Tag: pubmed

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2119513119. doi: 10.1073/pnas.2119513119. Epub 2022 Aug 22.

ABSTRACT

In a striking result, Louca and Pennell [S. Louca, M. W. Pennell, Nature 580, 502-505 (2020)] recently proved that a large class of phylogenetic birth-death models is statistically unidentifiable from lineage-through-time (LTT) data: Any pair of sufficiently smooth birth and death rate functions is “congruent” to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth-death models to study evolution. In this paper, we qualify their finding by proving that an alternative and widely used class of birth-death models is indeed identifiable. Specifically, we show that piecewise constant birth-death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant timetree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models. The sampling requirements needed for our results to hold are explicit and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic.

PMID:35994663 | DOI:10.1073/pnas.2119513119

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2121338119. doi: 10.1073/pnas.2121338119. Epub 2022 Aug 22.

ABSTRACT

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.

PMID:35994661 | DOI:10.1073/pnas.2121338119

Proc Natl Acad Sci U S A. 2022 Aug 30;119(35):e2203822119. doi: 10.1073/pnas.2203822119. Epub 2022 Aug 22.

ABSTRACT

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

PMID:35994637 | DOI:10.1073/pnas.2203822119

Retina. 2022 Sep 1;42(9):1673-1682. doi: 10.1097/IAE.0000000000003557.

ABSTRACT

BACKGROUND/PURPOSE: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images.

METHODS: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes.

RESULTS: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9.

CONCLUSION: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.

PMID:35994584 | DOI:10.1097/IAE.0000000000003557

IEEE Trans Pattern Anal Mach Intell. 2022 Aug 22;PP. doi: 10.1109/TPAMI.2022.3200745. Online ahead of print.

ABSTRACT

Photon-efficient imaging, which captures 3D images with single-photon sensors, has enabled a wide range of applications. However, two major challenges limit the reconstruction performance, i.e., the low photon counts accompanied by low signal-to-background ratio (SBR) and the multiple returns. In this paper, we propose a unified deep neural network that, for the first time, explicitly addresses these two challenges, and simultaneously recovers depth maps and intensity images from photon-efficient measurements. Starting from a general image formation model, our network is constituted of one encoder, where a non-local block is utilized to exploit the long-range correlations in both spatial and temporal dimensions of the raw measurement, and two decoders, which are designed to recover depth and intensity, respectively. Meanwhile, we investigate the statistics of the background noise photons and propose a noise prior block to further improve the reconstruction performance. The proposed network achieves decent reconstruction fidelity even under extremely low photon counts / SBR and heavy blur caused by the multiple-return effect, which significantly surpasses the existing methods. Moreover, our network trained on simulated data generalizes well to real-world imaging systems, which greatly extends the application scope of photon-efficient imaging in challenging scenarios with a strict limit on optical flux. Code is available at https://github.com/JiayongO-O/PENonLocal.

PMID:35994546 | DOI:10.1109/TPAMI.2022.3200745

IEEE Trans Cybern. 2022 Aug 22;PP. doi: 10.1109/TCYB.2022.3192112. Online ahead of print.

ABSTRACT

Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with Q -learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). Q -learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The ε -greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP.

PMID:35994539 | DOI:10.1109/TCYB.2022.3192112

IEEE Trans Image Process. 2022 Aug 22;PP. doi: 10.1109/TIP.2022.3197976. Online ahead of print.

ABSTRACT

Light field cameras can capture the radiance and direction of light rays by a single exposure, providing a new perspective to photography and 3D geometry perception. However, existing sub-aperture based light field cameras are limited by their sensor resolution to obtain high spatial and angular resolution images simultaneously. In this paper, we propose an inference-reconstruction variational autoencoder (IR-VAE) to reconstruct a dense light field image out of four corner reference views in a light field image. The proposed IR-VAE is comprised of one inference network and one reconstruction network, where the inference network infers novel views from existing reference views and viewpoint conditions, and the reconstruction network reconstructs novel views from a latent variable that contains the information of reference views, novel views, and viewpoints. The conditional latent variable in the inference network is regularized by the latent variable in the reconstruction network to facilitate information flow between the conditional latent variable and novel views. We also propose a statistic distance measurement dubbed the mean local maximum mean discrepancy (MLMMD) to enable the measurement of the statistic distance between two distributions with high-dimensional latent variables, which can capture richer information than their low-dimensional counterparts. Finally, we propose a viewpoint-dependent indirect view synthesis method to synthesize novel views more efficiently by leveraging adaptive convolution. Experimental results show that our proposed methods outperform state-of-the-art methods on different light field datasets.

PMID:35994531 | DOI:10.1109/TIP.2022.3197976

PLoS One. 2022 Aug 22;17(8):e0272783. doi: 10.1371/journal.pone.0272783. eCollection 2022.

ABSTRACT

We conducted a seroprevalence survey to estimate the true number of infections with SARS-CoV-2, the virus that causes COVID-19, in King County as of August 2020 by measuring the proportion of residents from who had antibodies against the virus. Participants from 727 households took part in a cross-sectional address-based household survey with random and non-random samples and provided dried blood spots that were tested for total antibody against the viral nucleocapsid protein, with confirmatory testing for immunoglobulin G against the spike protein. The data were weighted to match King County’s population based on sex, age group, income, race, and Hispanic status. After weighting and accounting for the accuracy of the tests, our best overall estimate of anti-SARS-CoV-2 seroprevalence in King County as of August 2020 is 3.9% (95% confidence interval (CI) 2.4%-6.0%) with an effective sample size of 589. Comparing seroprevalence with positive test reports, our survey suggests that viral testing underestimated incidence by a factor of about five and suggests that the proportion of cases that were serious (based on hospitalization) or fatal was 2.4% and 0.8%, respectively. Prevalence varied by subgroup; households reporting incomes at or below $100,000 in 2019 had nearly five times higher estimated antibody prevalence than those with incomes above $100,000. Those reporting non-White/non-Asian race had roughly seven times higher estimated antibody prevalence than those reporting White race. This survey was noteworthy for including people of all ages; among all age groups, the weighted estimate of prevalence was highest in older teens and young adults and lowest in young children, although these differences were not statistically significant.

PMID:35994500 | DOI:10.1371/journal.pone.0272783

PLoS One. 2022 Aug 22;17(8):e0273347. doi: 10.1371/journal.pone.0273347. eCollection 2022.

ABSTRACT

The elaborate ornamental plumage displayed by birds has largely been attributed to sexual selection, whereby the greater success of ornamented males in attaining mates drives a rapid elaboration of those ornaments. Indeed, plumage elaboration tends to be greatest in species with a high variance in reproductive success such as polygynous mating systems. Even among socially monogamous species, many males are extremely colourful. In their now-classic study, Møller and Birkhead (1994) suggested that increased variance in reproductive success afforded by extra-pair paternity should intensify sexual selection pressure and thus an elaboration of male plumage and sexual dichromatism, but the relatively few measures of extra-pair paternity at the time prevented a rigorous test of this hypothesis. In the nearly three decades since that paper’s publication, hundreds of studies have been published on rates of extra-pair paternity and more objective measures of plumage colouration have been developed, allowing for a large-scale comparative test of Møller and Birkhead’s (1994) hypothesis. Using an analysis of 186 socially monogamous passerine species with estimates of extra-pair paternity, our phylogenetically controlled analysis confirms Møller and Birkhead’s (1994) early work, demonstrating that rates of extra-pair paternity are positively associated with male, but not female, colouration and with the extent of sexual dichromatism. Plumage evolution is complex and multifaceted, driven by phylogenetic, ecological, and social factors, but our analysis confirms a key role of extra-pair mate choice in driving the evolution of ornamental traits.

PMID:35994495 | DOI:10.1371/journal.pone.0273347

PLoS One. 2022 Aug 22;17(8):e0272907. doi: 10.1371/journal.pone.0272907. eCollection 2022.

ABSTRACT

Mass media and social networks portray a unified image of the perfect male body. The intensity and universality of this influence is an important element of the process of Westernization, especially in traditional cultures such as that of Poland. The main aim of the present study was to investigate the differences between Polish gay and heterosexual men in terms of the role played by self-esteem and the level of internalization of sociocultural standards of body appearance as predictors of the development of their body images. The research study was conducted by reference to 19- to 29-year-old Polish heterosexual (n = 287) and gay (n = 97) men. The variables were measured using Polish versions of the Sociocultural Attitudes towards Appearance Scale-3, the Self-Esteem Scale, and the Multidimensional Body-Self Relations Questionnaire. Statistical analyses identified several variables as the main predictors of body image in both heterosexual and gay young men: self-esteem, information-seeking, perceived pressure and the internalization of sociocultural standards regarding an athletic body image drawn from mass media. The only significant difference between the two groups was the fact that self-esteem, perceived pressure and the internalization of sociocultural standards from mass media did not play a predictive role with respect to Appearance Orientation among the group of gay men.

PMID:35994493 | DOI:10.1371/journal.pone.0272907