abstract This article studies the conditional dependency between random variables, conditionally upon a covariate (vector). The conditional copula fully characterizes this conditional dependency. A way to summarize this dependence structure taking into account the impact of the covariate is via the average conditional copula, which under fairly general conditions coincides with the partial copula. A mean is just one way to summarize this conditional dependence behaviour. In this article, we introduce the notions of median conditional copula and more generally quantile conditional copula. We investigate the existence of these concepts and establish explicit expressions for calculating them. Examples are given to illustrate the concepts, and the practical use of them is demonstrated in real data examples.

Health sciences training for disability inclusion: the need to engage with emotion

Material aspects of disability inequality, such as access to quality services, housing and employment, are an urgent, worldwide concern. Less recognised though, are psychological layers of prejudice and discrimination, which...

Advanced Deep Learning Techniques for Information Security Vulnerability Detection Using Machine Learning

The increasing rate on the complexity and amount of security threats demand more advanced information security vulnerability detection techniques. Traditional methods have limited capability to respond to such diverse and evolving threats in real-time. This paper presents a complete solution by using new machine learning models in combine with advanced mathematical approaches to organize the early detection and prediction of vulnerabilities that later content will be about the information security systems. We investigate the accuracy of different models (with an emphasis on modern approaches that go beyond the standard SVM, CNN, and GNN). It initiates with Bayesian Networks that use probabilistic graphical models for showing the interactions of diverse security characteristics to support reasoning about vulnerabilities as a function on the conditional dependencies. Decision Trees and ensemble techniques like Extreme Gradient Boosting (XGBoost) are able to deal with both heterogeneous data types and are more capable of modelling complex interaction between security features. For unsupervised cases, we use things like Isolation Forests for anomaly detection and Gaussian Mixture Models (GMM) to detect rare patterns in network packets that may be a sign of security attacks. We further investigate the possibility of reinforcement learning (i.e., Q-Learning) to continue to evolve with network changes and identify threats in real-world threatening environment. The learning process is streamlined and the interpretability of results is improved by integrating mathematical techniques such as Markov Chains (for modelling probabilistic transitions within network states) and optimization methods like Lasso Regression (for feature selection). In addition to this, the approach explores Autoencoder-based models but most specifically Variational Autoencoders (VAEs) for their unsupervised learning ability in identifying rare and new zero-day vulnerabilities. Extensive experiments on a variety of cybersecurity datasets demonstrate the effectiveness and efficiency of our approach, where substantial enhancements in detection speed and accuracy are achieved. The cloud-based cybersecurity model introduced in this research, backed by advanced machine learning models and mathematical frameworks could potentially serve to lay a foundation for the future of real-time cyber security defence mechanisms.

A network analysis of affective and motivational individual differences and error monitoring in a non-clinical sample.

Error monitoring, which plays a crucial role in shaping adaptive behavior, is influenced by a complex interplay of affective and motivational factors. Understanding these associations often proves challenging due to the intricate nature of these variables. With the aim of addressing previous inconsistencies and methodological gaps, in this study, we utilized network analysis to investigate the relationship between affective and motivational individual differences and error monitoring. We employed six Gaussian Graphical Models on a non-clinical population ($N$ = 236) to examine the conditional dependence between the amplitude of response-related potentials (error-related negativity; correct-related negativity) and 29 self-report measures related to anxiety, depression, obsessive thoughts, compulsive behavior, and motivation while adjusting for covariates: age, handedness, and latency of error-related negativity and correct-related negativity. We then validated our results on an independent sample of 107 participants. Our findings revealed unique associations between error-related negativity amplitudes and specific traits. Notably, more pronounced error-related negativity amplitudes were associated with increased rumination and obsessing, and decreased reward sensitivity. Importantly, in our non-clinical sample, error-related negativity was not directly associated with trait anxiety. These results underscore the nuanced effects of affective and motivational traits on error processing in healthy population.

Thermochromic properties and surface chemical states of reactive magnetron sputtered vanadium oxide thin films at various deposition pressures

Vanadium oxide thin films were prepared using reactive magnetron sputtering method, varying the deposition pressures from 4 to 10 mTorr while maintaining the other sputtering parameters constant. Our investigations revealed sensitive dependence of deposition pressure, chemical states of the V and O atoms, crystal phases, and thermochromic (TC) optical modulation. Specifically, the film deposited at 8 mTorr displayed distinct TC characteristics with a prevalent monoclinic m-VO2 phase according to X-ray diffraction (XRD), supported by X-ray photoelectron spectroscopy (XPS). Optical transmittance modulation and electrical property measurements revealed a metal–insulator transition (MIT), confirming the intimate connection between TC characteristics and presence of the m-VO2 phase. Films deposited at pressures lower or higher than 8 mTorr exhibited typical semiconductor behavior without TC characteristics, further supported by in situ XRD, where (110) plane shifted to lower angle upon heating above τC. Chemical state analysis by XPS revealed a clear correlation between V−O peaks and the presence of a VO2 phase, with the film deposited at 8 mTorr displaying relatively large peak fitted area indicative of the VO2 phase. These findings highlight the critical and sensitive dependence of the deposition conditions in tailoring the properties of vanadium oxide thin films and provide valuable insights for potential applications in chromogenic films.

Energy dependence of heavy-ion initial condition in isobar collisions

Collisions of isobar nuclei, those with the same mass number but different structure parameters, provide a new way to probe the initial condition of the heavy ion collisions. Using transport model simulation of 96Ru+96Ru and 96Zr+96Zr collisions at two energies sNN=0.2 TeV and 5.02 TeV, where 96Ru and 96Zr nuclei have significantly different deformations and radial profiles, we identify sources of eccentricities contributing independently to the final state harmonic flow vn. The efficacy for flow generation differs among these sources, and explains the modest energy dependence of the isobar ratios of vn. Additionally, a significant component of vn is found to be uncorrelated with the eccentricity, but is instead generated dynamically during system evoluation. Experimental measurement of these ratios at the LHC energy and comparison with RHIC energy can provide insight into the collision-energy dependence of the initial condition.

Evaluation of three serological tests for the diagnosis of Brucella suis in dogs using Bayesian latent class analysis

Brucella suis infection of dogs is an emerging issue worldwide requiring specific management to address zoonotic risks and animal welfare concerns. Diagnosis in dogs is routinely based on serological testing, but to date these tests have only been validated for use in production animal species and humans. This study aimed to assess the diagnostic performance of three commonly used serological tests in dogs. Canine sera (n = 989) were tested with the Rose Bengal rapid plate agglutination test (RBRPT), the complement fixation test (CFT) and a competitive ELISA (C-ELISA). Diagnostic test performance was evaluated using a three test, two population Bayesian latent class analysis accounting for conditional dependence between the three tests. Positive and negative predictive values (PPV, NPV) were calculated for a range of expected prevalence estimates for the individual tests and test combinations interpreted in series and parallel. The RBRPT showed the highest individual Se of 0.902 (95 % posterior credible interval [PCI] 0.759–0.978) and the CFT the highest individual diagnostic specificity (Sp) of 0.914 (95 % PCI 0.886–0.946). The C-ELISA had marginally the best overall diagnostic performance (Youden’s index = 0.807). The CFT and the C-ELISA interpreted in parallel returned the highest combined Se and Sp (0.988 and 0.885, respectively). All tests returned NPVs of > 0.982 in 2–8 % prevalence settings. Series interpretation of the three-test combination as well as the two-test combinations of the RBRPT and the C-ELISA and the CFT and the C-ELISA produced a PPV of 0.502 when the estimated prevalence was 8 %. While all tests are suitable for the detection of B. suis antibodies in dogs, they should not be interpreted in isolation as their diagnostic value is dependent on the pre-test probability of the disease. As such they are useful tools for the diagnosis of B. suis in dogs when exposure, history and clinical presentation indicate a risk of brucellosis.

Effects of Growth and Treatment Conditions on the Quality of Norway Spruce (Picea abies L.) Sawn Timber

A study was conducted to improve the effectiveness of silvicultural production of structural sawn timber from softwoods. It intends to explore prediction methods for mechanical timber quality. The study material was obtained from six stands divided into age groups of approximately 40- and 80-year-old trees (examining the influence of age). The stands were differentiated by their applied thinning system of thinning from below or above (examining the influence of the thinning system). Resulting from these different levels of data, i.e., stand parameters, tree anatomy, and visual board properties are examined and analyzed in ordinal logistic models and linear mixed models. Visual board properties were discerned by means of the German standard for visual grading of sawn timber. The mechanical board properties were measured in on-edge bending strength tests and allocated into strength classes, which were modeled in dependence of visual characteristics and forestry conditions. The evaluation of mechanical properties attributed a significant loss of timber quality to short rotation periods, non-ideal water supply, and a single-tree management system. The prediction capabilities of models based on site and tree characteristics were on par with the accuracy of visual grading. Management adaptations by intense thinning from above can lead to a significant decline in Norway spruce (Picea abies L.) timber quality when site factors coincide. Particular care should be taken in the management of locations with high yield potential. Non-destructive evaluation based on site characteristics combined with terrestrial laser scan data of tree characteristics has potential as a pregrading method.

Temperature gradient estimation of long-span concrete box girders using average conditional exceedance rate function

An accurate assessment of temperature gradients in box girders is crucial for design and maintenance of long-span bridges. Although the current bridge specifications have provided temperature gradient patterns, these existing specifications are insufficient to accurately describe temperature gradients of bridges in service period. In this article, a novel methodology for determining an efficient positive temperature gradient pattern considering condition dependent and whole design service life is proposed. Firstly, statistical analysis and curve fitting are performed by using the long-term temperature data collected through structural health monitoring system, which indicates that temperature gradient patterns can be described as exponential functions. Secondly, the representative values of temperature difference have been estimated using average conditional exceedance rate (ACER) functions, which can use short-term data and consider conditional dependencies to predict long-term extreme values. Finally, the temperature gradient patterns of box girder of bridges in the whole service life have been estimated. Compared with the gradient patterns in current Chinese specification, the proposed temperature gradient pattern demonstrates better applicability in a long-span continuous concrete box-girder bridge. This result is expected to provide a research reference for accurately assessment of temperature gradients in box girders, which is crucial for design and maintenance of long-span bridges.

Accuracy of CAD4TB (Computer-Aided Detection for Tuberculosis) on paediatric chest radiographs.

Computer-aided detection (CAD) systems hold promise for improving tuberculosis (TB) detection on digital chest radiographs. However, data on their performance in exclusively paediatric populations are scarce. We conducted a retrospective diagnostic accuracy study evaluating the performance of CAD4TBv7 (Computer-Aided Detection for Tuberculosis version 7) using digital chest radiographs from well-characterised cohorts of Gambian children aged <15 years with presumed pulmonary TB. The children were consecutively recruited between 2012 and 2022. We measured CAD4TBv7 performance against a microbiological reference standard (MRS) of confirmed TB, and also performed Bayesian latent class analysis (LCA) to address the inherent limitations of the MRS in children. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC) and point estimates of sensitivity and specificity. A total of 724 children were included in the analysis, with confirmed TB in 58 (8%), unconfirmed TB in 145 (20%) and unlikely TB in 521 (72%). Using the MRS, CAD4TBv7 showed an AUROC of 0.70 (95% CI 0.60-0.79), and demonstrated sensitivity and specificity of 19.0% (95% CI 11-31%) and 99.0% (95% CI 98.0-100.0%), respectively. Applying Bayesian LCA with the assumption of conditional independence between tests, sensitivity and specificity estimates for CAD4TBv7 were 42.7% (95% CrI 29.2-57.5%) and 97.9% (95% CrI 96.6-98.8%), respectively. When allowing for conditional dependence between culture and Xpert assay, CAD4TBv7 demonstrated a sensitivity of 50.3% (95% CrI 32.9-70.0%) and specificity of 98.0% (95% CrI 96.7-98.9%). Although CAD4TBv7 demonstrated high specificity, its suboptimal sensitivity underscores the crucial need for optimisation of CAD4TBv7 for detecting TB in children.

Robust direction estimation in single-index models via cumulative divergence

In this paper, we address direction estimation in single-index models, with a focus on heavy-tailed data applications. Our method utilizes cumulative divergence to directly capture the conditional mean dependence between the response variable and the index predictor, resulting in a model-free property that obviates the need for initial link function estimation. Furthermore, our approach allows heavy-tailed predictors and is robust against the presence of outliers, leveraging the rank-based nature of cumulative divergence. We establish theoretical properties for our proposal under mild regularity conditions and illustrate its solid performance through comprehensive simulations and real data analysis.