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One of the main capabilities of atom probe tomography (APT) is the ability to not only identify but also characterize early stages of precipitation at length scales that are not achievable by other techniques. One of the most popular methods to identify nanoscale clustering in APT data, based on the density-based spatial clustering of applications with noise (DBSCAN), is used extensively in many branches of research. However, it is common that not all of the steps leading to the selection of certain parameters used in the analysis are reported. Without knowing the rationale behind parameter selection, it may be difficult to compare cluster parameters obtained by different researchers. In this work, a simple open-source tool, PosgenPy, is used to justify cluster search parameter selection via providing a systematic sweep through parameter values with multiple randomizations to minimize a false-positive cluster ratio. The tool is applied to several different microstructures: a simulated material system and two experimental datasets from a low-alloy steel . The analyses show how values for the various parameters can be selected to ensure that the calculated cluster number density and cluster composition are accurate.

The A-to-G point mutation at position 3243 in the human mitochondrial genome (m.3243A > G) is the most common pathogenic mtDNA variant responsible for disease in humans. It is widely accepted that m.3243A > G levels decrease in blood with age, and an age correction representing ~ 2% annual decline is often applied to account for this change in mutation level. Here we report that recent data indicate that the dynamics of m.3243A > G are more complex and depend on the mutation level in blood in a bi-phasic way. Consequently, the traditional 2% correction, which is adequate ‘on average’, creates opposite predictive biases at high and low mutation levels. Unbiased age correction is needed to circumvent these drawbacks of the standard model. We propose to eliminate both biases by using an approach where age correction depends on mutation level in a biphasic way to account for the dynamics of m.3243A > G in blood. The utility of this approach was further tested in estimating germline selection of m.3243A > G. The biphasic approach permitted us to uncover patterns consistent with the possibility of positive selection for m.3243A > G. Germline selection of m.3243A > G shows an ‘arching’ profile by which selection is positive at intermediate mutant fractions and declines at high and low mutant fractions. We conclude that use of this biphasic approach will greatly improve the accuracy of modelling changes in mtDNA mutation frequencies in the germline and in somatic cells during aging.

Reducing neonatal and child mortality is a global priority. In countries without comprehensive vital registration data to inform policy and planning, statistical modelling is used to estimate the distribution of key causes of death. This modelling presents challenges given that the input data are few, noisy, often not nationally representative of the country from which they are derived, and often do not report separately on all of the key causes. As more nationally representative data come to be available, it becomes possible to produce country estimates that go beyond fixed-effects models with national-level covariates by incorporating country-specific random effects. However, the existing frequentist multinomial model is limited by convergence problems when adding random effects, and had not incorporated a covariate selection procedure simultaneously over all causes. We report here on the translation of a fixed effects, frequentist model into a Bayesian framework to address these problems, incorporating a misclassification matrix with the potential to correct for mis-reported as well as unreported causes. We apply the new method and compare the model parameters and predicted distributions of eight key causes of death with those based on the previous, frequentist model.

Physicians, judges, teachers, and agents in many other settings differ systematically in the decisions they make when faced with similar cases. Standard approaches to interpreting and exploiting such differences assume they arise solely from variation in preferences. We develop an alternative framework that allows variation in preferences and diagnostic skill and show that both dimensions may be partially identified in standard settings under quasi-random assignment. We apply this framework to study pneumonia diagnoses by radiologists. Diagnosis rates vary widely among radiologists, and descriptive evidence suggests that a large component of this variation is due to differences in diagnostic skill. Our estimated model suggests that radiologists view failing to diagnose a patient with pneumonia as more costly than incorrectly diagnosing one without, and that this leads less skilled radiologists to optimally choose lower diagnostic thresholds. Variation in skill can explain 39% of the variation in diagnostic decisions, and policies that improve skill perform better than uniform decision guidelines. Failing to account for skill variation can lead to highly misleading results in research designs that use agent assignments as instruments.

Players receive a return to investment that is increasing in the proportion of others who invest and the state and incur a small cost for acquiring information about the state. Their information is reflected in a stochastic choice rule, specifying the probability of a signal leading to investment. If discontinuous stochastic choice rules are infinitely costly, there is a unique equilibrium as costs become small, in which actions are a best response to a uniform (Laplacian) belief over the proportion of others investing. Infeasibility of discontinuous stochastic choice rules captures the idea that it is impossible to perfectly distinguish states that are arbitrarily close together and is both empirically documented and satisfied by many natural micro-founded cost functionals on information. Our results generalize global game selection results and establish that they do not depend on the specific additive noise information structure.

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m−2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM–WGP) demonstrated that CGM–WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modelling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.

Adaptive challenges that humans faced as they expanded across the globe left specific molecular footprints that can be decoded in our today's genomes. Different sets of metrics are used to identify genomic regions that have undergone selection. However, there are fewer methods capable of pinpointing the allele ultimately responsible for this selection. Here, we present PopHumanVar, an interactive online application that is designed to facilitate the exploration and thorough analysis of candidate genomic regions by integrating both functional and population genomics data currently available. PopHumanVar generates useful summary reports of prioritized variants that are putatively causal of recent selective sweeps. It compiles data and graphically represents different layers of information, including natural selection statistics, as well as functional annotations and genealogical estimations of variant age, for biallelic single nucleotide variants (SNVs) of the 1000 Genomes Project phase 3. Specifically, PopHumanVar amasses SNV-based information from GEVA, SnpEFF, GWAS Catalog, ClinVar, RegulomeDB and DisGeNET databases, as well as accurate estimations of iHS, nSL and iSAFE statistics. Notably, PopHumanVar can successfully identify known causal variants of frequently reported candidate selection regions, including EDAR in East-Asians, ACKR1 (DARC) in Africans and LCT/MCM6 in Europeans. PopHumanVar is open and freely available at https://pophumanvar.uab.cat., Graphical Abstract Graphical AbstractPopHumanVar is an interactive online application that is designed to facilitate the exploration and thorough analysis of candidate genomic regions by integrating both functional and population genomics data currently available. PopHumanVar generates useful summary reports of prioritized variants that are putatively causal of recent selective sweeps.

With high-quality data from programs like the Hubble Frontier Fields, cluster lensing has reached the point that models are dominated by systematic rather than statistical uncertainties. We introduce a Bayesian framework to quantify systematic effects by determining how different lens modelling choices affect the results. Our framework includes a new two-sample test for quantifying the difference between posterior probability distributions that are sampled by methods like Monte Carlo Markov chains. We use the framework to examine choices related to the selection and treatment of cluster member galaxies in two of the Frontier Field clusters: Abell 2744 and MACS J0416.1–2403. When selecting member galaxies, choices about depth and area affect the models; we find that model results are robust for an I-band magnitude limit of mlim ≥ 22.5 mag and a radial cut of rlim ≥ 90 arcsec (from the centre of the field), although the radial limit likely depends on the spatial extent of lensed images. Mass is typically assigned to galaxies using luminosity/mass scaling relations. We find that the slopes of the scaling relations can have significant effects on lens model parameters but only modest effects on lensing magnifications. Interestingly, scatter in the scaling relations affects the two fields differently. This analysis illustrates how our framework can be used to analyse lens modelling choices and guide future cluster lensing programs.

We characterize the selection cuts and clustering properties of a magnitude-limited sample of bright galaxies that is part of the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth data release of the Legacy Imaging Surveys (DR9). We describe changes in the DR9 selection compared to the DR8 one and we also compare the DR9 selection in three distinct regions: BASS/MzLS in the north Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic Cap (SGC). We investigate the systematics associated with the selection and assess its completeness by matching the BGS targets with the Galaxy and Mass Assembly (GAMA) survey. We measure the angular clustering for the overall bright sample (rmag ≤ 19.5) and as function of apparent magnitude and colour. This enables to determine the clustering strength r0 and slope γ by fitting a power-law model that can be used to generate accurate mock catalogues for this tracer. We use a counts-in-cells technique to explore higher order statistics and cross-correlations with external spectroscopic data sets in order to check the evolution of the clustering with redshift and the redshift distribution of the BGS targets using clustering redshifts. While this work validates the properties of the BGS bright targets, the final target selection pipeline and clustering properties of the entire DESI BGS will be fully characterized and validated with the spectroscopic data of Survey Validation. © 2021 The Author(s)., PZ, OR-M, SC, PN, and CB acknowledge support from the Science Technology Facilities Council through ST/P000541/1 andST/T000244/1. OR-M is supported by the Mexican National Council of Science and Technology (CONACyT) through grant No. 297228/440775 and funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 734374. JM gratefully acknowledges support from the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DESC0020086 and from the National Science Foundation under grant AST-1616414. Authors want to thank the GAMA collaboration for early access to GAMA DR4 data for this work. Some of the results in this paper have been derived using the healpy and HEALPIX package. We acknowledge the usage of the HyperLeda database (http://leda.univ-lyon1.fr).This work also made extensive use of the NASA Astrophysics Data System and of the astro-ph preprint archive at arXiv.org. This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital grants ST/K00042X/1, ST/P002293/1, and ST/R002371/1, Durham University and STFC operations grant ST/R000832/1. DiRAC is part of the National e-Infrastructure. This research used resources of the National Energy Research Scientific Computing Center (NERSC). NERSC is a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. This research is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-05CH1123, and by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; additional support for DESI is provided by the U.S. National Science Foundation, Division of Astronomical Sciences under Contract No. AST-0950945 to the NSF’s National Optical-Infrared Astronomy Research Laboratory; the Science and Technologies Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the National Council of Science and Technology of Mexico; the Ministry of Economy of Spain, and by the DESI Member Institutions. The authors are honored to be permitted to conduct astronomical research on Iolkam Du’ag (Kitt Peak), a mountain with particular significance to the Tohono O’odham Nation., With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709.

This article discusses the purposes and drivers of handouts in party primaries in Malawi. We argue that existing explanations of handouts are incomplete because they are developed to identify dynamics in presidential or legislative elections. Rules of national elections are constitutionally protected, and their fairness is monitored by both local and international observers. In contrast, rules guiding candidate selection in parties are less institutionalized, and, in some contexts, such as Malawi, a secret ballot cannot be taken for granted. This underlines the need for empirical studies of the logic of handouts at the candidate selection stage to study how differences in institutional context affect the strategic choices behind engaging in the practice. We demonstrate why such an analysis needs to be informed by an in-depth knowledge about the party organizations in which the candidate selection process itself takes place. Through qualitative interviews with members of selectorates and party primary contestants, we find that the institutional environment affects the propensity to use handouts. Mechanisms such as leadership interventions and the opportunity to stand as independent candidates make primary outcomes less final. This reduces the authority of local party organizations to control candidate selection and appropriates the use of patrimonial strategies.

Law and economics theorists have long advanced theories of litigation and settlement, including the canonical Landes–Posner–Gould (LPG) and Priest and Klein (PK) models. Famously, PK predict that, as settlement rates rise, plaintiff win rates approach 50%. So far, though, empirical testing of these models has been hampered by two major limitations: first, these models make clear predictions about the effect of case stakes on settlement rates and plaintiff win rates, but lack of reliable data on stakes means these predictions have gone untested. Second, most of the studies have used data from the United States, a high-settlement, high-litigation-costs setting, and the generalizability of these models to other institutional settings has been less explored. We use a novel data set of Taiwanese court data to test previously untested predictions of the LPG and PK models and explore the extent to which these models apply to a low-settlement, low-litigation-cost setting. We find strong support for the predictions of the LPG model that we test but at best weak support for the 50% hypothesis of the PK model, consistent with recent research suggesting that the hypothesis will have limited applicability in a low-settlement, low-litigation-cost environment. These results suggest directions for research and provide guidance to policy on the role that stakes play in the filing and settlement of claims.

In natural fertilisation, the female reproductive tract allows only a strictly selected sperm subpopulation to proceed in the vicinity of an unfertilised oocyte. Female-mediated sperm selection (also known as cryptic female choice (CFC)) is far from a random process, which frequently biases paternity towards particular males over others. Earlier studies have shown that CFC is a ubiquitous phenomenon in the animal kingdom and often promotes assortative fertilisation between genetically compatible mates. Here, I demonstrate that CFC for genetic compatibility likely also occurs in humans and is mediated by a complex network of interacting male and female genes. I also show that the relative contribution of genetic compatibility (i.e. the male–female interaction effect) to reproductive success is generally high and frequently outweighs the effects of individual males and females. Together, these facts indicate that, along with male- and female-dependent pathological factors, reproductive failure can also result from gamete-level incompatibility of the reproductive partners. Therefore, I argue that a deeper understanding of these evolutionary mechanisms of sperm selection can pave the way towards a more inclusive view of infertility and open novel possibilities for the development of more personalised infertility diagnostics and treatments.

Evolvable traits of organisms can alter the environment those organisms experience. While it is well appreciated that those modified environments can influence natural selection to which organisms are exposed, they can also influence the expression of genetic variances and covariances of traits under selection. When genetic variance and covariance change in response to changes in the evolving, modified environment, rates and outcomes of evolution also change. Here we discuss the basic mechanisms whereby organisms modify their environments, review how those modified environments have been shown to alter genetic variance and covariance, and discuss potential evolutionary consequences of such dynamics. With these dynamics, responses to selection can be more rapid and sustained, leading to more extreme phenotypes, or they can be slower and truncated, leading to more conserved phenotypes. Patterns of correlated selection can also change, leading to greater or less evolutionary independence of traits, or even causing convergence or divergence of traits, even when selection on them is consistent across environments. Developing evolutionary models that incorporate changes in genetic variances and covariances when environments themselves evolve requires developing methods to predict how genetic parameters respond to environments—frequently multifactorial environments. It also requires a population-level analysis of how traits of collections of individuals modify environments for themselves and/or others in a population, possibly in spatially explicit ways. Despite the challenges of elucidating the mechanisms and nuances of these processes, even qualitative predictions of how environment-modifying traits alter evolutionary potential are likely to improve projections of evolutionary outcomes.

Though prior studies have analyzed the textual characteristics of online comments about politics, less is known about how selection into commenting behavior and exposure to other people’s comments changes the tone and content of political discourse. This article makes three contributions. First, we show that frequent commenters on Facebook are more likely to be interested in politics, to have more polarized opinions, and to use toxic language in comments in an elicitation task. Second, we find that people who comment on articles in the real world use more toxic language on average than the public as a whole; levels of toxicity in comments scraped from media outlet Facebook pages greatly exceed what is observed in comments we elicit on the same articles from a nationally representative sample. Finally, we demonstrate experimentally that exposure to toxic language in comments increases the toxicity of subsequent comments.

Generalizing estimates of causal effects from an experiment to a target population is of interest to scientists. However, researchers are usually constrained by available covariate information. Analysts can often collect many fewer variables from population samples than from experimental samples, which has limited applicability of existing approaches that assume rich covariate data from both experimental and population samples. In this article, we examine how to select covariates necessary for generalizing experimental results under such data constraints. In our concrete context of a large-scale development program in Uganda, although more than 40 pre-treatment covariates are available in the experiment, only 8 of them were also measured in a target population. We propose a method to estimate a separating set—a set of variables affecting both the sampling mechanism and treatment effect heterogeneity—and show that the population average treatment effect (PATE) can be identified by adjusting for estimated separating sets. Our algorithm only requires a rich set of covariates in the experimental data, not in the target population, by incorporating researcher-specific constraints on what variables are measured in the population data. Analysing the development experiment in Uganda, we show that the proposed algorithm can allow for the PATE estimation in situations where conventional methods fail due to data requirements.

Splicing of pre-mRNA is initiated by binding of U1 to the 5′ splice site and of Msl5-Mud2 heterodimer to the branch site (BS). Subsequent binding of U2 displaces Msl5-Mud2 from the BS to form the prespliceosome, a step governing branchpoint selection and hence 3′ splice site choice, and linking splicing to myelodysplasia and many cancers in human. Two DEAD-box proteins, Prp5 and Sub2, are required for this step, but neither is stably associated with the pre-mRNA during the reaction. Using BS-mutated ACT1 pre-mRNA, we previously identified a splicing intermediate complex, FIC, which contains U2 and Prp5, but cannot bind the tri-snRNP. We show here that Msl5 remains associated with the upstream cryptic branch site (CBS) in the FIC, with U2 binding a few bases downstream of the BS. U2 mutants that restore U2-BS base pairing enable dissociation of Prp5 and allows splicing to proceed. The CBS is required for splicing rescue by compensatory U2 mutants, and for formation of FIC, demonstrating a role for Msl5 in directing U2 to the BS, and of U2-BS base pairing for release of Prp5 and Msl5-Mud2 to form the prespliceosome. Our results provide insights into how the prespliceosome may form in normal splicing reaction.

Plastic phenotypic responses to environmental change are common, yet we lack a clear understanding of the fitness consequences of these plastic responses. Here, we use the evolution of herbicide resistance in the common morning glory (Ipomoea purpurea) as a model for understanding the relative importance of adaptive and maladaptive gene expression responses to herbicide. Specifically, we compare leaf gene expression changes caused by herbicide to the expression changes that evolve in response to artificial selection for herbicide resistance. We identify a number of genes that show plastic and evolved responses to herbicide and find that for the majority of genes with both plastic and evolved responses, plastic responses appear to be adaptive. We also find that selection for herbicide response increases gene expression plasticity. Overall, these results show the importance of adaptive plasticity for herbicide resistance in a common weed and that expression changes in response to strong environmental change can be adaptive., Impact Statement Predicting whether and how organisms will adapt to environmental change is a crucial goal. However, this goal can be complicated because environmental change can alter traits, in a process called plasticity. The extent and fitness consequences of plasticity will have important effects on the adaptive process. In this study, we use adaptation to herbicide in the agricultural weed, the common morning glory, as a model for understanding the extent and fitness consequences of plasticity in gene expression. We find evidence that gene expression plasticity is adaptive in the presence of herbicide, suggesting that understanding plasticity is crucial for understanding how organisms adapt to new environments.

Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling-based methods with relative ease and DIC is readily available in various Bayesian software. We find that sensitivity of DIC-based selection can be high, in the range of 90–100%. However, correct selection by DIC can be in the range of 0–2%. These performances persist consistently with increase in sample size. We establish that both marginal likelihood and DIC asymptotically disfavour under-fitted models, explaining the high sensitivities of both criteria. However, mis-selection probability of DIC remains bounded below by a positive constant in linear models with g-priors whereas mis-selection probability by marginal likelihood converges to 0 under certain conditions. A consequence of our results is that not only the DIC cannot asymptotically differentiate between the data-generating and an over-fitted model, but, in fact, it cannot asymptotically differentiate between two over-fitted models as well. We illustrate these results in multiple simulation studies and in a biomarker selection problem on cancer cachexia of non-small cell lung cancer patients. We further study the performances of HPM and DIC in generalized linear model as practitioners often choose to use DIC that is readily available in software in such non-conjugate settings.

Two competing theories of social support and role specialization have been invoked to explain how marital status affects labour market outcomes. Whereas evidence of beneficial labour market outcomes among married men and employed married women favours a social support perspective, evidence of married women’s reduced labour market participation corresponds to a role specialization perspective. We make two contributions to this literature. First, we apply both theories to first jobs in positions for which the employee is overeducated (educational attainment exceeding job requirements). Second, we employ preference theory to show how selection into marriage shapes its association with being in a position of overeducation. To account for potential selection, we model the probability of being married and the risk of starting a career in overeducation simultaneously based on retrospective data from the German National Educational Panel Study. In line with the theory of role specialization, married women seem to be more likely to start in overeducation than unmarried women. However, adjusting for selection into marriage yields lower risks of overeducation for married women and men in comparison to their unmarried counterparts. This supports the social support perspective on overqualification and highlights how selection into marriage shapes its association with labour market outcomes.

Background Primary and incisional ventral hernia trials collect unstandardized inconsistent data, limiting data interpretation and comparison. This study aimed to create two minimum data sets for primary and incisional ventral hernia interventional trials to standardize data collection and improve trial comparison. To support these data sets, standardized patient-reported outcome measures and trial methodology criteria were created. Methods To construct these data sets, nominal group technique methodology was employed, involving 15 internationally recognized abdominal wall surgeons and two patient representatives. Initially a maximum data set was created from previous systematic and panellist reviews. Thereafter, three stages of voting took place: stage 1, selection of the number of variables for data set inclusion; stage 2, selection of variables to be included; and stage 3, selection of variable definitions and detection methods. A steering committee interpreted and analysed the data. Results The maximum data set contained 245 variables. The three stages of voting commenced in October 2019 and had been completed by July 2020. The final primary ventral hernia data set included 32 variables, the incisional ventral hernia data set included 40 variables, the patient-reported outcome measures tool contained 25 questions, and 40 methodological criteria were chosen. The best known variable definitions were selected for accurate variable description. CT was selected as the optimal preoperative descriptor of hernia morphology. Standardized follow-up at 30 days, 1 year, and 5 years was selected. Conclusion These minimum data sets, patient-reported outcome measures, and methodological criteria have allowed creation of a manual for investigators aiming to undertake primary ventral hernia or incisional ventral hernia interventional trials. Adopting these data sets will improve trial methods and comparisons.

Genetic variation is the raw material upon which selection acts. The majority of environmental conditions change over time and therefore may result in variable selective effects. How temporally fluctuating environments impact the distribution of fitness effects and in turn population diversity is an unresolved question in evolutionary biology. Here, we employed continuous culturing using chemostats to establish environments that switch periodically between different nutrient limitations and compared the dynamics of selection to static conditions. We used the pooled Saccharomyces cerevisiae haploid gene deletion collection as a synthetic model for populations comprising thousands of unique genotypes. Using barcode sequencing (barseq), we find that static environments are uniquely characterized by a small number of high fitness genotypes that rapidly dominate the population leading to dramatic decreases in genetic diversity. By contrast, fluctuating environments are enriched in genotypes with neutral fitness effects and an absence of extreme fitness genotypes contributing to the maintenance of genetic diversity. We also identified a unique class of genotypes whose frequencies oscillate sinusoidally with a period matching the environmental fluctuation. Oscillatory behavior corresponds to large differences in short term fitness that are not observed across long timescales pointing to the importance of balancing selection in maintaining genetic diversity in fluctuating environments. Our results are consistent with a high degree of environmental specificity in the distribution of fitness effects and the combined effects of reduced and balancing selection in maintaining genetic diversity in the presence of variable selection.

BackgroundWrong patient selection errors may be tracked by retract–reorder (RAR) events. The aim of this quality improvement study was to assess the impact of reducing the number of concurrently open electronic health records from 4 to 2 on RAR errors generated by a tele-critical care service.MethodsThe study encompassed 32 months before and 21 months after restriction. Chi-Square test of proportions and T statistical process control chart for rare events were used.ResultsThere were 156 318 orders with 57 RAR errors (36.5/100 000 orders) before restriction, and 122 587 orders with 34 errors (27.7/100 000 orders) after. Rates were not statistically different (P = .20), but analysis was underpowered. When plotted on a T control chart, random variation was detected between RAR errors.ConclusionWe found no significant difference in RAR errors in the tele-critical care setting after open record limitation. Other strategies should be studied to reduce wrong patient selection errors.

Natural hybrid zones offer a powerful framework for understanding the genetic basis of speciation in progress because ongoing hybridization continually creates unfavorable gene combinations. Evidence indicates that postzygotic reproductive isolation is often caused by epistatic interactions between mutations in different genes that evolved independently of one another (hybrid incompatibilities). We examined the potential to detect epistatic selection against incompatibilities from genome sequence data using the site frequency spectrum (SFS) of polymorphisms by conducting individual-based simulations in SLiM. We found that the genome-wide SFS in hybrid populations assumes a diagnostic shape, with the continual input of fixed differences between source populations via migration inducing a mass at intermediate allele frequency. Epistatic selection locally distorts the SFS as non-incompatibility alleles rise in frequency in a manner analogous to a selective sweep. Building on these results, we present a statistical method to identify genomic regions containing incompatibility loci that locates departures in the local SFS compared with the genome-wide SFS. Cross-validation studies demonstrate that our method detects recessive and codominant incompatibilities across a range of scenarios varying in the strength of epistatic selection, migration rate, and hybrid zone age. Our approach takes advantage of whole genome sequence data, does not require knowledge of demographic history, and can be applied to any pair of nascent species that forms a hybrid zone.

Aims Identifying the potential role of vegetation context (defined as the density, species identity/diversity and height of co-occurring plants) in modifying selection on floral traits is a critical step for clarifying and predicting the floral evolutionary trajectory in complex co-flowering species competition environments. It is also helpful to understand the variation in pollinator-mediated selection. Methods We experimentally reduced vegetation height around individual plants of Spiranthes sinensis (a bumblebee-pollinated perennial orchid herb) and estimated how vegetation context modified selection on four floral traits (flowering start, plant height, corolla size and number of flowers) through female function and pollen removal over two continuous years. We randomly selected independent plants in each year. Important Findings We demonstrated that vegetation context modified selection for earlier flowering start and shorter plant height of S. sinensis. The strength of selection differed between years. In addition, selection was stronger through female function than through pollen removal. Our findings indicate the potential role of vegetation context in shaping the differentiation and diversification of flowers in angiosperms.

The strength of the stalk rind, measured as rind penetrometer resistance (RPR), is an important contributor to stalk lodging resistance. To enhance the genetic architecture of RPR, we combined selection mapping on populations developed by 15 cycles of divergent selection for high and low RPR with time-course transcriptomic and metabolic analyses of the stalks. Divergent selection significantly altered allele frequencies of 3,656 and 3,412 single- nucleotide polymorphisms (SNPs) in the high and low RPR populations, respectively. Surprisingly, only 110 (1.56%) SNPs under selection were common in both populations, while the majority (98.4%) were unique to each population. This result indicated that high and low RPR phenotypes are produced by biologically distinct mechanisms. Remarkably, regions harboring lignin and polysaccharide genes were preferentially selected in high and low RPR populations, respectively. The preferential selection was manifested as higher lignification and increased saccharification of the high and low RPR stalks, respectively. The evolution of distinct gene classes according to the direction of selection was unexpected in the context of parallel evolution and demonstrated that selection for a trait, albeit in different directions, does not necessarily act on the same genes. Tricin, a grass-specific monolignol that initiates the incorporation of lignin in the cell walls, emerged as a key determinant of RPR. Integration of selection mapping and transcriptomic analyses with published genetic studies of RPR identified several candidate genes including ZmMYB31, ZmNAC25, ZmMADS1, ZmEXPA2, ZmIAA41 and hk5. These findings provide a foundation for an enhanced understanding of RPR and the improvement of stalk lodging resistance.

Motivation When designing prediction models built with many features and relatively small sample sizes, feature selection methods often overfit training data, leading to selection of irrelevant features. One way to potentially mitigate overfitting is to incorporate domain knowledge during feature selection. Here, a feature ranking algorithm called ‘Family Rank’ is presented in which features are ranked based on a combination of graphical domain knowledge and feature scores computed from empirical data. Results A simulated dataset is used to demonstrate a scenario in which family rank outperforms other state-of-the-art graph based ranking algorithms, decreasing the sample size needed to detect true predictors by 2- to 3-fold. An example from oncology is then used to explore a real-world application of family rank. Availability and implementation An implementation of Family Rank is freely available at https://cran.r-project.org/package=FamilyRank. Supplementary information Supplementary data are available at Bioinformatics online.

Synopsis The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Genome size in cellular organisms varies by six orders of magnitude, yet the cause of this large variation remains unexplained. The influential Drift-Barrier Hypothesis proposes that large genomes tend to evolve in small populations due to inefficient selection. However, to our knowledge no explicit tests of the Drift-Barrier Hypothesis have been reported. We performed the first explicit test, by comparing estimated census population size and genome size in mammals while incorporating potential covariates and the effect of shared evolutionary history. We found a lack of correlation between census population size and genome size among 199 species of mammals. These results suggest that population size is not the predominant factor influencing genome size and that the Drift-Barrier Hypothesis should be considered provisional.

In present paper, thermodynamic analysis and techno-economic assessment of an organic Rankine cycle (ORC) coupled with solar parabolic trough collector (PTC) are investigated. The thermodynamic and economic models, based on the principles of thermodynamics and heat transfer, as well as dynamic economic analysis, are developed for the components of the combined system. By performing a parametric study, the effects of outlet temperature (TPTC,out) and temperature difference between inlet and outlet of PTC (ΔT) and pinch point temperature difference of heat exchangers on the thermo-economic performance are evaluated. Moreover, under reasonable thermodynamic boundary conditions, the optimal operational parameters are obtained via a Pareto frontier solution. It is indicated that the improving TPTC,out is beneficial to the thermo-economic performance. Among the fluid candidates, toluene yields the best energy, exergetic and economic performance. There exists an optimal ΔT at which the thermal and exergy efficiency achieve the maximum. The optimal ΔT is 19–26°C for the screened working fluids. The pinch point temperature difference of evaporator has different impact on the system performance compared with that of condenser. The evaporator pinch point temperature difference has slight influence on the economic performance when it is larger than 10°C. While there is optimized condenser pinch point temperature difference (7–8°C), the economic performance is the optimal. The PTC and heat exchanger are the main source of total investment cost, account for more than 75%. The Pareto frontier solutions can provide a design guide for the decision maker.

The impact of human-mediated environmental change on the evolutionary trajectories of wild organisms is poorly understood. In particular, species’ capacities to adapt rapidly (in hundreds of generations or less), reproducibly and predictably to extreme environmental change is unclear. Silene uniflora is predominantly a coastal species, but it has also colonised isolated, disused mines with phytotoxic, zinc-contaminated soils. To test whether rapid, parallel adaptation to anthropogenic pollution has taken place, we used reduced representation sequencing (ddRAD) to reconstruct the evolutionary history of geographically proximate mine and coastal population pairs and found largely independent colonisation of mines from different coastal sites. Furthermore, our results show that parallel evolution of zinc tolerance has occurred without gene flow spreading adaptive alleles between mine populations. In genomic regions where signatures of selection were detected across multiple mine-coast pairs, we identified genes with functions linked to physiological differences between the putative ecotypes, although genetic differentiation at specific loci is only partially shared between mine populations. Our results are consistent with a complex, polygenic genetic architecture underpinning rapid adaptation. This shows that even under a scenario of strong selection and rapid adaptation, evolutionary responses to human activities (and other environmental challenges) may be idiosyncratic at the genetic level and, therefore, difficult to predict from genomic data. © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Tree neighbourhood conditions vary greatly through time for individual trees and throughout a forest at any given time. Therefore, many trees experience very different neighbourhood conditions to the stand mean. Although changes in stand density or species composition are often recommended to mitigate the effects of global change on forest functioning, these recommendations are often based on stand means and lack guidance on acceptable variability around the means or acceptable ranges in neighbourhood conditions. This study examined how the spatial and temporal variability in tree neighbourhood conditions varies between species and with stand structural characteristics. A trees neighbourhood, defined by all trees within a 10-m radius, was quantified in terms of basal area GN, relative height rhN (tree height divided by mean height of all trees in neighbourhood), species proportion PropN and species richness RN. Weibull functions were used to describe the frequency distributions of GN, rhN, PropN and RN within the stands. Equations were developed to describe the Weibull shape, scale and location parameters as functions of the species, stand basal area, mean tree diameter and management (even-aged vs single-tree selection forests). All of these variables significantly influenced the frequency distributions of neighbourhood characteristics. These equations can be used to show the proportion of trees experiencing significantly higher or lower values of a given characteristic than the stand mean or other threshold values relevant for management objectives. Single-tree selection forests had much greater ranges in neighbourhoods. There were also large temporal changes in tree neighbourhoods, especially in single-tree selection forests. Large trees had not experienced better neighbourhood conditions than smaller trees of approximately the same age, when considered over the long-term. Given that foresters typically consider tree neighbourhoods when marking trees for thinning, and that trees respond to tree neighbourhood conditions rather than to mean stand conditions, stand-level recommendations may be easier to transfer into practice and to interpret, when accompanied by neighbourhood-level information that indicates the distribution of neighbourhoods that actually exist within a stand.

Alternative synonymous codons are often used at unequal frequencies. Classically, studies of such codon usage bias (CUB) attempted to separate the impact of neutral from selective forces by assuming that deviations from a predicted neutral equilibrium capture selection. However, GC-biased gene conversion (gBGC) can also cause deviation from a neutral null. Alternatively, selection has been inferred from CUB in highly expressed genes, but the accuracy of this approach has not been extensively tested, and gBGC can interfere with such extrapolations (e.g., if expression and gene conversion rates covary). It is therefore critical to examine deviations from a mutational null in a species with no gBGC. To achieve this goal, we implement such an analysis in the highly AT rich genome of Dictyostelium discoideum, where we find no evidence of gBGC. We infer neutral CUB under mutational equilibrium to quantify “adaptive codon preference,” a nontautologous genome wide quantitative measure of the relative selection strength driving CUB. We observe signatures of purifying selection consistent with selection favoring adaptive codon preference. Preferred codons are not GC rich, underscoring the independence from gBGC. Expression-associated “preference” largely matches adaptive codon preference but does not wholly capture the influence of selection shaping patterns across all genes, suggesting selective constraints associated specifically with high expression. We observe patterns consistent with effects on mRNA translation and stability shaping adaptive codon preference. Thus, our approach to quantifying adaptive codon preference provides a framework for inferring the sources of selection that shape CUB across different contexts within the genome.

Characterization of physiological traits affected by rice breeding for grain yield under drought conditions shows that atmospheric and soil stresses strengthen the relationship between leaf traits and yield across extensive trials in South Asia., Direct selection for yield under drought has resulted in the release of a number of drought-tolerant rice varieties across Asia. In this study, we characterized the physiological traits that have been affected by this strategy in breeding trials across sites in Bangladesh, India, and Nepal. Drought- breeding lines and drought-tolerant varieties showed consistently longer flag leaves and lower stomatal density than our drought-susceptible check variety, IR64. The influence of environmental parameters other than drought treatments on leaf traits was evidenced by close grouping of treatments within a site. Flag-leaf length and width appeared to be regulated by different environmental parameters. In separate trials in the Philippines, the same breeding lines studied in South Asia showed that canopy temperature under drought and harvest index across treatments were most correlated with grain yield. Both atmospheric and soil stress strengthened the relationships between leaf traits and yield. The stable expression of leaf traits among genotypes and the identification of the environmental conditions in which they contribute to yield, as well as the observation that some breeding lines showed longer time to flowering and higher canopy temperature than IR64, suggest that selection for additional physiological traits may result in further improvements of this breeding pool.

Solution intervals are often used to improve the signal-to-noise ratio during radio interferometric gain calibration. This work investigates how factors such as the noise level, intrinsic gain variability, degree of model incompleteness, and the presence of radio frequency interference impact the selection of solution intervals for calibration. We perform different interferometric simulations to demonstrate how these factors, in combination with the choice of solution intervals, affect calibration and imaging outputs and discuss practical guidelines for choosing optimal solution intervals. Furthermore, we present an algorithm capable of automatically selecting suitable solution intervals during calibration. By applying the algorithm to both simulated and real data, we show that it can successfully choose solution intervals that strike a good balance between capturing intrinsic gain variability and not fitting noise as long as the data are not too inhomogeneously flagged. Furthermore, we elaborate on several practical aspects that emphasize the need to develop regularized calibration algorithms that do not require solution intervals.

Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric environments such as the linear model or additive variants thereof. In this work, we abandon the linear model framework, which can be quite detrimental when the covariates impact the outcome in a non-linear way, and turn to tree-based methods for variable selection. Such variable screening is traditionally done by pruning down large trees or by ranking variables based on some importance measure. Despite heavily used in practice, these ad hoc selection rules are not yet well understood from a theoretical point of view. In this work, we devise a Bayesian tree-based probabilistic method and show that it is consistent for variable selection when the regression surface is a smooth mix of p > n covariates. These results are the first model selection consistency results for Bayesian forest priors. Probabilistic assessment of variable importance is made feasible by a spike-and-slab wrapper around sum-of-trees priors. Sampling from posterior distributions over trees is inherently very difficult. As an alternative to Markov Chain Monte Carlo (MCMC), we propose approximate Bayesian computation (ABC) Bayesian forests, a new ABC sampling method based on data-splitting that achieves higher ABC acceptance rate. We show that the method is robust and successful at finding variables with high marginal inclusion probabilities. Our ABC algorithm provides a new avenue towards approximating the median probability model in non-parametric setups where the marginal likelihood is intractable.

Many species are frequently faced with the decision about how to balance the use of thermal refuge against access to food resources. We evaluated the habitat use of female greater prairie chickens (Tympanuchus cupido) to assess the potential for trade-offs between thermal conditions and food resources during the habitat selection process. Our objectives were to 1) compare near-ground temperatures, invertebrate availability, and vegetation characteristics at sites used by greater prairie chickens to conditions at random landscape locations in various time since fire patches and 2) assess changes in conditions at used sites throughout the day to determine if selection for resources changes relative to ambient conditions, resulting in trade-offs between foraging sites and thermal refuge. We found that greater prairie chickens primarily used patches 0–12 months postfire that had relatively high abundances and biomasses of invertebrates compared to other time since fire patches. Greater prairie chickens further modified their selection at relatively fine spatial scales within these food-rich patches to select for areas with cooler temperatures during the hottest part of the day. The use of thermal refuge did not appear to influence the access to food resources as invertebrate abundance and biomass at used sites were consistent throughout the day. Our results show that food resources and thermal cover influences habitat selection for greater prairie chickens, but there was little evidence for trade-offs during the habitat selection process. Consideration of spatial and temporal scales is critical for evaluating trade-offs in habitat selection for animals and this research provides insights into the decision-making process by prairie chickens.

The effects of sexual selection are more conspicuous among male animals, and, as a result, the majority of sexual selection research focuses on males. However, burgeoning evidence suggests that sexual selection also acts on females, and there have been calls for an increased focus on females. Here, we used a multivariate approach to analyze sexual selection in Kawanaphila nartee, a spermatophore gift-giving bushcricket with dynamic sex roles. Early in the breeding season, females compete for males, and, later, when environmental food resources are more abundant, sex roles revert to Darwinian convention. Ear size, which is much greater in females than in males, has been suggested to affect female fitness as females with larger ears are more likely to reach calling males first under sex-role-reversed conditions. We tested this suggestion and found evidence of positive linear and nonlinear correlational selection acting on female ear size early in the breeding season (under reversed sex roles) but not later in the breeding season (under Darwinian sex roles). Interestingly, there was no correlation between mating success and reproductive success (Bateman gradient) at any time during the season. Together, our results indicate that even brief and circumscribed periods of intrasexual competition among females can lead to sexual selection on morphological characters and that this selection may not depend on multiple mating. Considering the wealth of reports in the literature of brief episodes of intrasexual competition among female animals, we recommend increased study of sexual selection acting on females.

This paper contributes to the research on multi-pillar pension systems with main focus on private pension funds (PFs). In this context, the specific objective of this study is to determine which second-pillar private fund is the best for participants in such systems on the basis of their risk profile. Based on the assumptions on utility functions of the participants in a pension scheme, four types of stochastic dominance (SD) relations are considered, specifically first order, second order, third order and SD generated by utility functions with decreasing absolute risk aversion. We conduct an analysis under two distributional assumptions: empirical and stable distribution of returns. Moreover, the investors for which non-dominated funds are the optimal choices are identified. Allowing for diversification, the efficiency of the PFs with respect to several types of SD is tested. Then, the observed behaviour of participants in the last quarter/year is compared to the results of SD analysis. Finally, the identified SD relations are stress-tested using data originating from a period of turmoil. Despite the focus on Lithuanian PFs, the methodology developed in this work can be employed by participants or PF managers in similar markets of other countries.

The test-negative study design is often used to estimate vaccine effectiveness in influenza studies, but it has also been proposed in the context of other infectious diseases, such as cholera, dengue, or Ebola. It was introduced as a variation of the case-control design, in an attempt to reduce confounding bias due to health-care–seeking behavior, and has quickly gained popularity because of its logistic advantages. However, examination of the directed acyclic graphs that describe the test-negative design reveals that without strong assumptions, the estimated odds ratio derived under this sampling mechanism is not collapsible over the selection variable, such that the results obtained for the sampled individuals cannot be generalized to the whole population. In this paper, we show that adjustment for severity of disease can reduce this bias and, under certain assumptions, makes it possible to unbiasedly estimate a causal odds ratio. We support our findings with extensive simulations and discuss them in the context of recently published cholera test-negative studies of the effectiveness of cholera vaccines.

The H$\alpha$ equivalent width (EW) is an observational proxy for specific star formation rate (sSFR) and a tracer of episodic star-formation activity. Previous assessments show that EW strongly anti-correlates with stellar mass as $M^{-0.25}$ similar to the sSFR -- stellar mass relation. However, such a correlation may be driven/formed by selection effects. In this study, we investigate how H$\alpha$ EWs correlate with galaxy properties and how selection biases could alter such correlations using a $z = 0.47$ narrowband-selected sample of 1572 H$\alpha$ emitters from the Ly$\alpha$ Galaxies in the Epoch of Reionization (LAGER) survey. The sample covers 3 deg$^2$ of COSMOS and $1.1\times10^5$ cMpc$^3$. We assume an intrinsic EW distribution to form mock samples of H$\alpha$ emitters (HAEs) and propagate the selection criteria to match observations, giving us control on how selection biases can affect the underlying results. We find EW intrinsically correlates with stellar mass as $W_0 \propto M^{-0.16\pm0.03}$ and decreases by a factor of $\sim 3$ from $10^{7}$ to $10^{10}$ M$_\odot$. We find low-mass HAEs to be $\sim 320$ times more likely to have rest-frame EW$ > 200$\AA compared to high-mass HAEs. Combining the intrinsic EW -- stellar mass correlation with an observed SMF correctly reproduces the observed H$\alpha$ LF, while not correcting for selection effects underestimates the number of bright HAEs. This suggests that the intrinsic EW -- stellar mass correlation is physically significant and reproduces three statistical distributions of galaxy populations (LF, SMF, EW distribution). At lower masses, we find there are more high-EW outliers compared to high masses, even after taking into account selection effects. Our results suggest that high sSFR outliers indicative of bursty SF activity are intrinsically more prevalent in low-mass HAEs and not a byproduct of selection effects., Comment: 21 Pages, 13 Figures, 2 Tables, accepted to MNRAS

Summary The growing availability of genomewide polymorphism data has fueled interest in detecting diverse selective processes affecting population diversity. However, no model-based approaches exist to jointly detect and distinguish the two complementary processes of balancing and positive selection. We extend the BalLeRMix B-statistic framework described in Cheng and DeGiorgio (2020) for detecting balancing selection and present BalLeRMix+, which implements five B statistic extensions based on mixture models to robustly identify both types of selection. BalLeRMix+ is implemented in Python and computes the composite likelihood ratios and associated model parameters for each genomic test position. Availability and implementation BalLeRMix+ is freely available at https://github.com/bioXiaoheng/BallerMixPlus. Supplementary information Supplementary data are available at Bioinformatics online.

Argentina and the United States were the principal destinations for Italian immigrants during the Age of Mass Migration. I assemble data following Italians from passenger lists to censuses in Argentina and the United States, enabling me to compare the economic outcomes of migrants with similar pre-migration characteristics but who moved to different countries. Italians assimilated faster in Argentina, and this advantage was unlikely to be due to selection. A higher human capital relative to natives and the Italian-Spanish similarity largely explain Italians’ advantage in Argentina. These findings highlight the importance of the fit between migrants’ characteristics and those of the receiving country.

Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several astronomical catalogues. However, some scientific articles have also shown that the training data therein contain multiple sources of bias. Hence, the performance of those classifiers on objects not belonging to the training data is uncertain, potentially resulting in the selection of incorrect models. Besides, it gives rise to the deployment of misleading classifiers. An example of the latter is the creation of open-source labelled catalogues with biased predictions. In this paper, we develop a method based on an informative marginal likelihood to evaluate variable star classifiers. We collect deterministic rules that are based on physical descriptors of RR Lyrae stars, and then, to mitigate the biases, we introduce those rules into the marginal likelihood estimation. We perform experiments with a set of Bayesian logistic regressions, which are trained to classify RR Lyraes, and we found that our method outperforms traditional non-informative cross-validation strategies, even when penalized models are assessed. Our methodology provides a more rigorous alternative to assess machine learning models using astronomical knowledge. From this approach, applications to other classes of variable stars and algorithmic improvements can be developed.

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome-wide distribution of summary statistics measuring genetic differentiation, such as FST, are often interpreted as evidence for alleles that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome-wide distribution of FST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate FST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to FST. However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of FST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Summary Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work implicitly assumes that the models are correctly specified or have fixed dimensionality, yet both model misspecification and high dimensionality are prevalent in practice. In this paper, we exploit the framework of model selection principles under the misspecified generalized linear models presented in Lv & Liu (2014), and investigate the asymptotic expansion of the posterior model probability in the setting of high-dimensional misspecified models. With a natural choice of prior probabilities that encourages interpretability and incorporates the Kullback–Leibler divergence, we suggest using the high-dimensional generalized Bayesian information criterion with prior probability for large-scale model selection with misspecification. Our new information criterion characterizes the impacts of both model misspecification and high dimensionality on model selection. We further establish the consistency of covariance contrast matrix estimation and the model selection consistency of the new information criterion in ultrahigh dimensions under some mild regularity conditions. Our numerical studies demonstrate that the proposed method enjoys improved model selection consistency over its main competitors.

Sexual dimorphism in gene expression is likely to be the underlying source of dimorphism in a variety of traits. Many analyses implicitly make the assumption that dimorphism only evolves when selection favors different phenotypes in the two sexes, although theory makes clear that it can also evolve as an indirect response to other kinds of selection. Furthermore, previous analyses consider the evolution of a single transcript or trait at a time, ignoring the genetic covariance with other transcripts and traits. We first show which aspects of the genetic-variance covariance matrix, G, affect dimorphism when these assumptions about selection are relaxed. We then reanalyze gene expression data from Drosophila melanogaster with these predictions in mind. Dimorphism of gene expression for individual transcripts shows the signature of both direct selection for dimorphism and indirect responses to selection. To account for the effect of measurement error on evolutionary predictions, we estimated a G matrix for eight linear combinations of expression traits. Sex-specific genetic variances in female- and male-biased transcription, as well as one relatively unbiased combination were quite unequal, ensuring that most forms of selection on these traits will have large effects on dimorphism. Predictions of response to selection based on the whole G matrix showed that sexually concordant and antagonistic selection are equally capable of changing sexual dimorphism. In addition, the indirect responses of dimorphism due to cross-trait covariances were quite substantial. The assumption that sexual dimorphism in transcription is an adaptation is likely to be incorrect in many specific cases.

Background In many real-world situations there are many components in a mixture that produce an enormous amount of information. Objective The main task is to build up balanced models that convert these data into meaningful information to deal with. Hence, different chemometric models were applied for the analysis of data obtained from a mixture containing sofosbuvir, ledipasvir, velpatasvir, daclatasvir, and valacyclovir that were recently used internationally for their antiviral activity. Methods Partial Least Squares, Spectral Residual Augmented Classical Least Squares, and Concentration Residual Augmented Classical Least Squares designs were applied with and without variable selection procedure [Genetic Algorithm (GA)]. The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample through handling the UV spectral data. Results Robust models were obtained by applying GA. The proposed methods were found to be rapid, simple, and required no preliminary separation steps. Conclusion These models can be used on a routine basis in quality control laboratories or factories giving competitor results to those obtained by the reported methods. Highlights The proposed models offer a powerful analytical alternative for laboratories that consider economic strategies in their requirements.

We find robust evidence that cohorts of male graduates who start college during worse economic times earn higher average wages than those who start during better times. This gap is not explained by differences in selection into employment, in economic conditions at the time of college graduation, or in field of study choices. Graduates who enroll in bad times are not more positively selected based on their high-school outcomes, but they achieve higher college grades, sort into higher-paying occupations, and earn higher wages conditional on their grades. We find similar but less robust patterns for female graduates. Our results suggest that individuals who enroll during economic downturns exert more effort during their studies.