Single Data Research Articles

Synthesis and Characterization of Lithium Pyrocarbonate (Li2[C2O5]) and Lithium Hydrogen Pyrocarbonate (Li[HC2O5]).

The anhydrous pyrocarbonate and the first hydrogen pyrocarbonate Li[HC2O5] have been synthesized in a laser-heated diamond anvil cell at moderate pressures ( GPa). The structures of the two compounds have been obtained from single crystal X-ray diffraction data. Raman spectroscopy and DFT calculations have been employed to further characterize their structure-property relations. The present results significantly enlarge the group of inorganic pyrocarbonates by the discovery of the hydrogenated pyrocarbonate anion Li[HC2O5]-. In the structure of Li[HC2O5] there is a symmetric O-H-O arrangement at high pressures, which converts to a conventional O-H O hydrogen bond upon pressure release.

Phenome-wide associations of human aging uncover sex-specific dynamics.

Aging varies significantly among individuals of the same chronological age, indicating that biological age (BA), estimated from molecular and physiological biomarkers, may better reflect aging. Prior research has often ignored sex-specific differences in aging patterns and mainly focused on aging biomarkers from a single data modality. Here we analyze a deeply phenotyped longitudinal cohort (10K project, Israel) of 10,000 healthy individuals aged 40-70 years that includes clinical, physiological, behavioral, environmental and multiomic parameters. Follow-up visits are scheduled every 2 years for a total of 25 years. We devised machine learning models of chronological age and computed biological aging scores that represented diverse physiological systems, revealing different aging patterns among sexes. Higher BA scores were associated with a higher prevalence of age-related medical conditions, highlighting the clinical relevance of these scores. Our analysis revealed system-specific aging dynamics and the potential of deeply phenotyped cohorts to accelerate improvements in our understanding of chronic diseases. Our findings present a more holistic view of the aging process, and lay the foundation for personalized medical prevention strategies.

Improved Rapid Equilibrium Dialysis-Mass Spectrometry (RED-MS) Method for Measuring Small Molecule-Protein Complex Binding Affinities in Solution.

Rapid equilibrium dialysis (RED) is predominantly used for the characterization of drug absorption, distribution, metabolism, and excretion (ADME) properties in plasma and biological fluids. We describe herein improvements in the use of RED in conjunction with mass spectrometry (RED-MS) to enable robust binding affinity measurements of small molecules for recombinant proteins and complexes from a single dialysis data set. The affinities calculated from RED-MS correlated well with measurements by both surface plasmon resonance (SPR) and affinity selection mass spectrometry (AS-MS). The method was particularly useful for quantifying the binding of small molecules to large protein complexes that were not amendable by common biophysical characterization techniques. Compound pooling and integration with automated liquid handling increased assay throughput and enabled the analysis of hundreds of measurements per week. RED-MS offers a viable option for measuring compound binding in solution and may facilitate small molecule affinity optimization toward difficult-to-drug protein complexes.

The Mediating Role of Leader-Member Exchange in the Relationship Between Paternalistic Leadership and Turnover Intention: A Study on Millennial Employees in China

This study investigates the relationship between three-dimensional paternalistic leadership and turnover intentions among millennial employees in China, with a focus on the mediating role of leader-member exchange (LMX). A sample of 241 millennial employees from private enterprises was analyzed using SPSS and PLS techniques. The findings indicate that LMX effectively mediates the influence of three-dimensional paternalistic leadership on turnover intentions. The study's use of single respondent and cross-sectional data may limit the robustness and generalizability of its conclusions. Nevertheless, the results offer valuable insights for management practices and decision-making processes in organizations. This research contributes to the literature by highlighting LMX as a crucial mediator in the relationship between three-dimensional paternalistic leadership and turnover intentions among millennial employees. It underscores the importance of further exploring LMX's mediating role in similar contexts, thereby advancing theoretical understanding and practical applications in leadership and employee retention strategies.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

BackgroundThe use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data.MethodsNS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells.ResultsBy modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs—brain, kidney, and lung.DiscussionFinally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.

Pre-service English teachers’ learning traits

The present study aimed to characterize a comprehensive learning profile of pre-service teachers at an English teacher education programme of a public university in Southeast Mexico. The learning attributes comprise their English entry level, learning strategies and styles, learning approaches, and learning preferences. The research design was non-experimental, descriptive transectional, with a single data collection procedure during the August 2023 term. Four learning scales and two placement tests were used to identify the predominant learning traits of the cohort studied. The results show a preference to use cognitive learning strategies more than any other type. In terms of learning styles, the findings show that most participants are primarily aural, but some students are bi- and even tri-oriented. In terms of their learning preferences, most students identified as analytical. Regarding learning approaches, most students typically employ a deep approach, which could be quite inadequate because they depend more on surface strategies.

Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures

The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (p > 0.05). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.

Multi-item Prediction Using LSTM with Single Data for Plant Growth

Multi-item Prediction Using LSTM with Single Data for Plant Growth

Photoinduced Isomerization of [N2]2- in a Bimetallic Lutetium Complex.

The first lanthanide dinitrogen photoswitch [(C5Me4H)2(THF)Lu]2(μ-η2:η2-N2), 1, is reported. 1 is a unique example of controlled isomerization between side-on and end-on coordination modes of [N2]2- in a bimetallic lutetium dinitrogen complex that results in photochromism. Near-infrared light (NIR) was used to promote this effect, as evidenced by single X-ray diffraction (XRD) connectivity and Raman data, generating the [N2]2- end-on bound isomer, [(C5Me4H)2(THF)Lu]2(μ-η1:η1-N2), 2. Although different ligands and coordinating solvents were studied to replicate and control the optical properties in 1/2, only the original configuration with C5Me4H ligands and THF as the coordinating solvent worked. Supported by the first-principles calculations, the electronic structures along with the mechanistic details of the side-on to end-on isomerization were unraveled. Preliminary reactivity studies show that 2 formed with NIR light reacts with anthracene, generating dihydroanthracene and anthracene dimers, indicating new redox reaction pathways.

Study on Coal Seam Roof Failure Based on Optical Fiber Acoustic Sensing and the Parallel Electrical Method

As China enters the stage of deep coal mining, the accidents caused by roof failure pose increasingly serious threats. Current research on roof failure zones often use single methods, but single geophysical data may result in multisolution issues during interpretation. This paper employed similar simulation experiments, exploring the strain failure characteristics and the changes in apparent resistivity caused by stress variations, taking the 11-3106 working face of a mining area as the research object. Through optical fiber strain and apparent resistivity, the locations and degrees of fracture in postmining rock strata were identified. The feasibility of using distributed optical fiber sensing and the parallel electrical method for qualitative and quantitative analysis of mining-induced fractures was verified. The results showed that optical fiber strain increased significantly at the location of rock fracture, with apparent resistivity anomalies rising correspondingly. The peak strain region corresponded well with the region of apparent resistivity anomalies. In a similar simulation with a geometric ratio of 1:100, the height of the caving zone was measured to be 31.65 cm, with a caving-to-mining ratio of 6.33. In the field working face, the caving zone height was 29.47 m, with a caving-to-mining ratio of 6.01, consistent with the actual conditions of the 11-3106 working face.

Penerapan model kolaboratif dalam kebijakan data tunggal industri emas bagi penerimaan negara dengan pendekatan multi helix

Indonesia is one of the largest gold producing countries in the world. Based on mineral commodity data, Indonesia’s gold reserves reached 2,600 tons. In addition, the mining production reached 66 metric tons in 2021 and 70 metric tons in 2022. However, there are various problems and challenges related to efforts to utilize gold minerals, one of which is the limited availability and validity data. Therefore, this research aims to analyze how a collaboration model related to the gold industry’s single data policy for tax purposes can be implemented using a Multi Helix approach. In the Multi Helix concept, the role of each actor has very close participation to create success in creating quality data in the gold industry sector and sustainable development. Furthermore, it is expected to contribute significantly to tax revenues. Aside from that, the existence of the SDI policy is intended to provide benefits for sustainable economic development by maintaining a good quality of environmental ecosystems. Additionally, SDI data is expected to have a hand in state revenues both in the form of taxes and royalties to the state optimally, thereby creating competitive and growing businesses.

Scalar and vectorized implementations of the density functional theory via Geant4 and GeantV project

In this paper, we present the implementation of the Density Functional Theory (DFT) method using the Geant4-DNA framework in the Single Instruction Single Data (SISD) mode. Furthermore, this implementation is improved in terms of execution time within the GeantV project with vectorization techniques such as Single Instruction Multiple Data (SIMD). Within this framework, a set of SIMD strategies in molecular calculation algorithms such as one-electron operators, two-electron operator, quadrature grids, and functionals, was implemented using the VecCore library. The applications developed in this work implement two DFT functionals, the Local Density Approximation (LDA) and the General Gradient Approximation (GGA), to approximate the molecular ground-state energies of small molecules and amino acids. To assess the performance of the implementations, a standard test simulation was performed in multiple CPU platforms. The SIMD vectorization strategy significantly accelerates DFT calculations, leading to time ratios ranging from 1.6 to 5.4 in either individual steps or entire implementations when compared with the scalar process within Geant4.

A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods.

Detecting significant expression patterns in single-cell and spatial transcriptomics with a flexible computational approach

Gene expression data holds the potential to shed light on multiple biological processes at once. However, data analysis methods for single cell sequencing mostly focus on finding cell clusters or the principal progression line of the data. Data analysis for spatial transcriptomics mostly addresses clustering and finding spatially variable genes. Existing data analysis methods are effective in finding the main data features, but they might miss less pronounced, albeit significant, processes, possibly involving a subset of the samples. In this work we present SPIRAL: Significant Process InfeRence ALgorithm. SPIRAL is based on Gaussian statistics to detect all statistically significant biological processes in single cell, bulk and spatial transcriptomics data. The algorithm outputs a list of structures, each defined by a set of genes working simultaneously in a specific population of cells. SPIRAL is unique in its flexibility: the structures are constructed by selecting subsets of genes and cells based on statistically significant and consistent differential expression. Every gene and every cell may be part of one structure, more or none. SPIRAL also provides several visual representations of structures and pathway enrichment information. We validated the statistical soundness of SPIRAL on synthetic datasets and applied it to single cell, spatial and bulk RNA-sequencing datasets. SPIRAL is available at https://spiral.technion.ac.il/.

Synthesis, Crystal Structure, Intermolecular Interactions, HOMO-LUMO, MEP, NLO Properties, and DFT/TD-DFT Investigation of (Z)-5-(4-Nitrobenzylidene)-3-N(3-Chlorophenyl)-2-Thioxothiazolidin-4-One

A novel heterocyclic compound named (Z)-5-(4-nitrobenzylidene)-3-N(3-chlorophenyl)-2-thioxothiazolidin-4-one (NCTh) was synthesized and analyzed using various techniques, including single crystal X-ray diffraction, FT-IR, UV-Vis, NMR spectroscopy, and mass spectral data methods. NCTh was observed to crystallize in the triclinic crystal system P-1 space group. To validate the experimental findings, density functional theory (DFT) calculations were performed using the B3LYP functional with the 6-311 G(d,p) basis set. The results indicated good agreement in geometrical parameters between the experimental and theoretical outcomes. The study also calculated HOMO-LUMO energies, molecular electrostatic potential (MEP), and global chemical reactivity descriptors to evaluate the compound’s reactivity. Additionally, the study conducted reduced density gradient and Hirshfeld surface analyses to reveal attractive, repulsive, and van der Waals strong and weak interactions. The calculation of thermodynamic parameters was also included. The study focused on investigating the nonlinear optical (NLO) properties of NCTh, which were characterized through key parameters such as the electric dipole moment (µ), polarizability (α), first-order hyperpolarizability (β), and second-order hyperpolarizability (γ). These properties provide insights into the material’s potential applications in optical and photonic technologies. Additionally, a molecular docking study was performed to further explore the structure-activity relationship, particularly in a biological context. The docking results revealed a strong ligand-protein interaction, with a binding energy of −10.3 kcal/mol, indicating significant affinity. Moreover, the inhibition constant (Ki) was calculated to be 0.0281 μM, suggesting a highly potent interaction, which could be relevant for drug design or biochemical applications.

Epidemiology of obesity during pregnancy in Europe: a systematic review

Abstract Background Obesity during pregnancy poses inter-generational health risks and places added burden on healthcare systems. Characterising levels of obesity during pregnancy is necessary to guide future research, policy and practice. Methods The systematic review was performed according to PRISMA guidelines. PubMed, Embase and Web of Science were searched. Studies published between 2009 and 2023 that report the prevalence of obesity during pregnancy in countries in the World Health Organization (WHO) European region were eligible. The Joanna Briggs Institute Prevalence Critical Appraisal tool was used. Two reviewers screened studies independently. A single reviewer completed data extraction and quality assessments, a second checked 20% of extractions and a third resolved disagreements. Narrative synthesis was completed. Results 100 studies reporting the prevalence of obesity during pregnancy in the WHO European region were identified. 59% from Western Europe, 36% Northern, 4% Southern and 1% Eastern. Data within the studies was collected between 1950 and 2019. 35% of studies reported national estimates, 65% regional. 39% studies had measured height and weight, 34% did not report how either were ascertained, 6% used self-report for both, 17% had self-reported height but measured weight, 4% a mixture of self-report and measurement. The smallest sample was 157 while the largest was 1,891,097. Estimated prevalence of obesity during pregnancy ranged from 2.3% (Sweden, data collected 1982-1989) to 28.3% (England, 2007). Conclusions This review identifies variation in the prevalence of obesity during pregnancy in the WHO European region. This may reflect diverse sociocultural contexts, different methodological approaches and temporal trends. There was limited evidence available for Southern and Eastern Europe. Key messages • There are high levels of obesity during pregnancy within the WHO European region. • Enhanced primary prevention and resourcing of obstetric services is needed.

Burden of inflammatory bowel disease in Belgium, 2020

Abstract Inflammatory bowel disease (IBD) comprises digestive disorders resulting from non-infectious inflammation of the colon and gastrointestinal tract. With its global prevalence on the rise, IBD poses significant challenges to healthcare systems. There is a need for more accurate disease burden estimates to guide decision-making processes within the health sector. As a part of the Belgian national burden of disease study (BeBOD), this study aims to estimate the burden of IBD in Belgium based on locally available data. We estimated the Belgian IBD burden in 2020 in terms of mortality, prevalence, years of life lost (YLLs) due to premature death, years lived with disability (YLDs), and disability-adjusted life years (DALYs). As there is no single comprehensive data source on prevalence of IBD in Belgium, a critical appraisal of existing local and national data sources was conducted. Prevalence data were combined with disability weights to yield YLDs. YLLs were calculated using the most recent Global Burden of Disease (GBD) 2019 reference life table and the number of deaths caused by non-infective IBD (ICD-10 K50-52). Cause of death data were extracted from Statbel, the Belgian statistical office. The best available data source to estimate the IBD prevalence in Belgium was a widespread network of general practitioners. The prevalence of IBD was 1.5% in 2020. The prevalence-based YLD for IBD was 220 YLDs per 100 000, while the YLL rate was 11.5 per 100 000. We estimated 26 276 DALYs caused by IBD in 2020. Of the total DALYs caused by IBD, 5% were due to YLLs and 95% were due to YLDs. Our burden estimates for IBD in Belgium were however higher than those of the GBD study. IBD imposes a low fatal burden, whereas the burden of morbidity is more prominent. Our findings could be useful for policy makers to justify and prioritize resource allocation. Integrating the current research in BeBOD will allow monitoring the burden of IBD over time. Key messages • Inflammatory bowel disease is characterized by a substantial non-fatal burden due to chronic relapsing and persistent symptoms. • National inflammatory bowel disease burden estimates are useful to guide decision-making within the health sector.

Novel R package to quantify and monetize burden of disease attributable to air pollution and noise

Abstract Background Ambient air pollution and noise substantially impact public health and the economy. Different methods to quantify these impacts are described in the literature. Objectives Within the European project BEST-COST (Burden of disease-based methods for estimating the socio-economic cost of environmental stressors), we aim to develop an open-source R package to quantify and monetise the disease burden attributable to air pollution and noise, including the impact of social inequalities. Methods The developed code will allow for different calculation pathways including: single baseline health data vs. age-specific estimates from life tables; relative and absolute risk; scenario comparison based on the population attributable fraction vs. population impact fraction; single-pollutant vs. correlated exposures; and outdoor vs. indoor air pollution. Moreover, the package can digest a variety of input data formats for the exposure-response function (e.g. relative risk with fixed shape vs. user-defined function) and exposure data (e.g. population-weighted mean vs. categorical distribution, spatial data). Eligible impact metrics will include morbidity incidence and prevalence, number of deaths, years of life lost, and years lived with disability. Finally, the R package will enable quantifying social inequities (using a novel multiple deprivation index) and monetising the attributable disease burden (using a state-of-the-art approach). Results During development, the R package will be tested in BEST-COST case studies in 5 European countries (BE, EE, FR, NO, PT), compared with existing tools, and discussed with key users in a workshop. The final R package will be available on GitHub in 2026. Conclusions This tool will allow the quantification and monetisation of the health impact of air pollution and noise. The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making. Key messages • This open-source tool will allow the quantification and monetisation of the health impact of air pollution and noise, including the impact of social inequalities. • The R package will be a flexible programming resource that can be used as a standalone or combined with existing tools to build a more solid basis for evidence-based policy making.

The burden of psoriasis in Belgium, 2021

Abstract Psoriasis is a common, chronic, inflammatory skin disease with a great impact on healthcare systems. Given the limitations of the currently available burden estimates, estimates of the Belgian national burden of disease study are necessary in order to guide decision-making processes within the health sector. According to the Global Psoriasis Atlas, the prevalence of psoriasis is rising globally. The current study aims to estimate the burden of psoriasis in Belgium using national data. As there is no single comprehensive data source on prevalence of psoriasis in Belgium, a critical appraisal of existing local and national data sources was conducted. We estimated the non-fatal psoriasis burden in terms of years lived with disability (YLD). Estimated prevalence data were combined with disability weights to yield YLDs imposed by psoriasis. Mortality attributable to psoriasis was assumed to be zero, in line with the Global Burden of Disease study (GBD) 2019. We assessed three data sources containing information on existing cases with psoriasis in Belgium. Based on qualitative and quantitative appraisal, a widespread network of general practitioners was identified as the best available national data source. In 2021, the prevalence of psoriasis in Belgium was 2.1%, leading to 13084 DALYs (113 DALYs per 100 000). The burden of psoriasis was in line with national disease burden estimates for bipolar disorder, multiple myeloma, and hearing loss. Our prevalence, YLD, and DALY estimates for psoriasis in Belgium were however lower than those of the GBD study. Psoriasis imposes a non-negligible burden on population health in Belgium, despite it not being a cause of death. Along progression in psoriasis management, objective evaluation of psoriasis burden is critical to track the trends at the population level. Key messages • Psoriasis is a common skin disease with a relatively high morbidity burden. • Information on psoriasis prevalence and burden are crucial for disease management and appropriate health-care planning.

Transfer learning for probabilistic localization of hidden cracks in concrete structures

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.