Complex Model Research Articles

Determining the Optimal Tradeoff between Compute and Accuracy for Diffusion Models in Molecular Docking

In the world of molecular biology, Molecular Docking has become a pillar for understanding complex interactions pivotal for drug design and more. The emergence of sophisticated machine learning models, such as Diffusion Generative Models, have significantly expanded our analytical capabilities. However, this evolution has introduced notable computational challenges. This study aims to examine the trade-off between computational demands and model accuracy. Our methodology, which incorporates free platforms, illuminates methods to conserve computational resources while maintaining nearoptimal accuracy. Our findings suggest that using 30 samples per complex, 15 inference steps, and 4 batch steps improves pose prediction accuracy and reduces computational resources. The proposed parameters achieve a 14% accuracy increase compared to the 40 samples per complex model and a 56.25% increase compared to the 10 samples per complex model. The optimized inference steps result in a 12.2% accuracy increase over the 20-step control using the 40 samples model and a 24.3% increase using the 10 samples model. Additionally, using 4 batch steps leads to a 40.6% increase in DiffDock Confidence for the 10-sample control and a 0.4% increase for the 40- sample control.

How stop smoking outreach works: a co-inquiry informed, complex intervention model

Abstract Background Across Europe, smoking prevalence is concentrating in underserved communities with high levels of deprivation. And yet, there is little research on how stop smoking services (SSS) can engage these communities. This study presents the findings of a 12-month collaboration involving an external public health research team and staff of an English SSS. Staff worked with community partners (e.g., schools) to raise awareness of the service, which included a newly introduced e-cigarette offer. Methods A complex intervention model was developed and refined through 6 facilitated co-inquiry sessions involving 13 SSS staff. Staff also completed 32 reflection forms on outreach events and rapid ethnography was conducted, by the research team, to evaluate staff’s working assumptions. This included 8 observations of outreach events and interviews with 10 SSS staff and 8 community members, from the service target groups. Data were organised into themes. Findings The model presents a picture of stop smoking outreach as challenging, unpredictable and resource intensive yet often having great impact. The themes enrich this to highlight 1) the appreciation for yet unpredictability of outreach referrals for people disconnected from health services; 2) the importance of staff adopting a person-centred approach; 3) the challenges and opportunities presented by e-cigarettes; and 4) the role of strategic community partnerships in optimising staff time on outreach. Conclusions Stop smoking outreach can generate considerable interest in SSS, particularly when delivered in combination with an e-cigarette offer. Future research should explore the economic and social return on investment in community outreach: while time consuming, referrals generated proactively may become more important for SSS as smoking concentrates in underserved communities. Key messages • Stop smoking outreach is an effective way to raise awareness and generate referrals but must be done non-judgementally and via effective community partnerships. • Staff’s careful discussion of an e-cigarette offer can change the perception of SSS without contributing to recreational normalization.

Are post-communist countries paying increased healthcare costs because of risky behaviour?

Abstract Background Based on the annual Country Health Profiles, we can see the difference in population behaviour across EU countries and the associated health risks. At first glance, the so-called post-communist countries are characterised by above-average per-capita harmful consumption of sin goods, alcohol and tobacco. Is this harmful consumption associated with increased healthcare costs? Methods The idea of the study is based on balanced panel-data published in Country Health Profiles. The member countries were divided into groups based on their health risk scores - high risk (above average), and low risk (below average). The group of countries with high risk is mostly composed of post-communist countries. The detailed per-capita annual data for the period 2013-2021 was collected, such as GDP, consumption of sin goods, alcohol, and tobacco. The analysis is based on fixed-effects linear regression models, following recent recommendations on suppressing the excessive role of statistical-inference tools. Results We built various models for different groups of countries, and in this abstract, we present results for the group of post-communist countries - a simple model of harmful consumption (sin goods, GDP) and a more complex model (alcohol, tobacco, GDP). The results show that in the case of the post-communist countries group, for a fixed level of GDP per capita, higher consumption of sin goods by EUR 1 per capita corresponds with total healthcare expenditures higher by EUR 0.244 per capita. For a fixed level of GDP per capita and tobacco consumption per capita, higher consumption of alcohol by EUR 1 per capita corresponds with total healthcare expenditures higher by EUR 1.367 per capita. Conclusions Harmful consumption has a multiplier effect in the health sector; besides direct impact, it influences an increase in healthcare expenditures. More emphasis should be placed on prevention and awareness of consumers to reduce harmful consumption. Key messages • For fixed GDP per capita, higher harmful consumption corresponds with higher healthcare costs in post-communist countries. • For fixed GDP per capita and tobacco consumption per capita, a €1 higher alcohol consumption corresponds with more than €1 higher healthcare expenditure in the post-communist countries.

Reducing Consumer–Brand Incongruity Through Corporate Social Responsibility and Brand Trust: Exploring Negative Word‐of‐Mouth (NWOM)

ABSTRACTDrawing upon consumer–brand disidentification theory and balance theory, this study examines symbolic and ideological incongruity in consumer–brand relationships through an original conceptual model shaped by negative past experiences, brand trust, perceived corporate social responsibility (CSR), and negative word‐of‐mouth (NWOM). A preliminary study was conducted to explore the dimensions of consumers' negative past experiences by topic detection. Latent Dirichlet allocation (LDA) topic modeling was undertaken to analyze online consumer reviews (n = 6095) about a coffee chain brand. The dimensions detected in this preliminary study were included in the research model and further analyzed in the main study. The main study, a cross‐sectional consumer survey (n = 522), tested the original research model by way of partial least squares structural equation modeling (PLS‐SEM) on SmartPLS. The findings showed that negative past experiences consisted of product‐related, service‐related, and technology‐related problems and negatively influenced brand trust. It was found that brand trust and perceived CSR negatively affected symbolic and ideological incongruity, while symbolic and ideological incongruity positively influenced NWOM. The findings provide empirical evidence for balance theory by showing that the three critical domains of consumer–brand relationships (ideological, symbolic, and experiential) provide a complex cognitive model that covers personal‐symbolic and moral‐societal aspects of consumer–brand disidentification and consequent NWOM intentions. In line with consumer–brand disidentification theory, the results contribute to the literature by demonstrating the direct negative impacts of brand trust and perceived CSR on symbolic and ideological incongruity, as well as the direct positive impacts of symbolic and ideological incongruity on NWOM.

Prediction of overweight/obesity risk in adolescence using static and dynamic multi-exposure models

Abstract Background Studying the risk factors for obesity development and timely identification of individuals at increased risk are key for obesity prevention. We aimed to assess the associations of multiple prenatal, infancy and childhood exposures with overweight/obesity at 13 years (y) and develop static and dynamic multiple-exposure prediction models. Methods In 4232 mother-child pairs from the Generation XXI birth cohort, we assessed, during pregnancy/infancy and childhood, 55 sociodemographic factors, anthropometric measures, lifestyle behaviors, and clinical exposures related to the mother and the child and categorized body mass index z-scores as normal weight and overweight/obesity at 13y. Static models for pregnancy/infancy, 4, 7 and 10y, and a dynamic model were fitted using Elastic Net and logistic regression and were compared in terms of predictive performance. Results Mothers with lower educational level and household income, no partner, overweight/obesity before and after pregnancy, excessive gestational weight gain, pregnancy complications and who were smokers during pregnancy had children with a higher risk of overweight/obesity at 13y. Children with higher BMI during infancy, overweight/obesity during childhood, an early introduction of solid foods (before 4 months), a higher consumption of soft drinks at 4y and females had a higher risk of overweight/obesity at 13y. We observed the lowest predictive performance for the pregnancy/infancy model (prediction error of 36%) and the highest predictive performance for the 10y and dynamic models (prediction errors of 17%). Conclusions We identified the most important predictors of overweight/obesity in adolescence, most of them modifiable, and able to be targeted in prevention strategies. Predicting overweight/obesity risk in adolescence might be suboptimal if only considering pregnancy/infancy data and improves as closer to the age of the outcome, with no additional benefit in using a complex dynamic model. Key messages • Prevention of overweight/obesity should start during prenatal life. • Children at higher risk of overweight/obesity may be identified years earlier using prediction models in clinical practice.

Fish Species Spinal Cord Injury Models Utility for Research: A Systematic Review of Methodologies and Outcomes

Context: Spinal cord injury (SCI) is a devastating condition that results in severe disability and significant comorbidities. The complex pathophysiology of SCI repair and difficulties understanding neural regeneration are treatment challenges. Objectives: The aim of this study is to systematically review the diverse applications of various fish species as models for SCI research. Evidence Acquisition: PRISMA guidelines were used to review observational and interventional studies that utilized fish species as SCI models, published from inception to July 2023. Two independent reviewers screened and performed the data extraction. One independent reviewer assessed the risk of bias for the included studies. Results: Five thousand six hundred and thirty-three records were reviewed, and 144 met the inclusion criteria and were categorized by fish species. The majority of studies employed complete transection injuries, with the remainder being crush injuries, laser injuries, electro-ablations, and demyelination with substances. Zebrafish (Danio rerio) were most commonly utilized 102/144 (71%), primarily with larval models. Other models included Lamprey (Petromyzon marinus and Lethenteron reissneri); Goldfish (Carassius auratus); European eel (Anguilla Anguilla); Knifefish (Apteronotus leptorhynchus and Apteronotus albifrons); Sailfin Molly (Poecilia latipinna); and African turquoise killifish (Nothobranchius furzeri). Conclusions: This systematic review highlights that fish models, particularly zebrafish, goldfish, and European eels, are important models for further defining SCI pathophysiology and regenerative processes. These models provide a less complex model to gain insights into apoptosis and glial networks.

Linear Programming-Based Sparse Kernel Regression with L1-Norm Minimization for Nonlinear System Modeling

This paper integrates L1-norm structural risk minimization with L1-norm approximation error to develop a new optimization framework for solving the parameters of sparse kernel regression models, addressing the challenges posed by complex model structures, over-fitting, and limited modeling accuracy in traditional nonlinear system modeling. The first L1-norm regulates the complexity of the model structure to maintain its sparsity, while another L1-norm is essential for ensuring modeling accuracy. In the optimization of support vector regression (SVR), the L2-norm structural risk is converted to an L1-norm framework through the condition of non-negative Lagrange multipliers. Furthermore, L1-norm optimization for modeling accuracy is attained by minimizing the maximum approximation error. The integrated L1-norm of structural risk and approximation errors creates a new, simplified optimization problem that is solved using linear programming (LP) instead of the more complex quadratic programming (QP). The proposed sparse kernel regression model has the following notable features: (1) it is solved through relatively simple LP; (2) it effectively balances the trade-off between model complexity and modeling accuracy; and (3) the solution is globally optimal rather than just locally optimal. In our three experiments, the sparsity metrics of SVs% were 2.67%, 1.40%, and 0.8%, with test RMSE values of 0.0667, 0.0701, 0.0614 (sinusoidal signal), and 0.0431 (step signal), respectively. This demonstrates the balance between sparsity and modeling accuracy.

Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR

Team errors are identified as a major contributor to incidents in safety-critical systems like nuclear power plants (NPPs). Safety in NPPs is more dependent on team performance than individual, and reducing team errors requires identifying key causative factors and chains. Current studies primarily use qualitative analysis whereas lacking quantitative methods. This study addressed this by constructing a complex network model of team error based on semantic features and coding of the Semantic Web and determining criteria for selecting key causative factors by three node failure strategies. The top 5 team error key PSFs and top 5 high-risk causative chains were identified in the results. Finally, a set of control strategies was proposed further. This research can guide identifying key causative factors which could be a good starting point to scrutinize the team performance and establish control strategies for team error in NPPs.

Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics

Interfacial processes involving metal (oxyhydr)oxide phases are important for the mobility and bioavailability of nutrients and contaminants in soils, sediments, and water. Consequently, these processes influence ecosystem health and functioning, and have shaped the biological and environmental co-evolution of Earth over geologic time. Here we employ reactive molecular dynamics simulations, supported by synchrotron X-ray spectroscopy to study the molecular-scale interfacial processes that influence surface complexation in ferrihydrite-water systems containing aqueous MoO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {MoO}_4}^{2-}$$\\end{document}. We validate the utility of this approach by calculating surface complexation models directly from simulations. The reactive force-field captures the realistic dynamics of surface restructuring, surface charge equilibration, and the evolution of the interfacial water hydrogen bond network in response to adsorption and proton transfer. We find that upon hydration and adsorption, ferrihydrite restructures into a more disordered phase through surface charge equilibration, as revealed by simulations and high-resolution X-ray diffraction. We observed how this restructuring leads to a different interfacial hydrogen bond network compared to bulk water by monitoring water dynamics. Using umbrella sampling, we constructed the free energy landscape of aqueous MoO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {MoO}_4}^{2-}$$\\end{document} adsorption at various concentrations and the deprotonation of the ferrihydrite surface. The results demonstrate excellent agreement with the values reported by experimental surface complexation models. These findings are important as reactive molecular dynamics opens new avenues to study mineral-water interfaces, enriching and refining surface complexation models beyond their foundational assumptions.Graphic

Substrate binding and catalytic mechanism of UDP-α-D-galactofuranose: β-galactofuranoside β-(1→5)-galactofuranosyltransferase GfsA

Abstract UDP-α-D-galactofuranose: β-galactofuranoside β-(1→5)-galactofuranosyltransferase, known as GfsA, is essential in synthesizing β-(1→5)-galactofuranosyl oligosaccharides that are incorporated into the cell wall of pathogenic fungi. This study analyzed the structure and function of GfsA from Aspergillus fumigatus. To provide crucial insights into the catalytic mechanism and substrate recognition, the complex structure was elucidated with manganese (Mn2+), a donor substrate product (UDP), and an acceptor sugar molecule (β-galactofuranose). In addition to the typical GT-A fold domain, GfsA has a unique domain formed by the N- and C-termini. The former interacts with the GT-A of another GfsA, forming a dimer. The active center that contains Mn2+, UDP, and galactofuranose forms a groove structure that is highly conserved in the GfsA of Pezizomycotina fungi. Enzymatic assays using site-directed mutants were conducted to determine the roles of specific active site residues in the enzymatic activity of GfsA. The predicted enzyme–substrate complex model containing UDP-α-D-galactofuranose characterized a specific β-galactofuranosyltransfer mechanism to the 5ʹ-OH of β-galactofuranose. Overall, the structure of GfsA in pathogenic fungi provide insights into the complex glycan biosynthetic processes of fungal pathogenesis and may inform the development of novel antifungal therapies.

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints

The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.

Uranium removal from contaminated groundwater using goethite-loaded composite microporous membrane

In this study, we have coupled adsorption and membrane separation for the removal of uranium from contaminated groundwater in environmentally relevant conditions at low energy requirements. This study mainly focuses on elucidating uranium, U(VI), adsorption mechanisms using surface complexation modeling approach in a novel goethite-loaded composite microfiltration membrane (GLM). This experiment involved immobilizing goethite nanorods in a microporous (0.22 μm pore size) poly (vinylidene fluoride) (PVDF) membrane. The effect of varying goethite loading and hydraulic residence time on U(VI) removal was investigated at field-relevant pH (i.e. pH 8.5). U(VI) adsorption (i.e. 4.95 μg·mg−1) was optimum at a goethite loading 1.20 mg·cm−2. The effect of varying hydraulic residence time had no impact on U(VI) removal which was also confirmed via performing batch adsorption kinetic experiments. GLM membrane loaded at 1.2 mg·cm−2 could treat 275 L of U(VI) contaminated water having 200 μg of U L−1 below WHO drinking water limit (i.e. 30 μg of U L−1) with 1 m2 of membrane surface area with a adsorption capacity of 6.12 μg·mg−1. Varying the pH of aqueous solution, containing U(VI) from pH 4.0 to pH 10.0, showed a significant impact on uranium uptake ranging from 0.7 μg·mg−1 to 2.63 μg·mg−1 by the composite membrane. The adsorption mechanism of uranium onto goethite was explained via the formation of bidentate surface complexes using the Surface Complexation Model (SCM). The results of batch pH edge experiments and SCM have been compared with pH experiments performed using GLM. The results of SCM predicted the batch pH edge experiment within a RMSE of 0.055. The trend of U(VI) removal in membrane experiments was observed to be similar to that of batch pH edge experiments and was well predicted SCM model. Our results showed that the novel goethite-loaded membrane has the potential for the effective removal of uranium with a lower specific energy consumption.

Interactions between dissolved organic matter with different molecular weights and nonylphenol in surface water bodies

Dissolved organic matter (DOM) has a complex composition, which can interact with various pollutants and affect the removal of pollutants. Therefore, a thorough understanding of the interaction between the encccvironmental hormone nonylphenol (NP) and DOM is crucial for environmental impact and development. In this study, the interaction was investigated by means of excitation emission matrix (EEM) fluorescence spectroscopy, UV–Vis spectroscopy, FT-IR spectroscopy, nuclear magnetic resonance (NMR) and complex model analysis. The interaction between different MW DOM and NP was verified by the spectral characterization data. According to the characterization analysis, the main components of DOM in water samples were proteinoid (C1, C2, C4) with MW < 1 k Da, and their binding capacity (log Ka value) and binding site number (n) showed the maximum values (3.37, 3.24, 3.26; 0.81, 1.22, 0.52). For the humus like substance (C3) with larger molecular weight, the log Ka value and the number of binding points n increased with increasing molecular weight, and the maximum values were 3.13 and 0.31, respectively. It can be seen that low molecular weight proteins have strong binding ability and binding sites with NP, and high molecular weight humus also have strong binding ability. Overall, the interaction between DOM and NP has molecular weight dependence and heterogeneity. The purpose of this study is to deeply understand the interaction characteristics of different MW DOM with NP, and to provide theoretical support and reference for the study of the removal effects of NP pollutants.

The impact of residential winter HVAC usage behavior on arthritis in older adults: A PLS-SEM analysis

Environmental factors like temperature and humidity have been shown to influence arthritis symptoms in older adults, and HVAC equipment can regulate indoor environmental parameters. This study aimed to explore the pathways and effectiveness of residential HVAC usage behavior during winter on arthritis conditions. Conducted across four major climate zones in China, this cross-sectional study involved 3579 older adults aged 60–74 years and collected data on demographics, residential environments, lifestyles, and health outcomes. Utilizing Partial Least Squares - Structural Equation Modeling (PLS-SEM), a complex structural equation model (SEM) and an HVAC-Health mediation model was developed to analyze the relationships among influencing factors and health outcomes. Results revealed that socio-economic status, building characteristics, lifestyles, HVAC usage behavior, and indoor environmental exposure significantly affect the frequency of arthritis flare-ups (p < 0.05). Specifically, lifestyles, indoor wet and cold exposures have strong direct effects on health outcomes, with path coefficients of 0.178, 0.155 and 0.143, respectively. Moreover, HVAC usage indirectly influences health through mediators, with cold exposure (β = 0.045, p < 0.001), air pollution (β = 0.025, p < 0.001), and wet exposure (β = 0.012, p = 0.001) ranked in terms of their mediating effects. This research underscores the crucial role of HVAC systems in mitigating arthritis symptoms in older adults, advocating for enhanced implementation of HVAC technologies in senior housing to promote health and well-being. The originality of this study lies in employing the PLS-SEM method to elucidate how the indoor environment—particularly under the premise of HVAC usage—affect arthritis symptoms in older adults.

Family Systems Dialectical Behavioral Therapy: A Proposed Clinical Synthesis

Dialectical behavioral therapy (DBT) has been well documented as an effective treatment for individuals with intense emotions. A comprehensive family model that addresses family systems utilizing DBT has yet to be developed. This article presents a model for DBT for a family system that goes beyond serving the individual and utilizing the family system for support. The theoretical concepts of DBT named “family systems (FS)DBT” are reviewed. The author reviews the current literature of DBT, how (FS)DBT addresses the dialectic of acceptance and change, how the complex model of individualized therapy such as DBT could be systematically applied to a complex and changing family system through psychoeducation, and the remaining phases of (FS)DBT. This proposed comprehensive model for doing (FS)DBT will have significant implications for clinicians working directly with complex FS that have multiple family members with intense emotions.

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application.

A regularisation technique to precisely infer limb darkening using transit measurements: Can we estimate stellar surface magnetic fields?

Abstract The high-precision measurements of exoplanet transit light curves that are now available contain information about the planet properties, their orbital parameters, and stellar limb darkening (LD). Recent 3D magneto-hydrodynamical (MHD) simulations of stellar atmospheres have shown that LD depends on the photospheric magnetic field, and hence its precise determination can be used to estimate the field strength. Among existing LD laws, the uses of the simplest ones may lead to biased inferences, whereas the uses of complex laws typically lead to a large degeneracy among the LD parameters. We have developed a novel approach in which we use a complex LD model but with second derivative regularisation during the fitting process. Regularisation controls the complexity of the model appropriately and reduces the degeneracy among LD parameters, thus resulting in precise inferences. The tests on simulated data suggest that our inferences are not only precise but also accurate. This technique is used to re-analyse 43 transit light curves measured by the NASA Kepler and TESS missions. Comparisons of our LD inferences with the corresponding literature values show good agreement, while the precisions of our measurements are better by up to a factor of 2. We find that 1D non-magnetic model atmospheres fail to reproduce the observations while 3D MHD simulations are qualitatively consistent. The LD measurements, together with MHD simulations, confirm that Kepler-17, WASP-18, and KELT-24 have relatively high magnetic fields (&amp;gt;200 G). This study paves the way for estimating the stellar surface magnetic field using the LD measurements.

Radiomics in the differential diagnosis of focal brain lesions: a retrospective study

INTRODUCTION: Glioblastoma and solitary metastases are the most common malignant neoplasms of the brain, characterized by high mortality and severe disability in patients. The method of choice for neuroimaging glioblastomas and metastases is contrast-enhanced magnetic resonance imaging. However, differentiation between the two is often difficult due to similar radiological features on MRI. Radiomics and machine learning can differentiate the primary origin of brain metastases and identify pathological tumor types noninvasively.OBJECTIVE: Application of texture analysis for differential diagnosis of glioblastomas and metastases of different etiologies.MATERIALS AND METHODS: 169 MRI studies from the RSCRR database were used in the study, 11 of which visualized morphologically differentiated glioblastoma of the brain, 55 lung cancer metastases and 103 breast cancer metastases. Segmentation of the regions of interest was performed semi-automatically in the free 3D-Slicer software with the ability to upload radiomic features from the regions of interest. For each lesion, 107 radiomic features were calculated from T1 and T2 sequences. Statistics: The calculation of statistical indicators was performed in a computer program for statistical data processing IBMSPSS Statistics 23. In statistical data processing, the Mann-Whitney statistical criterion for quantitative indicators and correlation analysis using the Pearson criterion were used to reduce the feature space. The reduction of the feature space and the selection of predictors by the feature_importances measure based on decision forests were carried out. Machine learning models were built in Python 3.10 using specialized libraries.RESULTS: For the model based on radiomic features extracted from T1 sequence, random forest showed the most efficient result, ROC-AUC=0.815 [0.749; 0.874]. For the model based on the radiomic features extracted from the T2 sequence, random forest showed the most effective result, ROC-AUC=0.817 [0.743; 0.873]. For the complex model based on radiomic features extracted from T1 and T2 sequences, random forest showed the most efficient result, ROC-AUC=0.855 [0.789; 0.906].DISCUSSION: The classification models and their metrics obtained by us indicate that the radiomic features extracted from T2 weighted MR images make it possible to differentiate breast cancer metastases from lung cancer metastases with higher sensitivity than the features extracted from T1 weighted MR images. We also identified a large number of significantly different indicators in the construction of models for the differentiation of glioblastomas from metastases, which demonstrates the prospects of this direction. It is planned to continue the study with the expansion of samples. Our conclusions are also confirmed by the research results of our foreign colleagues.CONCLUSION: The models we have obtained are highly accurate and sensitive to the differentiation of metastases of various etiologies and demonstrate significant potential in continuing this study with an expansion of samples.

Inference of Phylogenetic Networks from Sequence Data using Composite Likelihood.

While phylogenies have been essential in understanding how species evolve, they do not adequately describe some evolutionary processes. For instance, hybridization, a common phenomenon where interbreeding between two species leads to formation of a new species, must be depicted by a phylogenetic network, a structure that modifies a phylogenetic tree by allowing two branches to merge into one, resulting in reticulation. However, existing methods for estimating networks become computationally expensive as the dataset size and/or topological complexity increase. The lack of methods for scalable inference hampers phylogenetic networks from being widely used in practice, despite accumulating evidence that hybridization occurs frequently in nature. Here, we propose a novel method, PhyNEST (Phylogenetic Network Estimation using SiTe patterns), that estimates binary, level-1 phylogenetic networks with a fixed, user-specified number of reticulations directly from sequence data. By using the composite likelihood as the basis for inference, PhyNEST is able to use the full genomic data in a computationally tractable manner, eliminating the need to summarize the data as a set of gene trees prior to network estimation. To search network space, PhyNEST implements both hill climbing and simulated annealing algorithms. PhyNEST assumes that the data are composed of coalescent independent sites that evolve according to the Jukes-Cantor substitution model and that the network has a constant effective population size. Simulation studies demonstrate that PhyNEST is often more accurate than two existing composite likelihood summary methods (SNaQ and PhyloNet) and that it is robust to at least one form of model misspecification (assuming a less complex nucleotide substitution model than the true generating model). We applied PhyNEST to reconstruct the evolutionary relationships among Heliconius butterflies and Papionini primates, characterized by hybrid speciation and widespread introgression, respectively. PhyNEST is implemented in an open-source Julia package and is publicly available at https://github.com/sungsik-kong/PhyNEST.jl.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.