Genomic Diversity in Two Related Plant Species with and without Sex Chromosomes - Silene latifolia and S. vulgaris

Genome size evolution is a complex process influenced by polyploidization, satellite DNA accumulation, and expansion of retroelements. How this process could be affected by different reproductive strategies is still poorly understood.We analyzed differences in the number and distribution of major repetitive DNA elements in two closely related species, Silene latifolia and S. vulgaris. Both species are diploid and possess the same chromosome number (2n = 24), but differ in their genome size and mode of reproduction. The dioecious S. latifolia (1C = 2.70 pg DNA) possesses sex chromosomes and its genome is 2.5× larger than that of the gynodioecious S. vulgaris (1C = 1.13 pg DNA), which does not possess sex chromosomes. We discovered that the genome of S. latifolia is larger mainly due to the expansion of Ogre retrotransposons. Surprisingly, the centromeric STAR-C and TR1 tandem repeats were found to be more abundant in S. vulgaris, the species with the smaller genome. We further examined the distribution of major repetitive sequences in related species in the Caryophyllaceae family. The results of FISH (fluorescence in situ hybridization) on mitotic chromosomes with the Retand element indicate that large rearrangements occurred during the evolution of the Caryophyllaceae family.Our data demonstrate that the evolution of genome size in the genus Silene is accompanied by the expansion of different repetitive elements with specific patterns in the dioecious species possessing the sex chromosomes

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

This article contains supporting information online at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113561119/-/DCSupplemental.Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Integrative Biolog

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evolution of sex determination and heterogamety changes in section Otites of the genus Silene

Abstract Switches in heterogamety are known to occur in both animals and plants. Although plant sex determination systems probably often evolved more recently than those in several well-studied animals, including mammals, and have had less time for switches to occur, we previously detected a switch in heterogamety in the plant genus Silene: section Otites has both female and male heterogamety, whereas S. latifolia and its close relatives, in a different section of the genus, Melandrium (subgenus Behenantha), all have male heterogamety. Here we analyse the evolution of sex chromosomes in section Otites, which is estimated to have evolved only about 0.55 MYA. Our study confirms female heterogamety in S. otites and newly reveals female heterogamety in S. borysthenica. Sequence analyses and genetic mapping show that the sex-linked regions of these two species are the same, but the region in S. colpophylla, a close relative with male heterogamety, is different. The sex chromosome pairs of S. colpophylla and S. otites each correspond to an autosome of the other species, and both differ from the XY pair in S. latifolia. Silene section Otites species are suitable for detailed studies of the events involved in such changes, and our phylogenetic analysis suggests a possible change from female to male heterogamety within this section. Our analyses suggest a possibility that has so far not been considered, change in heterogamety through hybridization, in which a male-determining chromosome from one species is introgressed into another one, and over-rides its previous sex-determining system

“Comparto Barriera-Secante: un possibile orizzonte sperimentale” in “Comune di Cesena: il piano strutturale"

Urbanistica Quaderni Archivio, Rom