Empirical Data Sets Research Articles

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing.

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which, in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogeneous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

Filtering higher-order datasets

Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset’s structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

BackgroundThe accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program.ResultsOur results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.ConclusionsThe dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference

This study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions. It considers a risk- and ambiguity-averse investor allocating resources to a risk-free asset, a market index, a green stock, and a brown stock. The study employs a robust control approach rooted in relative entropy to account for model misspecification and derive closed-form optimal investment strategies. The key contribution of this study includes demonstrating, using two sets of empirical data on asset returns and ESG ratings, the substantial influence of ambiguity on optimal trading strategies, particularly highlighting the differential effects of market, green, and brown ambiguities. As a by-product of our analytical solutions, the study contrasts ambiguity-averse investors with their non-ambiguity counterparts, revealing more cautious risk exposures with a reduction in short-selling positions for the former. Furthermore, three types of investors who employ popular suboptimal strategies are identified, together with two loss measures used to quantify their performance. The findings reveal that popular strategies, not accounting for ESG and misspecification in the model, could lead to significant financial costs, with the extent of loss varying depending on those two factors: investors’ ambiguity aversion profiles and ESG preferences.

“Swimming Against the Current” of Demutualizations in the 1990s and 2000s: The Case of the Private Statutory Pension Insurance Sector in Finland

The global insurance industry of the 1990s and 2000s witnessed a significant wave of demutualizations often regarded as a world-wide phenomenon. In this article, we describe the case of the Finnish private statutory pension insurance sector and the reasons why it eventually “swam against the current” by explicitly deciding to emphasize mutuals —that still today account for 96% of the particular sector in Finland and has ensured that the almost 150 billion euros of pension assets has stayed in control and in use of the Finnish people, organizations and entrepreneurs. As an empirical dataset, we analyze two interviews of high-level experts who participated in the working group drafting the guidelines for the Finnish Act on Pension Insurance Companies.Received: 28 April 2023 Accepted: 20 December 2023

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BackgroundDue to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex.ResultsWe tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer’s disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei.ConclusionsOur novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

Gasoline quality prediction using the alternating conditional expectation (ACE) algorithm and ATR-FTIR

Accurate estimation of oxygenates is a critical issue in the quality evaluation of gasoline samples. This work aims to examine the nonparametric robust principal component analysis-alternating conditional expectation (rPCA-ACE) algorithm combined with FTIR spectroscopy as a rapid and accurate analytical method for predicting the quality of gasoline samples based on oxygenates content (methanol, methyl tert-butyl ether, and isobutanol). In the ACE algorithm, a set of optimal transformations is estimated for both the independent and dependent variables. These transformations reveal their non-linear relationships and generate a maximum linear effect between the transformed independent variables and the transformed response variable. In this study, the ACE algorithm was applied to an empirical gasoline dataset and considered a series of possible transformations of the independent and dependent variables to find the best transformations. Among all possible transformations, the ACE algorithm identified a series of polynomials and a nearly linear transformation as the best transformations for the independent and dependent variables, respectively. The regression statistics for calibration and prediction, including the correlation coefficient (Rcal2 = 0.9692), root mean square error of calibration (RMSEC = 2.8638), and root mean square error of prediction (RMSEP = 4.0498) (%v/v) for oxygenates content, were calculated. The ACE model showed improved regression results compared to the linear PLS model (Rcal2 = 0.9550, RMSEC = 3.9052, RMSEP = 5.1342) and PCR model (Rcal2 = 0.9160, RMSEC = 6.5330, RMSEP = 7.0270). By applying the ACE technique to the synthetic fully non-linear dataset obtained from the equation y′=exp(y) for the response variable, we demonstrated the power of the ACE algorithm in multivariate analysis and its ability to identify the exact functional relationship between independent and dependent variables to solve fully non-linear problems.

Analyzing housing supply location choice: a comparative study of the modelling frameworks.

The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation's housing supply model configuration.

Hypergraph patterns and collaboration structure

Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly formed collaboration may include people that have collaborated before in the past, and people with no collaboration history. Such existing relationships between team members have been reported to influence the performance of teams. However, it is not clear how existing relationships between team members should be quantified, and whether some relationships are more likely to occur in new collaborations than others. Here we introduce a new family of structural patterns, m-patterns, which formalize relationships between collaborators and we study the prevalence of such structures in data and a simple random-hypergraph null model. We analyze the frequency with which different collaboration structures appear in our null model and show how such frequencies depend on size and hyperedge density in the hypergraphs. Comparing the null model to data of human and non-human collaborations, we find that some collaboration structures are vastly under- and overrepresented in empirical datasets. Finally, we find that structures of scientific collaborations on COVID-19 papers in some cases are statistically significantly different from those of non-COVID-19 papers. Examining citation counts for 4 different scientific fields, we also find indications that repeat collaborations are more successful for 2-author scientific publications and less successful for 3-author scientific publications as compared to other collaboration structures.

Performance of tree-building methods using a morphological dataset and a well-supported Hexapoda phylogeny.

Recently, many studies have addressed the performance of phylogenetic tree-building methods (maximum parsimony, maximum likelihood, and Bayesian inference), focusing primarily on simulated data. However, for discrete morphological data, there is no consensus yet on which methods recover the phylogeny with better performance. To address this lack of consensus, we investigate the performance of different methods using an empirical dataset for hexapods as a model. As an empirical test of performance, we applied normalized indices to effectively measure accuracy (normalized Robinson-Foulds metric, nRF) and precision, which are measured via resolution, one minus Colless' consensus fork index (1-CFI). Additionally, to further explore phylogenetic accuracy and support measures, we calculated other statistics, such as the true positive rate (statistical power) and the false positive rate (type I error), and constructed receiver operating characteristic plots to visualize the relationship between these statistics. We applied the normalized indices to the reconstructed trees from the reanalyses of an empirical discrete morphological dataset from extant Hexapoda using a well-supported phylogenomic tree as a reference. Maximum likelihood and Bayesian inference applying the k-state Markov (Mk) model (without or with a discrete gamma distribution) performed better, showing higher precision (resolution). Additionally, our results suggest that most available tree topology tests are reliable estimators of the performance measures applied in this study. Thus, we suggest that likelihood-based methods and tree topology tests should be used more often in phylogenetic tree studies based on discrete morphological characters. Our study provides a fair indication that morphological datasets have robust phylogenetic signal.

Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends

The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies.

The impact of COVID-19 related flight reductions on bird prevalence and behaviour at Manchester Airport, UK, and the implications for airport management

Airport management is a complex and multifarious activity, involving many operators including airlines, retailers and ground handlers, and processes. The presence of wildlife at airports poses a safety risk to aircraft operations and as such managing wildlife hazards is a mandatory legal responsibility. This is important not only from a safety perspective but also from the fact that safety incidents can impact the operational efficiency and the reputation of an airport. Airport operators are required to devise and enact site-specific Wildlife Hazard Management Plans (WHMP) to reduce the risk of aircraft-wildlife interaction under normal airport operating conditions. The COVID-19 pandemic, however, led to an unprecedented reduction in commercial air traffic and the partial or total suspension of flights at some airports. The aim of this paper is to examine the impact of COVID-19 related flight reductions on bird prevalence and behaviour and the potential implications for airport management. Drawing on an empirical dataset of wildlife observations at Manchester Airport, UK, in 2019 and 2020, this paper details the airfield ornithology before and during the pandemic and examines the impact of COVID-19 related flight reductions on bird prevalence and behaviour. The findings reveal variations in the frequency and apparency of individual species as well as changes in the spatial location of bird sightings on the airfield. The paper concludes by discussing the implications of these findings for post-pandemic operations and for the formulation of future airport wildlife hazard management policies.

Cost-asymmetric memory hard password hashing

In the past decade billions of user passwords have been exposed to the dangerous threat of offline password cracking attacks. An offline attacker who has stolen the cryptographic hash of a user's password can check as many password guesses as s/he likes limited only by the resources that s/he is willing to invest to crack the password. Pepper and key-stretching are two techniques that have been proposed to deter an offline attacker by increasing guessing costs. Pepper ensures that the cost of rejecting an incorrect password guess is higher than the (expected) cost of verifying a correct password guess. This is useful because most of the offline attacker's guesses will be incorrect. Unfortunately, as we observe the traditional peppering defense seems to be incompatible with modern memory hard key-stretching algorithms such as Argon2 or Scrypt. We introduce an alternative to pepper which we call Cost-Asymmetric Memory Hard Password Authentication which benefits from the same cost-asymmetry as the classical peppering defense i.e., the cost of rejecting an incorrect password guess is larger than the expected cost to authenticate a correct password guess. When configured properly we prove that our mechanism can only reduce the percentage of user passwords that are cracked by a rational offline attacker whose goal is to maximize (expected) profit i.e., the total value of cracked passwords minus the total guessing costs. We evaluate the effectiveness of our mechanism on empirical password datasets against a rational offline attacker. Our empirical analysis shows that our mechanism can reduce the percentage of user passwords that are cracked by a rational attacker by up to 10%.

Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values.

Best linear unbiased prediction (BLUP) is widely used in plant research to address experimental variation. For phenotypic values, BLUP accuracy is largely dependent on properly controlled experimental repetition and how variable components are outlined in the model. Thus, determining BLUP robustness implies the need to evaluate contributions from each repetition. Here, we assessed the robustness of BLUP values for simulated or empirical phenotypic datasets, where the BLUP value and each experimental repetition served as dependent and independent (feature) variables, respectively. Our technique incorporated machine learning and partial dependence. First, we compared the feature importance estimated with the neural networks. Second, we compared estimated average marginal effects of individual repetitions, calculated with a partial dependence analysis. We showed that contributions of experimental repetitions are unequal in a phenotypic dataset, suggesting that the calculated BLUP value is likely to be influenced by some repetitions more than others (such as failing to detect simulated true positive associations). To resolve disproportionate sources, variable components in the BLUP model must be further outlined.

PVS-GEN: Systematic Approach for Universal Synthetic Data Generation Involving Parameterization, Verification, and Segmentation.

Synthetic data generation addresses the challenges of obtaining extensive empirical datasets, offering benefits such as cost-effectiveness, time efficiency, and robust model development. Nonetheless, synthetic data-generation methodologies still encounter significant difficulties, including a lack of standardized metrics for modeling different data types and comparing generated results. This study introduces PVS-GEN, an automated, general-purpose process for synthetic data generation and verification. The PVS-GEN method parameterizes time-series data with minimal human intervention and verifies model construction using a specific metric derived from extracted parameters. For complex data, the process iteratively segments the empirical dataset until an extracted parameter can reproduce synthetic data that reflects the empirical characteristics, irrespective of the sensor data type. Moreover, we introduce the PoR metric to quantify the quality of the generated data by evaluating its time-series characteristics. Consequently, the proposed method can automatically generate diverse time-series data that covers a wide range of sensor types. We compared PVS-GEN with existing synthetic data-generation methodologies, and PVS-GEN demonstrated a superior performance. It generated data with a similarity of up to 37.1% across multiple data types and by 19.6% on average using the proposed metric, irrespective of the data type.

Human Factors on the Road: Truck Drivers’ Heterogeneity in Distribution

While human factors in warehousing have received considerable attention in recent years, they remain largely underexamined in transportation. This is surprising, given that truck driver shortage and load space scarcity are recent key challenges in the logistics industry that significantly hamper the growth potential of European economies. While increasing demand for loadspace meets a decreasing availability of professional truck drivers, managers across all industries need to think about how scarce human resources can be deployed more efficiently. Therefore, the goal of this study is to quantify truck drivers’ heterogeneity in retail distribution. We formulate and apply a mixed-effect multilevel regression model where delivery routes are nested within truck drivers. The model is applied to a unique empirical dataset, including N = 51,164 routes performed by 218 truck drivers in a six-month time frame in 2021, obtained in collaboration with a German brick-and-mortar grocery retailer. We find that truck drivers’ heterogeneity accounts for 29.9% of the total route duration time variation. This impact is significantly higher compared to existing empirical research on the quantification of heterogeneity for warehouse workers and proposes a higher potential for transport managers to increase efficiency in collection and distribution by taking into account driver heterogeneity in tour planning and allocation.

An Illustration of a Latent Class Analysis for Interrater Agreement: Identifying Subpopulations with Different Agreement Levels

This study proposes a latent class analysis (LCA) approach to investigate interrater agreement based on rating patterns. LCA identifies which subjects are rated similarly or differently by raters, providing a new perspective for investigating agreement. Using an empirical dataset of parents and teachers evaluating pupils, the study found two latent classes of respondents, one belonging to a moderate agreement pattern and one belonging to low agreement pattern. We calculated raw agreement coefficient (RAC) per behaviour in the whole sample and each latent class. When RAC was calculated in the whole sample, many behaviour had low/moderate RAC values. However, LCA showed that these items had higher RAC values in the high agreement and lower RAC values in the low agreement class.

Development of a seismic vulnerability and risk model for typical bridges considering innovative intensity measures

Seismic intensity measures are one of the core parameters for estimating regional bridges’ seismic risk and vulnerability. Using macroscopic seismic intensity indicators to predict and assess the seismic vulnerability of bridges can contribute positively to establishing urban and rural earthquake risk models. However, the developed vulnerability model rarely considers the contribution of instrument intensity to its quantification results and ignores the influence of nondamage bridge samples on the development of bridge risk models and the membership relationship between seismic vulnerability levels, which makes the developed model somewhat localized and uncertain. This study proposes a quantitative method and innovative model considering composite seismic intensity (instrumental and macroseismic intensity) indicators. The model was updated and validated using 300,000 acceleration records monitored by ten seismic stations during the Luding earthquake on September 5, 2022, in China, and the features of seismic motion parameters were analysed. The established empirical vulnerability dataset of bridges during the 2008 Wenchuan earthquake in China was updated and optimized using the developed models and methods. The traditional vulnerability level of bridges has been expanded by adding and refining actual samples (2317 bridges) from different seismic intensity zones. A vulnerability prediction model based on an updated extended bridge database was developed using nonlinear regression methods. A fragility membership index calculation model for estimating the correlation features of bridge vulnerability levels is proposed by combining the probability risk and fuzzy membership correlation algorithm. The traditional method for calculating the seismic vulnerability index has been improved, and a multiparameter bridge vulnerability model validation and rationality analysis have been conducted using the newly developed bridge sample dataset.

When cheating turns into a stabilizing mechanism of plant-pollinator communities.

Mutualistic interactions, such as plant-mycorrhizal or plant-pollinator interactions, are widespread in ecological communities and frequently exploited by cheaters, species that profit from interactions without providing benefits in return. Cheating usually negatively affects the fitness of the individuals that are cheated on, but the effects of cheating at the community level remains poorly understood. Here, we describe 2 different kinds of cheating in mutualistic networks and use a generalized Lotka-Volterra model to show that they have very different consequences for the persistence of the community. Conservative cheating, where a species cheats on its mutualistic partners to escape the cost of mutualistic interactions, negatively affects community persistence. In contrast, innovative cheating occurs with species with whom legitimate interactions are not possible, because of a physiological or morphological barrier. Innovative cheating can enhance community persistence under some conditions: when cheaters have few mutualistic partners, cheat at low or intermediate frequency and the cost associated with mutualism is not too high. Under these conditions, the negative effects of cheating on partner persistence are overcompensated at the community level by the positive feedback loops that arise in diverse mutualistic communities. Using an empirical dataset of plant-bird interactions (hummingbirds and flowerpiercers), we found that observed cheating patterns are highly consistent with theoretical cheating patterns found to increase community persistence. This result suggests that the cheating patterns observed in nature could contribute to promote species coexistence in mutualistic communities, instead of necessarily destabilizing them.

Decarbonisation through modernisation: The UK’s EV transition as a vehicle for industrial change

As the transport sector remains the only sector in the United Kingdom (UK) in which emissions continue to rise, electric vehicles (EVs) have become an increasingly prominent political subject. This article finds that far from simply a low carbon transition, the EV transition in the UK is a means to address the attendant frailties of its economic model. Drawing upon an original empirical dataset of 33 interviews alongside a document analysis, I show that decarbonising the UK automobile sector is tied to a process of industrial and economic modernisation. These findings reveal the contested features of the transition, as the desire to capitalise on broader shifts in the global automobile market is brought into opposition with Britain’s financialised political economy. Finally, it shows that the modernisation tenet of Ecological Modernisation, which has come to be routinely neglected in the literature, should be revisited in the context of Brexit, COVID-19 and economic crises.