Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

Dim light melatonin onset (DLMO) is the most reliable measure of human central circadian timing. Its modulation by light exposure and chronotype has been scarcely approached. We evaluated the impact of light changes on the interaction between melatonin, sleep, and chronotype in university students (n = 12) between the Antarctic summer (10 days) and the autumn equinox in Montevideo, Uruguay (10 days). Circadian preferences were tested by validated questionnaires. A Morningness–Eveningness Questionnaire average value (47 ± 8.01) was used to separate late and early participants. Daylight exposure (measured by actimetry) was significantly higher in Antarctica versus Montevideo in both sensitive time windows (the morning phase-advancing and the evening phase-delaying). Melatonin was measured in hourly saliva samples (18–24 h) collected in dim light conditions (<30 lx) during the last night of each study period. Early and late participants were exposed to similar amounts of light in both sites and time windows, but only early participants were significantly more exposed during the late evening in Antarctica. Late participants advanced their DLMO with no changes in sleep onset time in Antarctica, while early participants delayed their DLMO and sleep onset time. This different susceptibility to respond to light may be explained by a subtle difference in evening light exposure between chronotypes.

Background: South America has become the new epicenter of the COVID-19 pandemic with more than 1.1M reported cases and >50,000 deaths (June 2020). Conversely, Uruguay stands out as an outlier managing this health crisis with remarkable success. Methods: We developed a molecular diagnostic test to detect SARS-CoV-2. This methodology was transferred to research institutes, public hospitals and academic laboratories all around the country, creating a “ COVID-19 diagnostic lab network ”. Uruguay also implemented active epidemiological surveillance following the “Test, Trace and Isolate” (TETRIS) strategy coupled to real-time genomic epidemiology. Results: Three months after the first cases were detected, the number of positive individuals reached 826 (23 deaths, 112 active cases and 691 recovered). The Uruguayan strategy was based in a close synergy established between the national health authorities and the scientific community. In turn, academia rapidly responded to develop national RT-qPCR tests. Consequently, Uruguay was able to perform ∼1,000 molecular tests per day in a matter of weeks. The “ COVID-19 diagnostic lab network ” performed more than 54% of the molecular tests in the country. This, together with real- time genomics, were instrumental to implement the TETRIS strategy, helping to contain domestic transmission of the main outbreaks registered so far. Conclusions: Uruguay has successfully navigated the first trimester of the COVID-19 health crisis in South America. A rapid response by the scientific community to increase testing capacity, together with national health authorities seeking out the support from the academia were fundamental to successfully contain, until now, the COVID-19 outbreak in the country.

Zika virus (ZIKV) is an emerging pathogen member of the Flaviviridae family. ZIKV has spread rapidly in the Latin American region, causing hundreds of thousands of cases of ZIKV disease, as well as microcephaly in congenital infections. Detailed studies on the pattern of evolution of ZIKV strains have been extremely important to our understanding of viral survival, fitness, and evasion of the host’s immune system. For these reasons, we performed a comprehensive phylogenetic analysis of ZIKV strains recently isolated in the Americas. The results of these studies revealed evidence of diversification of ZIKV strains circulating in the Latin American region into at least five different genetic clusters. This diversification was also reflected in the different trends in dinucleotide bias and codon usage variation. Amino acid substitutions were found in E and prM proteins of the ZIKV strains isolated in this region, revealing the presence of novel genetic variants circulating in Latin America.

Eukaryotic genomes are compositionally heterogeneous, that is, composed by regions that differ in guanine–cytosine (GC) content (isochores). The most well documented case is that of vertebrates (mainly mammals) although it has been also noted among unicellular eukaryotes and invertebrates. In the human genome, regarded as a typical mammal, this heterogeneity is associated with several features. Specifically, genes located in GC-richest regions are the GC3-richest, display CpG islands and have shorter introns. Furthermore, these genes are more heavily expressed and tend to be located at the extremes of the chromosomes. Although the compositional heterogeneity seems to be widespread among eukaryotes, the associated properties noted in the human genome and other mammals have not been investigated in depth in other taxa. Here we provide evidence that the genome of the parasitic flatworm Schistosoma mansoni is compositionally heterogeneous and exhibits an isochore-like structure, displaying some features associated, until now, only with the human and other vertebrate genomes, with the exception of gene concentration.