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The Genetics Society of America is calling for submissions of papers on biobank-scale genomic data analyses. Biobanks, which are large collections of biological, medical, and genetic data on the same individuals, have revolutionized human genomics research. However, there is substantial heterogeneity between biobanks worldwide, creating both challenges and opportunities for data analysis. The series will focus on various topics, including population structures, selection bias, measurement errors, clinical observations, linkage with external data sources, meta-analysis, computational analysis methods, family-based analyses, ethical and privacy-related issues, and technological considerations. Manuscripts can be submitted until May 31, 2024, and the series will be an ongoing resource in this area. [Extracted from the article]

Objective: To describe the technical performance of nested PCR for identifying hepatitis B viral (HBV) DNA. Methods: Hepatitis B viral DNA was extracted from a dried bloodstain on filter paper by a Chelex-100 method. Then the DNA fragment was amplified by nested polymerase chain reaction (PCR). The sensitivity and specificity of this method were also analyzed. The positive rate of the nested PCR-based method was compared with that of the enzyme-linked immunosorbent assays (ELISA) method. Results: The lowest detection limit of the test was 5 copies of HBV DNA per μL by this method. McNemar's test showed that the difference between the positive rates of these 2 methods was not statistically significant (P=0.289, P>0.05). Hepatitis B viral test results showed a good concordance between these 2 methods (kappa=0.727). Conclusion: A very small amount of the dried blood sample is required for the detection, which could overcome the shortage of the normal ELISA method that requires a relatively large amount of fresh blood samples. [ABSTRACT FROM AUTHOR]

With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR. [ABSTRACT FROM AUTHOR]

Motivation Reconstructing evolutionary histories of biological entities, such as genes, cells, organisms, populations, and species, from phenotypic and molecular sequencing data is central to many biological, palaeontological, and biomedical disciplines. Typically, due to uncertainties and incompleteness in data, the true evolutionary history (phylogeny) is challenging to estimate. Statistical modelling approaches address this problem by introducing and studying probability distributions over all possible evolutionary histories, but can also introduce uncertainties due to misspecification. In practice, computational methods are deployed to learn those distributions typically by sampling them. This approach, however, is fundamentally challenging as it requires designing and implementing various statistical methods over a space of phylogenetic trees (or treespace). Although the problem of developing statistics over a treespace has received substantial attention in the literature and numerous breakthroughs have been made, it remains largely unsolved. The challenge of solving this problem is 2-fold: a treespace has nontrivial often counter-intuitive geometry implying that much of classical Euclidean statistics does not immediately apply; many parametrizations of treespace with promising statistical properties are computationally hard, so they cannot be used in data analyses. As a result, there is no single conventional method for estimating even the most fundamental statistics over any treespace, such as mean and variance, and various heuristics are used in practice. Despite the existence of numerous tree summary methods to approximate means of probability distributions over a treespace based on its geometry, and the theoretical promise of this idea, none of the attempts resulted in a practical method for summarizing tree samples. Results In this paper, we present a tree summary method along with useful properties of our chosen treespace while focusing on its impact on phylogenetic analyses of real datasets. We perform an extensive benchmark study and demonstrate that our method outperforms currently most popular methods with respect to a number of important 'quality' statistics. Further, we apply our method to three empirical datasets ranging from cancer evolution to linguistics and find novel insights into corresponding evolutionary problems in all of them. We hence conclude that this treespace is a promising candidate to serve as a foundation for developing statistics over phylogenetic trees analytically, as well as new computational tools for evolutionary data analyses. Availability and implementation An implementation is available at https://github.com/bioDS/Centroid-Code. [ABSTRACT FROM AUTHOR]

The discrete cosine transform is a commonly used technique in the field of signal processing that employs cosine basis functions for signal analysis. Traditionally, the regression coefficients of the cosine basis functions are solely based on frequency information. This paper extends the regression coefficients associated with the cosine basis functions to take into account both frequency and time information, not just frequency information alone. This modification results in an ill-posed linear system, which requires regularization to prevent overfitting. To address this, this paper uses shaping regularization, a technique used to stabilize ill-posed problems. By doing so, the absolute values of these extended coefficients, now exhibiting variations in both frequency and time domains, are defined as the time–frequency distribution of that input signal. The numerical experiments conducted to validate this approach demonstrate that the proposed method yields a commendable time–frequency resolution. Consequently, it proves valuable for interpreting seismic data, showcasing its potential for applications in this field. [ABSTRACT FROM AUTHOR]

The article comments on the paper about relation between correlation and causation by Charles Spearman. According to the author, the human mind played a key role in linking correlation with causation deterministically. She adds that correlation demonstrates its ability to quantify the strength of a causal relationship despite the absence of evidence of its effectivity. She also asserts the key role of correlation in scientific discovery and innovation.

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

This document is a correction to a previously published paper titled "Mapping of geological structures and sediment thickness from analysis of aeromagnetic data over the Obudu Basement Complex of Nigeria." The correction addresses incorrect affiliations for two authors, Saad S. Alarifi and Peter Andráš, and provides the correct affiliations for both individuals. Additionally, the correction includes a correction to the city and P.O. Box in affiliation 2, and mentions the omission of the words "grant no." in the Acknowledgments section. These errors have been rectified in the paper. [Extracted from the article]

Purpose: Choosing Wisely (CW) is an emergent approach to identify and reduce unnecessary care, such as tests and treatments that do not add value for patients and may even cause harm. The purpose of this paper is to investigate whether and how Lean Healthcare Management (LHM) can support CW objectives, focusing on customer needs and on waste elimination.Data Sources: A systematic literature review has been performed in Scopus, PubMed and Web of Science.Study Selection: Peer reviewed articles published in English language have been selected. Papers were considered if they regarded LHM and its possible support for achieving CW objectives. Data extraction. The links between the LHM purposes of adoption and the pursued CW objectives were investigated. Moreover, LHM tools, practices and interventions to support CW were grasped.Results Of Data Synthesis: Sixteen articles were included in the analysis. Links between the identified LHM purposes of adoption and CW objectives were discovered: through process understanding, optimization, evaluation and control, LHM contributes to the reduction of overuses in healthcare, but also to the delivery of a more effective and evidence-based care (EBC). Moreover, it provides an objective approach useful for choosing the most cost-effective solution among different alternatives.Conclusions: Results highlight how LHM, and with which tools and practices, can be adopted to enhance the healthcare appropriateness pursued by CW, paving the way for interesting future research about this emerging topic. [ABSTRACT FROM AUTHOR]

This paper aims to empower cardiovascular (CV) researchers by promoting diversity, equity, and inclusion (DE&I) principles throughout the research cycle. It defines DE&I and introduces practical strategies for implementation in recruitment, retention, and team dynamics within CV research. Evidence-based approaches supporting underrepresented populations' participation are outlined for each research phase. Emphasizing the significance of inclusive research environments, the paper offers guidance and resources. We invite CV researchers to actively embrace DE&I principles, enhancing research relevance and addressing longstanding CV health disparities. [ABSTRACT FROM AUTHOR]

This study compared the reliability and validity of student scores from paper–pencil and e-based assessments using the "maze" and "silent reading fluency" (SRF) tasks. Forty students who were deaf and hard of hearing and reading between the second and fifth grade reading levels and their teachers (n  = 21) participated. For maze, alternate form reliability coefficients obtained from correct scores and correct scores adjusted for guessing ranged from r  = .61 to.84 (p s  <  .01); criterion-related validity coefficients ranged from r  = .33 to.67 (most p s < .01). For SRF, reliability coefficients obtained from correct scores ranged from r  = .50 to.75 (p s  <  .01); validity ranged from r  = .25 to.72. Differences between student performance on paper–pencil and e-based conditions were generally non-significant for maze; significant differences between conditions for SRF favored the paper–pencil condition. Findings suggest that maze holds promise, with inconclusive results for SRF. [ABSTRACT FROM AUTHOR]

This paper estimates the impact of access to mobile money services on local economic activity. The analysis combines data from the early expansion of the mobile agent network in Kenya with a local-level measure of economic performance proxied by the intensity of nighttime lights. Leveraging the variation in areas that gained access to mobile money services at different times and the high resolution of the data, the paper shows that access to mobile money services increased local economic activity and that these effects increased over time. The positive effects are more pronounced for areas that were initially more affluent, urban, and better connected to infrastructure. These results suggest that mobile money can complement, rather than just substitute for, other alternatives that enable people to connect, trade, and allocate investments within their networks. [ABSTRACT FROM AUTHOR]

The urgency for energy transition is evident through the increasing demand for new technologies such as water electrolysers (WEs), which have the potential to generate green hydrogen using renewable electricity. This paper aims to provide a comprehensive overview of the technical capabilities of commercially available WE system products. The analysis is based on publicly accessible data gathered from 28 WE manufacturers worldwide with a total of 186 products, focusing on technology types and various technical characteristics of each WE system, including capacity, footprint, hydrogen output pressure, hydrogen purity and conversion rate. The analysis reveals that the current WE system solutions in the market exhibit diverse and varied characteristics. Further, there is a lack of standardized product specifications adopted by manufacturers. This underscores the urgent need for the development of frameworks and standards. Implementing such standards is crucial for enhancing clarity and understanding, facilitating efficient comparisons and selection processes, and supporting the future advancement of WE technologies and WE-enabled Power-to-X applications on a global scale. [ABSTRACT FROM AUTHOR]

Low-frequency seismic data plays a crucial role in seismic data processing and seismic wave inversion. At present, there are two methods to realize the low-frequency excitation of vibrators: one is exciting low-frequency vibrators by linear sweep signals, and the other is exciting conventional vibrators by nonlinear low-frequency sweep signals. The cost of exploration using low-frequency vibroseis is high, and it is challenging to obtain sufficient low-frequency information using traditional vibrators. To this end, this paper comparatively studies the low-frequency sweep signal characteristics and data effects of low-frequency and traditional vibrators. Therefore, three kinds of linear and nonlinear low-frequency sweep signal are designed. Theoretical analysis shows that there are certain differences between linear and nonlinear signal in design methods, signal shapes, etc. but after correlation calculations the signal spectra reflecting the seismic response and the related wavelet shapes are basically consistent. Besides, the actual force signal data shows that the linear and nonlinear harmonic distortion are basically equivalent. Finally, based on the forward simulation of three sweep signals and the comparative analysis of field test data, it can be considered that the linear and nonlinear low-frequency sweep signals of vibrators have almost the same denoising ability under the basic conditions of the spectrum and wavelet. Both can achieve low-frequency excitation and obtain rich low-frequency information, and the quality of seismic data is basically the same, so they can be applied in practical production. [ABSTRACT FROM AUTHOR]

Contemporary searches for new risk factors frequently involve genome-wide explorations of very large numbers of candidate risk variants. Given that diseases can often be classified into subtypes that possess evidence of etiologic heterogeneity, the question arises as to whether or not a search for new risk factors would be improved by looking separately within subtypes. Etiologic risk heterogeneity inevitably increases the signal in at least one of the subtypes, but this advantage may be offset by smaller sample sizes and the increased chances of false discovery. In this article, the authors show that only a relatively modest degree of etiologic heterogeneity is necessary for the subtyping strategies to have improved statistical power. In practice, effective exploitation of etiologic heterogeneity requires strong evidence that the subtypes selected are likely to exhibit substantial heterogeneity. Further, defining the subtypes that demonstrate the most heterogeneous profiles is important for optimizing the search for new risk factors. The concepts are illustrated by using data from a breast cancer study in which results are available separately for estrogen receptor-positive (ER+) and -negative (ER−) tumors. [ABSTRACT FROM AUTHOR]

Background. Disparity between patient report and physician perception of pain from radiotracer injection for sentinel node biopsy is thought to center on the severity of the intervention, ethnic composition of population queried, and socioeconomic factors. Objective. The objectives of this study were, first, to explore agreement between physicians' and their breast cancer patients' pain assessment during subareolar radionucleotide injection; and second, to evaluate potential ethnic differences in ratings. Methods. A trial was conducted, from January 2006 to April 2009, where 140 breast cancer patients were randomly assigned to standard topical lidocaine-4% cream and 99mTc-sulfur colloid injection, or to one of three other groups: placebo cream and 99mTc-sulfur colloid injection containing NaHCO3, 1% lidocaine, or NaHCO3 + 1% lidocaine. Providers and patients completed numeric pain scales (0-10) immediately after injection. Results. Patients and providers rated pain similarly over the entire cohort (median, 3 vs 2, P = 0.15). Patients rated pain statistically significantly higher than physicians in the standard (6 vs 5, P = 0.045) and placebo + NaHCO3 (5 vs 4, P = 0.032) groups. No significant difference in scores existed between all African Americans and their physicians (3 vs 4, P = 0.27). Conclusion. Patient-physician pain assessment congruence over the less painful injections and their statistically similar scores with the more painful methods suggests the importance of utilizing the least painful method possible. Providers tended to underestimate patients with the highest pain ratings-those in the greatest analgesic need. Lack of statistical difference between African American and physician scores may reflect the equal-access-to-care over the entire patient cohort, supporting the conclusion that socioeconomic factors may lie at the heart of previously reported discrepancies. [ABSTRACT FROM AUTHOR]

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data analysis refers to the use of appropriate methods to analyze the dataset generated by RNA-sequencing performed on the single-cell transcriptome. It usually contains three steps: normalization to eliminate the technical noise, dimensionality reduction to facilitate visual understanding and data compression and clustering to divide the data into several similarity-based clusters. In addition, the gene expression data contain a large number of zero counts. These zero counts are considered relevant to random dropout events induced by multiple factors in the sequencing experiments, such as low RNA input, and the stochastic nature of the gene expression pattern at the single-cell level. The zero counts can be eliminated only through the analysis of the scRNA-seq data, and although many methods have been proposed to this end, there is still a lack of research on the combined effect of existing methods. In this paper, we summarize the two kinds of normalization, two kinds of dimension reduction and three kinds of clustering methods widely used in the current mainstream scRNA-seq data analysis. Furthermore, we propose to combine these methods into 12 technology combinations, each with a whole set of scRNA-seq data analysis processes. We evaluated the proposed combinations using Goolam, a publicly available scRNA-seq, by comparing the final clustering results and found the most suitable collection scheme of these classic methods. Our results showed that using appropriate technology combinations can improve the efficiency and accuracy of the scRNA-seq data analysis. The combinations not only satisfy the basic requirements of noise reduction, dimension reduction and cell clustering but also ensure preserving the heterogeneity of cells in downstream analysis. The dataset, Goolam, used in the study can be obtained from the ArrayExpress database under the accession number E-MTAB-3321. [ABSTRACT FROM AUTHOR]

Nested sampling parameter estimation differs from evidence estimation, in that it incurs an additional source of uncertainty. This uncertainty affects estimates of parameter means and credible intervals in gravitational wave analyses and beyond, and yet, it is typically not accounted for in standard uncertainty estimation methods. In this paper, we present two novel methods to quantify this uncertainty more accurately for any chain based nested sampler, using the additional likelihood calls made at run time in producing independent samples. Using injected signals of black hole binary coalescences as an example, we first show concretely that the usual uncertainty estimation method is insufficient to capture the true error bar on parameter estimates. We then demonstrate how the extra points in the chains of chain based samplers may be carefully utilized to estimate this uncertainty correctly, and provide a way to check the accuracy of the resulting error bars. Finally, we discuss how this uncertainty affects p – p plots and coverage assessments. [ABSTRACT FROM AUTHOR]

This paper presents analytical results on the anatomy of nested sampling, from which a technique is developed to estimate the run-time of the algorithm that works for any nested sampling implementation. We test these methods on both toy models and true cosmological nested sampling runs. The method gives an order-of-magnitude prediction of the endpoint at all times, forecasting the true endpoint within standard error around the halfway point. [ABSTRACT FROM AUTHOR]

The number of genome assemblies has rapidly increased in recent history, with NCBI databases reaching over 41,000 eukaryotic genome assemblies across about 2,300 species. Increases in read length and improvements in assembly algorithms have led to increased contiguity and larger genome assemblies. While this number of assemblies is impressive, only about a third of these assemblies have corresponding genome size estimations for their respective species on publicly available databases. In this paper, genome assemblies are assessed regarding their total size compared to their respective publicly available genome size estimations. These deviations in size are assessed related to genome size, kingdom, sequencing platform, and standard assembly metrics, such as N50 and BUSCO values. A large proportion of assemblies deviate from their estimated genome size by more than 10%, with increasing deviations in size with increased genome size, suggesting nonprotein coding and structural DNA may be to blame. Modest differences in performance of sequencing platforms are noted as well. While standard metrics of genome assessment are more likely to indicate an assembly approaching the estimated genome size, much of the variation in this deviation in size is not explained with these raw metrics. A new, proportional N50 metric is proposed, in which N50 values are made relative to the average chromosome size of each species. This new metric has a stronger relationship with complete genome assemblies and, due to its proportional nature, allows for a more direct comparison across assemblies for genomes with variation in sizes and architectures. [ABSTRACT FROM AUTHOR]

The top 100 most cited papers in social work journals in 2000–09 were recently listed in a British Journal of Social Work paper by Hodge, Lacasse and Benson. The citations of these 100 papers in a two-year period (2007–09) were identified and a further manual web-based search was conducted to identify some key features of the citing sources. Descriptive statistics are presented on language of publication and national location of citing authors, as well as type of citing source. Bivariate analysis was conducted of the national location of cited journal by national location of citing first author. This analysis shows a strong tendency towards within-country citation in both the UK and USA, providing some evidence of intellectual chauvinism amongst social work academics and, more specifically, evidence of a Great Atlantic Divide. [ABSTRACT FROM PUBLISHER]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two‐level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single‐level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two‐level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower‐level gene–gene interactions and higher‐level pathway–pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two‐level selection as well as censoring. Its computational efficiency is much desirable for high‐dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability. [ABSTRACT FROM AUTHOR]

Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression coefficients among subjects. In this paper, we develop a flexible and interpretable approach, called grouped GEE analysis, to modeling longitudinal data with allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and subjects within the same group share the same regression coefficient. We provide a simple algorithm for grouping subjects and estimating the regression coefficients simultaneously, and show the asymptotic properties of the proposed estimator. The number of groups can be determined by the cross validation with averaging method. We demonstrate the proposed method through simulation studies and an application to a real data set. [ABSTRACT FROM AUTHOR]

The presence of an urban active fault carries a geological hazard risk that threatens urban construction. Therefore, detecting such faults is crucial for mitigating potential risks and ensuring safe urban development. The grounded-wire source short-offset transient electromagnetic method (SOTEM) offers an opportunity to identify faults with high-conductivity. The results of a SOTEM survey conducted in Shunping, Hebei Province, were presented to detect buried faults and locate the industrial park. Data were analysed using the one-dimensional inversion method with SOTEMsoft software, which was denoised using the short-time sliding window adaptive singular value decomposition algorithm based on the wavelet threshold. Despite the challenges posed by urban features, particularly traffic, the survey results located the fault and hold significant potential to reveal its extent and depth. The study highlights the importance of avoiding fault sites during urban construction, even though the area is currently tectonically stable. This paper reveals an unusual opportunity to study the effectiveness of electromagnetic methods for detecting buried faults in cities and reveal geological structures covered by Quaternary formations. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a mathematical programming technique for efficiency analysis. For dealing with the data in ratio form, the DEA model for ratio data known as DEA-R exists in the literature. However, some ratio data like financial risk may be negative naturally. In this paper, we contribute to the literature in two ways. In the first place, we deal with DEA-R models in the presence of negative ratio data by proposing an inverse DEA model for merger analysis. In the second place, we develop DEA-R models for merger analysis that can deal with negative data. We apply our models in a real-world application of efficiency and merger analysis of an Iranian bank with 66 branches. The proposed models maintain data confidentiality. This motivates managers to participate in the evaluation and merger process. Our models also provide a reasonable endogenous weight restriction framework without restricting weights exogenously. [ABSTRACT FROM AUTHOR]

Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socio-economic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crash occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011–2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction. [ABSTRACT FROM AUTHOR]

Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or 'compositional', in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health. [ABSTRACT FROM AUTHOR]

Wade Hampton Frost, who was a Professor of Epidemiology at Johns Hopkins University from 1919 to 1938, spurred the development of epidemiologic methods. His 6 publications in the American Journal of Hygiene, which later became the American Journal of Epidemiology, comprise a 1928 Cutter lecture on a theory of epidemics, a survey-based study of tonsillectomy and immunity to Corynebacterium diphtheriae (1931), 2 papers from a longitudinal study of the incidence of minor respiratory diseases (1933 and 1935), an attack rate ratio analysis of the decline of diphtheria in Baltimore (1936), and a 1936 lecture on the age, time, and cohort analysis of tuberculosis mortality. These 6 American Journal of Hygiene /American Journal of Epidemiology papers attest that Frost's personal evolution mirrored that of the emerging “early” epidemiology: The scope of epidemiology extended beyond the study of epidemics of acute infectious diseases, and rigorous comparative study designs and their associated quantitative methods came to light. [ABSTRACT FROM PUBLISHER]

Background Occupational colour vision testing is a requirement in a number of transport industries, and there are a number of tests that are considered acceptable by the various industry regulatory bodies. Aims To review the occupational colour vision tests currently in use nationally and internationally and determine whether they give consistent results. Methods A systematic review of the evidence was carried out according to standard methods. The Ovid Medline database was searched from 1946 to March 2013 using a broad and inclusive strategy. Results A total of 8951 citations were identified, from which 20 papers were selected for data analysis. Of these papers, 13 of 20 assessed test sensitivity and specificity, and 11 papers measured the number, type and severity of colour vision deficiency of subjects passing the tests. Three studies also measured test repeatability. The quality of studies included was generally good. Sensitivity and specificity ranged from 64% to 100% and 88% to 100%, respectively. The studies evaluating the newer screenbased tests reported the highest sensitivity and specificity. The marked variability reported between tests and within tests can be attributed to many factors including test protocol, sample selection, test distance and time for dark adaptation. Conclusions There was low consistency between the colour vision tests examined. Lantern tests cannot be used to identify type or severity of colour vision deficit and, when used as a screening test for 'colour safe' status, give variable results. These results highlight the need for standardization across the transport industries. [ABSTRACT FROM AUTHOR]

Given the extreme dependence of agriculture on weather conditions, this paper analyses the effect of climatic variations on this economic sector, by considering both a huge data set and a flexible spatiotemporal model specification. In particular, we study the response of N‐fertilizer application to abnormal weather conditions, while accounting for other relevant control variables. The data set consists of gridded data spanning over 21 years (1993–2013), while the methodological strategy makes use of a spatial dynamic panel data (SDPD) model that accounts for both space and time fixed effects, besides dealing with both space and time dependences. Time‐invariant short‐ and long‐term effects, as well as time‐varying marginal effects are also properly defined, revealing interesting results on the impact of both GDP and weather conditions on fertilizer utilizations. The analysis considers four macroregions—Europe, South America, Southeast Asia and Africa—to allow for comparisons among different socio‐economic societies. In addition to finding both spatial (in the form of knowledge spillover effects) and temporal dependences as well as a good support for the existence of an environmental Kuznets curve for fertilizer application, the paper shows peculiar responses of N‐fertilization to deviations from normal weather conditions of moisture for each selected region, calling for ad hoc policy interventions. [ABSTRACT FROM AUTHOR]

The quasi-maximum likelihood estimation is a commonly-used method for estimating the generalized autoregressive conditional heteroscedastic parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the generalized autoregressive conditional heteroscedastic parameters based on ranks of the residuals, called R-estimators, with the property that they are asymptotically normal under the existence of a finite |$2+\delta$| moment of the errors and are highly efficient. We propose a fast algorithm for computing the R-estimators. Both real data analysis and simulations show the superior performance of the proposed estimators under the heavy-tailed and asymmetric distributions. [ABSTRACT FROM AUTHOR]

We analyze the relationship between asset price bubbles and systemic risk, using bank-level data covering almost 30 years. Banks' systemic risk already rises during a bubble's buildup and even more so during its bust. The increase in risk strongly differs across banks and by bubble. It depends on bank characteristics (especially bank size) and bubble characteristics and can become very large: in a median real estate bust, systemic risk increases by almost 70% of the median for banks with unfavorable characteristics. These results emphasize the importance of bank-level factors in the buildup of financial fragility during bubble episodes. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Building on signaling theory, this paper analyzes whether projects signaling patented technologies have a higher probability of funding success on a reward-based crowdfunding platform as compared with a control group of similar projects. Our analysis of a set of Kickstarter projects suggests the existence of an apparent paradox concerning the role of patents in this funding context. Despite solid evidence in entrepreneurial finance literature on the positive role of patents for accessing financing from professional investors such as venture capitals and business angels, our results from the reward-based crowdfunding context point out a negative signaling role of patents. We provide explanations to reconcile such evidence with previous relevant literature and highlight promising avenues for future research in this area. [ABSTRACT FROM AUTHOR]

This paper introduces new data on the creation of subsidiary bodies (SBs) by members of the United Nations Security Council (UNSC) between 1972 and 2020. Delegation to SBs is one of the principal means through which the UNSC acts, and these bodies are designed to carry out crucial functions such as peacekeeping, implementing sanctions, and investigating crises. Yet, no research has systematically evaluated their creation, design, and use. Our dataset includes a typology of all proposed and created SBs as well as information about their purpose and design. After introducing the data, we empirically analyze the determinants of SB creation. Multivariate regression demonstrates that SBs are more likely to be created when the preferences of the permanent members are aligned. Moreover, stronger bodies are more likely to be created during periods of high preference alignment, while middle- and lower-strength bodies are less influenced by member alignment. These results provide unique evidence demonstrating how politics affects the choice of when and how the UNSC responds to global problems. Our data and analysis paint a picture of a more proactive UNSC than is commonly portrayed in the literature, and these data will enable scholars to further analyze UNSC action. [ABSTRACT FROM AUTHOR]

Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7):1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research. [ABSTRACT FROM AUTHOR]

Motivation Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. Results In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with.ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data. Availability and implementation Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph. [ABSTRACT FROM AUTHOR]

In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993–2005) and the Hispanic Community Health Study/Study of Latinos (2008–2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error. [ABSTRACT FROM AUTHOR]

Patient journey mapping is an emerging field of research that uses various methods to map and report evidence relating to patient experiences and interactions with healthcare providers, services, and systems. This research often involves the development of visual, narrative, and descriptive maps or tables, which describe patient journeys and transitions into, through, and out of health services. This methods corner paper presents an overview of how patient journey mapping has been conducted within the health sector, providing cardiovascular examples. It introduces six key steps for conducting patient journey mapping and describes the opportunities and benefits of using patient journey mapping and future implications of using this approach. [ABSTRACT FROM AUTHOR]

Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a probe to investigate the physical properties inside supernovae. However, how to search for and extract the GW signals from CCSNe remains an open question owing to their complicated time–frequency structure. In this paper, we apply the ensemble empirical mode decomposition (EEMD) method to decompose and reconstruct simulated GW data generated by the magnetorotational mechanism and the neutrino-driven mechanism within the Advanced LIGO, using the match score as the criterion for assessing the quality of the reconstruction. The results indicate that by decomposing the data, the sum of the first six intrinsic mode functions (IMFs) can be used as the reconstructed waveform. To determine the probability that our reconstructed waveform corresponds to a real GW waveform, we calculate the false alarm probability of reconstruction (FAPR). By setting the threshold of the match score to be 0.75, we obtain the FAPRs of GW sources at distances of 5 and 10 kpc to be 6 × 10−3 and 1 × 10−2, respectively. If we normalize the maximum amplitude of the GW signal to 5 × 10−21, the FAPR at this threshold is 4 × 10−3. Furthermore, in our study, the reconstruction distance is not equivalent to the detection distance. When the strain of GWs reaches 7 × 10−21, and the match score threshold is set at 0.75, we can reconstruct GW waveforms up to approximately 36 kpc. [ABSTRACT FROM AUTHOR]

Global 21-cm experiments are built to study the evolution of the Universe between the cosmic dawn and the epoch of reionization. FlexKnot is a function parametrized by freely moving knots stringed together by splines. Adopting the FlexKnot function as the signal model has the potential to separate the global 21-cm signal from the foregrounds and systematics while being capable of recovering the crucial features given by theoretical predictions. In this paper, we implement the FlexKnot method by integrating twice over a function of freely moving knots interpolated linearly. The function is also constrained at the lower frequencies corresponding to the dark ages by theoretical values. The FlexKnot model is tested in the framework of the realistic data analysis pipeline of the REACH global signal experiment using simulated antenna temperature data. We demonstrate that the FlexKnot model performs better than existing signal models, e.g. the Gaussian signal model, at reconstructing the shape of the true signals present in the simulated REACH data, especially for injected signals with complex structures. The capabilities of the FlexKnot signal model is also tested by introducing various systematics and simulated global signals of different types. These tests show that four to five knots are sufficient to recover the general shape of most realistic injected signals, with or without sinusoidal systematics. We show that true signals whose absorption trough is of amplitude between 120 and 450 mK can be well recovered with systematics up to about 50 mK. [ABSTRACT FROM AUTHOR]

Long-period comets C/2019 L3 (ATLAS) and C/2020 P3 (ATLAS) remain active at heliocentric distance greater than 4 au. Broad-band Charge-Coupled Device (CCD) photometry of long-period comets C/2019 L3 and C/2020 P3 is analysed in this paper. The observation was obtained with three telescopes: ZEISS-1000 at Simeiz Observatory, Maksutov at Abastumani Astrophysical Observatory, and ZTSh at Crimean Astrophysical Observatory. The R -band A (0) f ρ values of C/2019 L3 range from (5043 ± 244) to (13611 ± 1874) cm, and tend to decrease first and then increase. The average colour indices of C/2019 L3 are B – V = 0.75 ± 0.06, V – R = 0.28 ± 0.05, and R – I = 0.21 ± 0.05. The reddening of C/2019 L3 exhibits variations during the observational runs, ranging from (13.75 ± 1.07) to (−15.69 ± 0.37) per cent/kÅ with an average value of (0.94 ± 0.23) per cent/kÅ. It possesses a non-steady coma since the average gradient of its surface brightness profile is −1.60. For comet C/2020 P3, its R -band A (0) f ρ values range from (606 ± 31) to (869 ± 20) cm, the colour indices are B–V = 1.04 ± 0.09, V–R = 0.26 ± 0.05, and R – I = 0.76 ± 0.04, and the reddening is (−6.65 ± 0.01) per cent/kÅ. The colour indices of the two comets differ from those of other long-period comets. Compared with other long-period comets, comet C/2019 L3 is very active at heliocentric distance of ∼4 au, while C/2020 P3 is moderately active at heliocentric distance of ∼7 au. [ABSTRACT FROM AUTHOR]

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within‐subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly. [ABSTRACT FROM AUTHOR]

Multimodality or multiconstruct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high‐resolution imaging collections, it is of great interest to identify predictive imaging markers and intermodality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher‐order terms can only be included in the predictive mechanism following a two‐step procedure, suffering from potential misspecification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and intermodality interactions within the same inference platform in the presence of high‐dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multimodality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions. [ABSTRACT FROM AUTHOR]

In recent years, public policymakers in Europe have become increasingly aware of the need to support Europe's failing semiconductor industry. This is an emerging policy area, and this paper examines the current state of the industry in Europe and assesses its potential future. It contends that the competitiveness of the industry will be related to its innovative capacity, especially its capability to engage in processes of open innovation. The industry in Europe is largely located in a number of discrete regional clusters, and the analysis focuses on data collected from a series of interviews with lead representatives of these clusters. The analysis indicates that the challenges facing the industry stem from the structure of the industry in Europe and the structure of the wider European technology industry. It is concluded that policies, such as the introduction of the European Chips Act, are likely to have a limited, or even negative, impact on the types of open innovation–led growth that will improve long-term competitiveness. [ABSTRACT FROM AUTHOR]

Recurrent events—outcomes that an individual can experience repeatedly over the course of follow-up—are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates, which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide code to ease application of this method. [ABSTRACT FROM AUTHOR]

The aim of this paper is twofold. First, a new functional representation of accelerometer data of a distributional nature is introduced to build a complete individualized profile of each subject's physical activity levels. Second, we extend two nonparametric functional regression models, kernel smoothing and kernel ridge regression, to handle survey data and obtain reliable conclusions about the influence of physical activity. The advantages of the proposed distributional representation are demonstrated through various analyses performed on the NHANES cohort, which possesses a complex sampling design. [ABSTRACT FROM AUTHOR]

Responsive survey design (RSD) aims to increase the efficiency of survey data collection via live monitoring of paradata and the introduction of protocol changes when survey errors and increased costs seem imminent. Daily predictions of response propensity for all active sampled cases are among the most important quantities for live monitoring of data collection outcomes, making sound predictions of these propensities essential for the success of RSD. Because it relies on real-time updates of prior beliefs about key design quantities, such as predicted response propensities, RSD stands to benefit from Bayesian approaches. However, empirical evidence of the merits of these approaches is lacking in the literature, and the derivation of informative prior distributions is required for these approaches to be effective. In this paper, we evaluate the ability of two approaches to deriving prior distributions for the coefficients defining daily response propensity models to improve predictions of daily response propensity in a real data collection employing RSD. The first approach involves analyses of historical data from the same survey, and the second approach involves literature review. We find that Bayesian methods based on these two approaches result in higher-quality predictions of response propensity than more standard approaches ignoring prior information. This is especially true during the early-to-middle periods of data collection, when survey managers using RSD often consider interventions. [ABSTRACT FROM AUTHOR]