Multilevel Data Research Articles

Multi-tissue proteogenomic analysis for mechanistic toxicology studies in non-model species

New approach methodologies (NAM), including omics and in vitro approaches, are contributing to the implementation of 3R (reduction, refinement and replacement) strategies in regulatory science and risk assessment. In this study, we present an integrative transcriptomics and proteomics analysis workflow for the validation and revision of complex fish genomes and demonstrate how proteogenomics expression matrices can be used to support multi-level omics data integration in non-model species in vivo and in vitro. Using Atlantic salmon as an example, we constructed proteogenomic databases from publicly available transcriptomic data and in-house generated RNA-Seq and LC-MS/MS data. Our analysis identified ∼80,000 peptides, providing direct evidence of translation for over 40,000 RefSeq structures. The data also highlighted 183 co-located peptide groups that supported a single transcript each, and in each case, either corrected a previous annotation, supported Ensembl annotations not present in RefSeq, or identified novel previously unannotated genes. Proteogenomics data-derived expression matrices revealed distinct profiles for the different tissue types analyzed. Focusing on proteins involved in defense against xenobiotics, we detected distinct expression patterns across different salmon tissues and observed homology in the expression of chemical defense proteins between in vivo and in vitro liver systems. Our study demonstrates the potential of proteogenomic analyses in extending our understanding of complex fish genomes and provides an advanced bioinformatic toolkit to support the further development of NAMs and their application in regulatory science and (eco)toxicological studies of non-model species.

A computationally efficient sequential regression imputation algorithm for multilevel data

ABSTRACT Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the missing data from their perspective predictive distributions, which leads to significantly improved computation time in the class of variable-by-variable imputation algorithms. We assess the computational performance in a comprehensive simulation study. We then compare and contrast the performance of our algorithm with commonly used alternatives. The results show that our method has a significant advantage over the commonly used alternatives with respect to computational efficiency and inferential quality. Finally, we demonstrate our methods in a substantive problem aimed at investigating the effects of area-level behavioral, socioeconomic, and demographic characteristics on poor birth outcomes in New York State among singleton births.

In God We Distrust: Christian Nationalism and Anti‐Atheist Attitude

AbstractPrior research found that Christian nationalism, a belief that integrates Christian identity with American national identity, was associated with more negative views toward marginalized groups, such as religious minorities. Relatively less known is the relationship between Christian nationalism and attitudes toward atheists. Specifically, even less is known about whether or not Christian nationalism that is above individuals at the macro, geographic level may influence individual views of atheists. Using nationally representative multilevel data, this study found that higher individual Christian nationalism was associated with more negative views toward atheists. No robust state‐level Christian nationalism influence was found for anti‐atheist attitude. However, Blacks, relative to Whites, were less likely to view atheists negatively in states where Christian nationalism was higher. Meanwhile, in states where Christian nationalism was higher, Black Protestants were less prejudiced against atheists compared to Evangelical Protestants.

A new fatigue cumulative damage model based on material parameters and stress interaction

In order to evaluate the fatigue damage accumulation process of materials under different stress levels and to predict the fatigue lives, a new fatigue accumulation damage model based on material parameters and stress interaction is proposed. The parameters of the material required for the action coefficient introduced by the proposed model are the slope of the S-N characteristic curve and the ultimate stress. In addition, the fatigue cumulative damage of the previous level and the ratio of adjacent stresses are also taken into consideration. Besides, we have conducted extensive experiments by utilizing the two-level stress experimental data of steel 300CVM, 16Mn steel, LY12CZ, and AL-2024-T42, the three-level stress experimental data of LY12CZ material, the four-level and five-level stress experimental data of Aluminum alloy material, respectively. Especially, we calculate and compare the fatigue prediction results of our proposed model with those of the 6 existing models, i.e., Manson-Halford model, YG model, Yue model, Haghgouei model, Subramanyan model and Hashin model. These materials used for verification, in addition to the fatigue test data of high cycle lives, also include the combination of high cycle and low cycle multi-level stress test data. Finally, we compare the cumulative fatigue life of each model via two-level to five-level stress tests, and calculate the difference between the fatigue damage prediction results and the test results to evaluate the advantage of the proposed model. The predicted results show that the model proposed in this paper is in great agreement with the real testing results. Besides, the prediction effect of the proposed model is significantly superior to that of the existing 6 models, especially when both the fatigue low-cycle and high-cycle mixed stresses are used, which validates the high robustness of the proposed model under various complex stress conditions.

Photoelectric biomemristors for artificial visual perception systems

The redundant information generated by traditional image sensors causes a large amount of energy waste, which can no longer meet the needs of the rapid development of current artificial intelligence. Therefore, it is urgent to develop visual bionic systems with perception, memory and processing functions. Photoelectric memristors based on graphene quantum dots (GQDs) and the body fluid of Bombyx mori (BM) were fabricated, which can change its storage state when triggered by optical or electrical signals, and synaptic characteristics such as multilevel data storage, synaptic potentiation and synaptic depression were realized. The Boolean logic "OR" operation, the enhancement/inhibition of the synapse under the light signal and the double pulse dissimilation behavior are realized by UV light-driven resistance variable memory. The results show that photoelectric memristors have attractive biological applications in information perception, storage and computation and provide a potential method for the next generation of artificial visual perception systems.

A Framework for Analyzing Individual-Tree and Whole-Stand Growth by Fusing Multilevel Data: Stochastic Differential Equation and Copula Network

In forestry, growth functions form the basis of research and are widely used for the mathematical modeling of stand variables, e.g., tree or stand basal area, stand height, stand volume, site index, and many more. In this study, to estimate five-dimensional dependencies between tree diameter at breast height, potentially available area, height, crown area and crown base height, we used a normal copula approach whereby the growths of individual variables are described using a stochastic differential equation with mixed-effect parameters. The normal copula combines the marginal distributions of tree diameter at breast height, potentially available area, height, crown area, and crown base height into a joint multivariate probability distribution. Copula models have the advantage of being able to use collected longitudinal, multivariate, and discrete data for which the number of measurements of individual variables does not match. This study introduced a normalized multivariate interaction information measure based on differential entropy to assess the causality between tree size variables. In order to accurately and quantitatively assess the stochastic processes of the tree size variables’ growth and to provide a scientific basis for the formalization of models, an analysis method of the synergetic theory of information entropy has been proposed. Theoretical findings are illustrated using an uneven-aged, mixed-species empirical dataset of permanent experimental plots in Lithuania.

Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness

International business research often links the cultural and institutional characteristics of countries to the features of the individuals inhabiting these countries. A distinct approach to analyzing such multilevel problems with deep learning and explainable artificial intelligence methods is presented, using country characteristics as explicit spatial coordinates. Deep learning is tolerant of noise and faults and can approximate arbitrarily complex mathematical structures by developing multiple abstractions. An applied example demonstrates the applicability of this approach by exploring the effect of personal-care advertising spending in 27 countries on the subjective happiness of 376,442 individuals, indicating a statistically significant positive effect, albeit with a trivial effect size.

High-Quality Circularly Polarized Organic Afterglow from Nonconjugated Amorphous Chiral Copolymers.

Organic materials featuring circularly polarized luminescence (CPL) and/or afterglow emission represent an active research frontier with promising applications in various fields, but the achievement of high-performance CPL organic afterglow (CPOA) remains a huge challenge due to the intrinsic contradictions between the luminescent lifetime/dissymmetry factor (glum) and phosphorescent quantum efficiency (PhQY). Herein, we report a simple and universal approach to design efficient CPOA from amorphous copolymers by incorporating chiral chromophores into a nonconjugated clusterization-triggered emissive polymer with plenty of hydron-bonding interactions, followed by aggregation engineering using water dissolution and evaporation. With this chiral copolymerization and aggregation engineering (CCAE) strategy, high-performance CPOA polymers with PhQYs of up to 6.32%, ultralong lifetimes of over 650 ms, glum values of 3.54 × 10-3, and the highest figure-of-merit were achieved at room temperature. Given the impressive CPOA performance of these polymers, the applications in multilevel data anticounterfeiting and reversible displays with high stability were demonstrated. These findings through the CCAE strategy to overcome the inherent restraints of CPOA materials lay the foundation for the development of amorphous polymers with superior CPOA, significantly expanding the understanding of CPL and the design of organic afterglow materials.

Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity

BackgroundIn medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights.MethodsTo analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them.ResultsA numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model.ConclusionThe method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

Do Victims Really Help Their Abusive Supervisors? Reevaluating the Positive Consequences of Abusive Supervision.

Do victims really help their abusive supervisors? Does abusive supervision have any positive consequence? The study aims to address this concern through extending the work by Tröster and Van Quaquebeke (2021). Using subordinates' self-reports, Tröster and Van Quaquebeke (2021) found that abusive supervision in high-quality leader-member exchange (LMX) relationship motivates subordinates to blame themselves, subsequently making them feel guilty and make up for it by being more helpful. By integrating both subordinates' and supervisors' perspectives, and using multi-wave, multi-source, and multi-level data collected in China, we obtain three major findings. First, as a replication of their findings, LMX moderates the direct effect of abusive supervision on workplace self-blame, and the indirect effect of abusive supervision on workplace guilt via workplace self-blame. The positive direct and indirect effects are stronger when LMX quality is higher. Second, different from their findings, LMX moderates the indirect effect of abusive supervision on supervisor-directed helping (evaluated by supervisors) via workplace self-blame and workplace guilt such that the negative indirect effect is stronger when LMX quality is higher. Third, as an extension, supervisor-evaluated LMX (SLMX) moderates the effect of workplace guilt on supervisor-directed helping such that the negative effect is stronger when SLMX is lower-quality. Put together, LMX and SLMX moderate the indirect effect of abusive supervision on supervisor-directed helping via workplace self-blame and workplace guilt. The negative indirect effect is stronger when LMX quality is higher, but SLMX quality is lower. Our study challenges previous speculations on the positive or beneficial consequences of abusive supervision, and thus contributes to the literature on abusive supervision.

Corporate sustainability entrepreneurship: The role of green entrepreneurial orientation and organizational resilience capacity for green innovation

Our study researches the antecedents and outcomes of sustainability entrepreneurship in established firms. Specifically, we investigate the antecedents of green innovation and the specific role that a green entrepreneurial orientation plays in a firm’s green innovation. Using multi-level and multi-source data from 197 manufacturing firms in Pakistan, our findings reveal that technical orientation and environmental collaboration drive green entrepreneurial orientation and organizational resilience capacity which in turn support green innovation. Our study extends the entrepreneurial orientation concept to the sustainability context and contributes to a new line of research in corporate entrepreneurship. Further, we investigate these corporate entrepreneurial phenomena within an under-researched, emerging country context in which we provide nuanced insights on mechanisms for developing sustainable and resilient initiatives to achieve green innovation in resource-constrained environments.

A dataset of dryland ground temperature in typical small watersheds in the hilly areas of Central Sichuan Basin (1997-2021)

The underlying surface temperature (i.e. land surface temperature) and soil temperature at different depths are collectively referred to as ground temperature. The variations and variation rules of ground temperature comprehensively depict the exchange of energy and materials between the atmosphere and the land surface. Hence ground temperature is an important parameter for assessing soil thermal conditions. Located in the agro-ecological zone of subtropical Sichuan Basin, Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network (also referred to as Yanting Station, code YGA) serves as an experimental observation and research platform of Agro-Ecosystem set by the Chinese Ecosystem Research Network (CERN) and the National Science and Technology Infrastructure Platform in this ecological region. This dataset is based on the long-term automatic station observation data and manual observation data of the comprehensive meteorological observation field of Yanting Station. It consists of four data files, namely, hourly ground temperature data from the automatic observation stations on a daily basis (1997-2021), daily ground temperature statistical data from the automatic observation stations (2004-2021), monthly statistical data of ground temperature from the automatic observation stations (2004-2021) and daily and monthly surface temperature data form the manual observation stations (2004-2021). The data volume is about 8.85 MB. Ground temperature is the networked observation index of field stations. Observation instruments, data acquisition procedures, and data quality control adhere to standardized norms and protocols, ensuring the utmost reliability and comparability of ground temperature data. The accumulation of long time series and multi-level geothermal data can provide scientific data support for the energy and material exchange of ecosystem, the response to environmental change, and the application research of cultivated land quality assessment and food security in the main agricultural areas of the basin.

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.

LogBASA: Log Anomaly Detection Based on System Behavior Analysis and Global Semantic Awareness

System log anomaly detection is important for ensuring stable system operation and achieving rapid fault diagnosis. System log sequences include data on the execution paths and time stamps of system tasks in addition to a large amount of semantic information, which enhances the reliability and effectiveness of anomaly detection. At the same time, considering the correlation between system log sequences can effectively improve fault diagnosis efficiency. However, the existing system log anomaly detection methods mostly consider only the sequence patterns or semantic information on the logs, so their anomaly detection results show a high rate of missed and false alarms. To solve these problems, this paper proposed an unsupervised log anomaly detection model (LogBASA) based on the system behavior analysis and global semantic awareness, aiming to decrease the leakage rate and increase the log sequence anomaly detection accuracy. First, a system log knowledge graph was constructed based on massive, unstructured, and multilevel system log data to represent log sequence patterns, which facilitates subsequent anomaly detection and localization. Then, a self-attention encoder-decoder transformer model was developed for log spatiotemporal association analysis. This model combines semantic mapping and spatiotemporal features of log sequences to analyze system behavior and log semantics in multiple dimensions. Furthermore, a system log anomaly detection method that combines adaptive spatial boundary delineation and sequence reconstruction objective functions was proposed. This method uses special words to characterize the log sequence states, delineates anomaly boundaries automatically, and reconstructs log sequences through unsupervised training for anomaly detection. Finally, the proposed method was verified by numerous experiments on three real datasets. The results indicate that the proposed method can achieve an accuracy rate of 99.3%, 95.1%, and 97.2% on HDFS, BGL, and Thunderbird datasets, which proves the effectiveness and superiority of the LogBASA model.

A comparison study of environmental health related issues and policies between private and public U.S. schools using multi-level data

ObjectiveLittle is known about school environmental health (EH) problems in private schools. This study identified and compared the school EH related issues and policies between private and public schools in the U.S. MethodsSchool EH information was collected from school administrators on two levels – in New York State (NYS) and nationwide. In NYS, questionnaires were developed and distributed to administrators from both public and private schools via conferences (2015–2016). Nationwide survey data were obtained from the 2014 School Health Policies and Practices Study. We compared the perceived school environment as well as policies related to identification and training on EH issues between private and public schools both in NYS and nationwide. ResultsIn NYS, both private and public schools reported limited use of proactive approaches in identifying EH issues. Compared to public schools, more private school respondents reported poorer perceived indoor air quality (IAQ) (30% vs. 0%), along with less routine training and lower use of policies that help improve air quality in school. Nationwide, fewer private schools implemented indoor air quality management programs (18% vs. 58%) and were able to always meet ventilation standards (44% vs. 64%) than public schools. Staff members in private schools received less required training on a wide range of EH issues than staff members in public schools. ConclusionU.S. private schools reported worse perceived indoor air quality than public schools and a lack of awareness and protective policies. Future environmental research and implementation of policies are needed in private schools.

Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models?

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether—and to what extent—the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals’ autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices

Diverse long-term potentiation and depression based on multilevel LiSiO x memristor for neuromorphic computing

Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiO x /TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiO x memristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiO x /TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.

ESMAC: Efficient and Secure Multi-Owner Access Control With TEE in Multi-Level Data Processing

Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.

Crystallization behavior of MnTe/GeTe stacked thin films for multi-level phase change memory

With substantial data storage requirements, enhancing the storage density of emerging non-volatile memories is a common challenge for achieving commercial applications. Phase change memory is capable of multi-level data storage in a storage cell, which can be applied in neural-inspired and all-photonic memory computing. In this study, we propose a MnTe/GeTe stacked thin film structure suitable for multi-level phase change memories and systematically study on the correlation between structure and performance. As the temperature rises, the stacked thin film sequentially crystallizes into form MnTe2, GeTe and Te phases, resulting in three changes in resistance. By utilizing different resistance states, the phase change memory cell based on the MnTe (15 nm)/GeTe (35 nm) stacked thin film can achieve multi-level storage with a 50 ns electrical pulse. The results show that the amorphous thermal stability of the stacked thin films is improved and that the minimal thickness variation before and after crystallization enhances its reliability. Therefore, the MnTe/GeTe stacked thin film is a phase change memory material with favorable structural properties and potential for multi-level storage applications.

Modeling (in)congruence in groups: A how-to guide for applying polynomial regression and response surface method to multilevel data.

Modeling (in)congruence in groups: A how-to guide for applying polynomial regression and response surface method to multilevel data.