Large Datasets Research Articles

Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen

Copy number variation (CNV) can drive rapid evolution in changing environments. In microbial pathogens, such adaptation is a key factor underpinning epidemics and colonization of new niches. However, the genomic determinants of such adaptation remain poorly understood. Here, we systematically investigate CNVs in a large genome sequencing dataset spanning a worldwide collection of 1104 genomes from the major wheat pathogen Zymoseptoria tritici. We found overall strong purifying selection acting on most CNVs. Genomic defense mechanisms likely accelerated gene loss over episodes of continental colonization. Local adaptation along climatic gradients was likely facilitated by CNVs affecting secondary metabolite production and gene loss in general. One of the strongest loci for climatic adaptation is a highly conserved gene of the NAD-dependent Sirtuin family. The Sirtuin CNV locus localizes to an ~68-kb Starship mobile element unique to the species carrying genes highly expressed during plant infection. The element has likely lost the ability to transpose, demonstrating how the ongoing domestication of cargo-carrying selfish elements can contribute to selectable variation within populations. Our work highlights how standing variation in gene copy numbers at the global scale can be a major factor driving climatic and metabolic adaptation in microbial species.

An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets. The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols. Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation. This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.

An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets. The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols. Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation. This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.

Depth from Defocus Technique For High Number Density Particle Images

The Depth from Defocus (DFD) imaging technique is used to measure the size and number concentration of particles in dispersed two-phase flows, but until now it has primarily been applied to low concentration particle images. This study explores how the technique can be extended to handle overlapping images caused by neighboring particles, significantly broadening the application scope of the DFD technique and enabling measurements at higher particle number/volume concentrations. The processing algorithms are experimentally validated using a dedicated apparatus that can systematically vary particle size, shape, and degree of image overlap. Additionally, this study explores the use of Convolutional Neural Networks (CNN) for this task, comparing these results with those obtained using conventional analyses in terms of accuracy, tolerable concentration limits, and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. An image generation procedure for out-of-focus neighboring spherical particles, resulting in a known blurred image overlap, is therefore first developed. This procedure is validated using laboratory images with known particle size distribution, position, and image overlap before creating the teaching dataset. The trained processing scheme is then applied to both synthetic datasets and experimental data, allowing the evaluation of the technique’s limits in terms of image overlap and tolerable volume concentration, as a function of particle size distribution.

A Single Camera Calibration-Free Approach For Dispersion Size Measurement Using Depth From Defocus

The Depth from Defocus (DFD) imaging technique is used to measure the size and number concentration of particles in dispersed two-phase flows, but until now it has primarily been applied to low concentration particle images. This study explores how the technique can be extended to handle overlapping images caused by neighboring particles, significantly broadening the application scope of the DFD technique and enabling measurements at higher particle number/volume concentrations. The processing algorithms are experimentally validated using a dedicated apparatus that can systematically vary particle size, shape, and degree of image overlap. Additionally, this study explores the use of Convolutional Neural Networks (CNN) for this task, comparing these results with those obtained using conventional analyses in terms of accuracy, tolerable concentration limits, and computational speed. This approach requires a large teaching dataset of images, which is only practical and feasible if the dataset can be synthetically generated. An image generation procedure for out-of-focus neighboring spherical particles, resulting in a known blurred image overlap, is therefore first developed. This procedure is validated using laboratory images with known particle size distribution, position, and image overlap before creating the teaching dataset. The trained processing scheme is then applied to both synthetic datasets and experimental data, allowing the evaluation of the technique’s limits in terms of image overlap and tolerable volume concentration, as a function of particle size distribution.

Digitalisation of the Preliminary Investigation Phase, Fundamental and Human Rights and the Principle of Openness - Balancing Conflicting Interests in the Review of Large Data Sets

The EU courts have divided an investigation into two distinct stages with different aims: the preliminary investigation phase and the contradictory phase. This paper examines issues related to the digitalisation of the preliminary investigation phase, from screening and open source intelligence to data processing during unannounced inspections or dawn raids. The question is how rights of defence are secured without jeopardising an investigation where data sets have grown beyond anything previously known. Within the context of the principle of openness of government activities in Finland and Sweden, the author sets out to find how the main rule of openness is balanced with the objectives of the preliminary investigation phase. The article examines case-law and literature, complemented with public statements from competition authorities, to find that a fair balance between conflicting interests has been achieved thus far. Examples of open questions currently subject to debate do, nonetheless, range from using personal apps for detection to whether national identity as per Article 4(2) of the Treaty on European Union can tip the balance between confidentiality of correspondence and cartel enforcement categorically in favour of the former. The Court of Justice will likely have to address the issue of national identity in the ongoing Ronos case.

Accurate determination of active slip systems using a novel lattice rotation analysis: Quasi-in-situ EBSD/ECCI study on the homogeneous/heterogeneous deformation of polycrystalline zirconium

The accurate determination of slip systems is crucial for understanding deformation mechanisms of metals in particular, how slip/twining transfer or blocking occurs. This paper proposes a novel crystallographic lattice rotation analysis, which combines quasi-in-situ Electron Back Scatter Diffraction (EBSD) and Electron channeling contrast imaging (ECCI) techniques to precisely predict the active slip system with specific slip directions in polycrystalline Zr. Large data sets for hundreds of grains were quantitatively analyzed by this method to statistically gain deep insight on the deformation mechanism of Zr. Our results show that prismatic 〈a〉 slip is the primary slip system in most grains, and pyramidal 〈a〉 slip plays a secondary role in coordinating the local stress state of plastic deformation. The predicted slip direction is the same as the direction in which dislocation density increases. This method can serve as a complementary tool to the intragranular misorientation axis (IGMA) method, bridging the gap between macro-mechanical response and microstructural deformation mechanisms.

Digitalisation of the Preliminary Investigation Phase, Fundamental and Human Rights and the Principle of Openness - Balancing Conflicting Interests in the Review of Large Data Sets

The EU courts have divided an investigation into two distinct stages with different aims: the preliminary investigation phase and the contradictory phase. This paper examines issues related to the digitalisation of the preliminary investigation phase, from screening and open source intelligence to data processing during unannounced inspections or dawn raids. The question is how rights of defence are secured without jeopardising an investigation where data sets have grown beyond anything previously known. Within the context of the principle of openness of government activities in Finland and Sweden, the author sets out to find how the main rule of openness is balanced with the objectives of the preliminary investigation phase. The article examines case-law and literature, complemented with public statements from competition authorities, to find that a fair balance between conflicting interests has been achieved thus far. Examples of open questions currently subject to debate do, nonetheless, range from using personal apps for detection to whether national identity as per Article 4(2) of the Treaty on European Union can tip the balance between confidentiality of correspondence and cartel enforcement categorically in favour of the former. The Court of Justice will likely have to address the issue of national identity in the ongoing Ronos case.

Fast linear model trees by PILOT

Linear model trees are regression trees that incorporate linear models in the leaf nodes. This preserves the intuitive interpretation of decision trees and at the same time enables them to better capture linear relationships, which is hard for standard decision trees. But most existing methods for fitting linear model trees are time consuming and therefore not scalable to large data sets. In addition, they are more prone to overfitting and extrapolation issues than standard regression trees. In this paper we introduce PILOT, a new algorithm for linear model trees that is fast, regularized, stable and interpretable. PILOT trains in a greedy fashion like classic regression trees, but incorporates an L2 boosting approach and a model selection rule for fitting linear models in the nodes. The abbreviation PILOT stands for PIecewise Linear Organic Tree, where ‘organic’ refers to the fact that no pruning is carried out. PILOT has the same low time and space complexity as CART without its pruning. An empirical study indicates that PILOT tends to outperform standard decision trees and other linear model trees on a variety of data sets. Moreover, we prove its consistency in an additive model setting under weak assumptions. When the data is generated by a linear model, the convergence rate is polynomial.

Fast mvSLOUCH: Multivariate Ornstein–Uhlenbeck‐based models of trait evolution on large phylogenies

Abstract The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein–Uhlenbeck processes. The results of our analysis demonstrate the ability of the modelling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.

Fast mvSLOUCH: Multivariate Ornstein–Uhlenbeck‐based models of trait evolution on large phylogenies

Abstract The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein–Uhlenbeck processes. The results of our analysis demonstrate the ability of the modelling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.

Elite Soccer Players’ Weekly Workload Assessment Through a New Training Load and Performance Score

ABSTRACT Purpose: Monitoring players’ training load allows practitioners to enhance physical performance while reducing injury risk. The aim of this study was to identify the key external load indicators in professional U19 soccer. Methods: Twenty-four-professional Italian young (U19) soccer players were monitored by using the rating of perceived exertion (CR-10 RPE scale) and a wearable inertial sensor during the competitive season. Three main components were detected by a Principal Component Analysis (PCA): i) volume metabolic related component, ii) intensity mechanical stimuli component, and iii) intensity metabolic/mechanical component. We hence computed two scores (i.e. Performance [PERF] and total workload [WORK]) permitting to investigate the weekly microcycle. Results: Correlation analysis showed that scores (i.e. PERF and WORK) are low correlated (r = -0.20) suggesting that they were independent. Autocorrelation analysis showed that a weekly microcycle is detectable in all the scores. Two-way ANOVA RM showed a statistical difference between match day (MD) and playing position for the three PCA components and PERF score. Conclusion: We proposed an innovative approach to assess both the players’ physical performance and training load by using a machine learning approach allowing reducing a large dataset in an objective way. This approach may help practitioners to prescribe the training in the microcycle based on the two scores.

Elite Soccer Players’ Weekly Workload Assessment Through a New Training Load and Performance Score

ABSTRACT Purpose: Monitoring players’ training load allows practitioners to enhance physical performance while reducing injury risk. The aim of this study was to identify the key external load indicators in professional U19 soccer. Methods: Twenty-four-professional Italian young (U19) soccer players were monitored by using the rating of perceived exertion (CR-10 RPE scale) and a wearable inertial sensor during the competitive season. Three main components were detected by a Principal Component Analysis (PCA): i) volume metabolic related component, ii) intensity mechanical stimuli component, and iii) intensity metabolic/mechanical component. We hence computed two scores (i.e. Performance [PERF] and total workload [WORK]) permitting to investigate the weekly microcycle. Results: Correlation analysis showed that scores (i.e. PERF and WORK) are low correlated (r = -0.20) suggesting that they were independent. Autocorrelation analysis showed that a weekly microcycle is detectable in all the scores. Two-way ANOVA RM showed a statistical difference between match day (MD) and playing position for the three PCA components and PERF score. Conclusion: We proposed an innovative approach to assess both the players’ physical performance and training load by using a machine learning approach allowing reducing a large dataset in an objective way. This approach may help practitioners to prescribe the training in the microcycle based on the two scores.

Accelerating structure search using atomistic graph-based classifiers.

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles.

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results

Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed at closing this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123–158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RI_PEG), predicted using another model, can be used as a molecular descriptor. This descriptor significantly improves the accuracy of the QSRR model. Both deep learning-based and linear models were considered for RI_PEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online: https://github.com/mtshn/chereshnya.

How to fight fake papers: a review on important information sources and steps towards solution of the problem.

Scientific fake papers, containing manipulated or completely fabricated data, are a problem that has reached dramatic dimensions. Companies known as paper mills (or more bluntly as "criminal science publishing gangs") produce and sell such fake papers on a large scale. The main drivers of the fake paper flood arethe pressure in academic systems and (monetary) incentives to publish in respected scientific journals and sometimes the personal desire for increased "prestige." Published fake papers cause substantial scientific, economic, and social damage. There are numerous information sources that deal with this topic from different points of view. This review aims to provide an overview of these information sources until June 2024. Much more original research with larger datasets is needed, for example on the extent and impact of the fake paper problem and especially on how to detect them, as many findings are based more on small datasets, anecdotal evidence, and assumptions. A long-term solution would be to overcome the mantra of publication metrics for evaluating scientists in academia.

First and Second Reductions in an Aprotic Solvent: Comparing Computational and Experimental One-Electron Reduction Potentials for 345 Quinones.

Using reference reduction potentials of quinones recently measured relative to the saturated calomel electrode (SCE) in N,N-dimethylformamide (DMF), we benchmark absolute one-electron reduction potentials computed for 345 Q/Q•- and 265 Q•-/Q2- half-reactions using adiabatic electron affinities computed with density functional theory and solvation energies computed with four continuum solvation models: IEF-PCM, C-PCM, COSMO, and SM12. Regression analyses indicate a strong linear correlation between experimental and absolute computed Q/Q•- reduction potentials with Pearson's correlation coefficient (r) between 0.95 and 0.96 and the mean absolute error (MAE) relative to the linear fit between 83.29 and 89.51 mV for different solvation methods when the slope of the regression is constrained to 1. The same analysis for Q•-/Q2- gave a linear regression with r between 0.74 and 0.90 and MAE between 95.87 and 144.53 mV, respectively. The y-intercept values obtained from the linear regressions are in good agreement with the range of absolute reduction potentials reported in the literature for the SCE but reveal several sources of systematic error. The y-intercepts from Q•-/Q2- calculations are lower than those from Q/Q•- by around 320-410 mV for IEF-PCM, C-PCM, and SM12 compared to 210 mV for COSMO. Systematic errors also arise between molecules having different ring sizes (benzoquinones, naphthoquinones, and anthraquinones) and different substituents (titratable vs nontitratable). SCF convergence issues were found to be a source of random error that was slightly reduced by directly optimizing the solute structure in the continuum solvent reaction field. While SM12 MAEs were lower than those of the other solvation models for Q/Q•-, SM12 had larger MAEs for Q•-/Q2- pointing to a larger error when describing multiply charged anions in DMF. Altogether, the results highlight the advantages of, and further need for, testing computational methods using a large experimental data set that is not skewed (e.g., having more titratable than nontitratable substituents on different parent groups or vice versa) to help further distinguish between sources of random and systematic errors in the calculations.

Next-generation bioinformatics: An ultrafast and user-friendly tool for phylogenomic data exploration.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS and Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

Proposal and investigation of a distributed learning strategy in Orbital Edge Computing-endowed satellite networks for Earth Observation applications

One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. Instead, Federated Learning (FL) could be fruitfully leveraged in this scenario, since it provides for each satellite to train a local model only with its own dataset, and then to share its trained model with a central server, which receives models trained by the different satellites and aggregates them into a new global model being eventually shared with all the satellites, and this repeats until convergence is reached. However, because communication with a node acting as a central parameter server may be still limited by short visibility time, the described process may need a long time because of limited communication windows, negatively impacting the time needed to reach model convergence. For this reason, we propose a communication strategy to support a completely distributed learning technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that distributed learning outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time, as it has been verified on a land coverage classification task based on the EuroSAT dataset.

Image Deconvolution and Point-spread Function Reconstruction with STARRED: A Wavelet-based Two-channel Method Optimized for Light-curve Extraction

We present starred, a point-spread function (PSF) reconstruction, two-channel deconvolution, and light-curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high-signal-to-noise-ratio, high-resolution frame combining all data and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: (i) the extraction of supernovae light curves and the scene representation of their host galaxy; (ii) the extraction of lensed quasar light curves for time-delay cosmography; and (iii) the measurement of the spectral energy distribution of globular clusters in the “Sparkler,” a galaxy at redshift z = 1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and graphics processing unit acceleration. This enables the rapid processing of large time-domain data sets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects.