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Earth, Venus, Mars and some extrasolar terrestrial planets 1 have a mass and radius that is consistent with a mass fraction of about 30% metallic core and 70% silicate mantle 2 . At the inner frontier of the Solar System, Mercury has a completely different composition, with a mass fraction of about 70% metallic core and 30% silicate mantle 3 . Several formation or evolution scenarios are proposed to explain this metal-rich composition, such as a giant impact 4 , mantle evaporation 5 or the depletion of silicate at the inner edge of the protoplanetary disk 6 . These scenarios are still strongly debated. Here, we report the discovery of a multiple transiting planetary system (K2-229) in which the inner planet has a radius of 1.165 ± 0.066 Earth radii and a mass of 2.59 ± 0.43 Earth masses. This Earth-sized planet thus has a core-mass fraction that is compatible with that of Mercury, although it was expected to be similar to that of Earth based on host-star chemistry 7 . This larger Mercury analogue either formed with a very peculiar composition or has evolved, for example, by losing part of its mantle. Further characterization of Mercury-like exoplanets such as K2-229 b will help to put the detailed in situ observations of Mercury (with MESSENGER and BepiColombo 8 ) into the global context of the formation and evolution of solar and extrasolar terrestrial planets. The abundance of metals in Mercury’s interior is unique among the rocky planets of the Solar System. The characterization of the ‘super-Mercury’ exoplanet presented in this paper will improve our understanding of how Mercury-like planets can form and evolve.

Despite advances in recent years, there remains a lack of information on the habitat specificity of many aquatic insects, especially for the most recently described taxa and for very diverse regions such as the Amazon. This study evaluates the relationship between niche breadth and habitat specificity of aquatic insects in 219 streams. Local environmental variation in streams impacted by multiple land uses have resulted in distinct assemblages in each region. An Outlying Mean Index analysis was use to evaluate niche position and revealed that environmental changes could favor specialist insects. For example, streams impacted in one of the regions had higher values of depth, dissolved oxygen, percentage of roots, and percentage of wood in the substrate. These conditions contributed to the occurrence of the locally tolerant Hydrosmilodon and Zelusia genera, as well as the locally sensitive genera Harpagobaetis, Leptohyphes, and Traverhyphes. Results showed that some taxa from each regional assemblage occupying less often occupied habitats, and these taxa are represented by few individuals and with a low frequency of occurrence. The knowledge generated here is important for protecting habitats and maintaining the local biodiversity of aquatic systems in the Amazon impacted by multiple lands uses and land conversion activities.

We conducted an event-related potential (ERP) study to investigate the electrocortical dynamics of attentional feature-based processing in the Stroop matching task. Participants in the study (n = 37) compared the ink color of a colored word with the meaning of a color-word in white ink. The two task stimuli were presented simultaneously or with SOAs (Stimulus Onset Asynchrony) of 400 and 1,200 ms. The Stroop matching effect was maximal during SOA-0, was reduced at SOA-400, and was inverted at SOA-1200. We focused the ERP analysis on the N1 component. Paralleling the behavioral results, the N1 amplitude was greater for congruent stimuli than incongruent stimuli during SOA-0. This difference was attenuated at SOA-400, and at SOA-1200, an inverse pattern was observed. The results provide evidence that early selection processing participated in the Stroop matching task phenomenon and also suggest that the temporal modulation of early attention is a function of task characteristics such as SOA.

To understand the formation and composition of planetary systems it is important to study their host stars composition since both are formed in the same stellar nebula. In this work, we analyze the behaviour of chemical abundances of Cu, Zn, Sr, Y, Zr, Ba, Ce, Nd, and Eu in the large and homogeneous HARPS-GTO planet search sample (R ∼ 115000). This sample is composed of 120 stars hosting high-mass planets, 29 stars hosting exclusively Neptunians and Super-Earths and 910 stars without detected giant planets. We compare the [X/Fe] ratios of such elements in different metallicity bins and we find that planet hosts present higher abundances of Zn for [Fe/H] < −0.1 dex. On the other hand, Ba, Sr, Ce, and Zr abundances are underabundant in stars with planets, with a bigger difference for stars only hosting low-mass planets. However, most of the offsets found can be explained by differences in stellar parameters and by the fact that planet hosts at low metallicity mostly belong to the Galactic thick disk. Only in the case of Ba we find a statistically significant (3σ) underabundance of 0.03 dex for low-mass planet hosts. The origin of these elements is quite complex due to their evolution during the history of the Galaxy. Therefore, it is necessary to understand and characterize the stellar populations to which planet hosts belong in order to do a fair comparison with stars without detected planets. This work demonstrates that the effects of Galactic chemical evolution and not the presence of planets mostly account for the differences we find.

We introduce a new method to infer the posterior distribution for planet occurrence rates from radial-velocity (RV) observations. The approach combines posterior samples from the analysis of individual RV datasets of several stars, using importance sampling to reweight them appropriately. This eliminates the need for injection-recovery tests to compute detection limits and avoids the explicit definition of a detection threshold. We validate the method on simulated RV datasets and show that it yields unbiased estimates of the occurrence rate in different regions, with increasing precision as more stars are included in the analysis.

We compared the virulence profile and REP-PCR genotypes of Escherichia coli strains isolated from subclinical and clinical mastitis cases and dairy farm environments in Minas Gerais State, Brazil, to determine virulence factors and genotypes potentially associated with subclinical persistence in the udder. The virulence profile was obtained by the search for three virulence genes: lpfA (long polar fimbriae), fliC (flagella), and escN (type III secretion system). Subclinical isolates exhibited mainly the fliC gene (33.33%) and fliC + escN genes (30.30%). Clinical isolates exhibited mainly fliC + escN genes (50%) and environmental isolates the lpfA + escN genes (58.04%). Strains isolated from subclinical mastitis showed 6.75 times more positivity to fliC than environmental isolates. Thirty-four genotypes were observed in the REP-PCR analysis, and clinical mastitis isolates indicated more genetic proximity to dairy farm environment isolates than subclinical mastitis isolates. In conclusion, the results suggested that flagella may be an important virulence factor for mammary persistent E. coli infection in cattle, however, none of the E. coli REP-PCR genotypes were associated with subclinical infection.

The mountain bongo antelope Tragelaphus eurycerus isaaci has rapidly declined in recent decades, due to a combination of hunting, habitat degradation and disease. Endemic to Kenya, mountain bongo populations have shrunk to approximately 100 individuals now mainly confined to the Aberdares mountain ranges. Indirect observation of bongo signs (e.g. tracks, dung) can be misleading, thus methods to ensure reliable species identification, such as DNA-based techniques, are necessary to effectively study and monitor this species. We assessed bongo presence in four mountain habitats in Kenya (Mount Kenya National Park, Aberdare National Park, Eburu and Mau forests) and carried out a preliminary analysis of genetic variation by examining 466 bp of the first domain of the mtDNA control region using DNA extracted from faecal samples. Of the 201 dung samples collected in the field, 102 samples were molecularly identified as bongo, 97 as waterbuck, one as African buffalo and one as Aders’ duiker. Overall species-identification accuracy by experienced trackers was 64%, with very high error of commission when identifying bongo sign (37%), and high error of omission for waterbuck sign (82%), suggesting that the two species’ signs are easily confused. Despite high variation in the mtDNA control region in most antelope species, our results suggest low genetic variation in mountain bongo as only two haplotypes were detected in 102 samples analyzed. In contrast, the analysis of 63 waterbuck samples from the same sites revealed 21 haplotypes. Nevertheless, further examination using nuclear DNA markers (e.g. microsatellites) in a multi-locus approach is still required, especially because the use of mitochondrial DNA can result in population overestimation as distinct dung samples can potentially be originated from the same individual.

We apply the Bayesian framework to assess the presence of a correlation between two quantities. To do so, we estimate the probability distribution of the parameter of interest, ρ , characterizing the strength of the correlation. We provide an implementation of these ideas and concepts using python programming language and the pyMC module in a very short (∼ 130 lines of code, heavily commented) and user-friendly program. We used this tool to assess the presence and properties of the correlation between planetary surface gravity and stellar activity level as measured by the log( R HK ′ ) indicator. The results of the Bayesian analysis are qualitatively similar to those obtained via p-value analysis, and support the presence of a correlation in the data. The results are more robust in their derivation and more informative, revealing interesting features such as asymmetric posterior distributions or markedly different credible intervals, and allowing for a deeper exploration. We encourage the reader interested in this kind of problem to apply our code to his/her own scientific problems. The full understanding of what the Bayesian framework is can only be gained through the insight that comes by handling priors, assessing the convergence of Monte Carlo runs, and a multitude of other practical problems. We hope to contribute so that Bayesian analysis becomes a tool in the toolkit of researchers, and they understand by experience its advantages and limitations.

ASTRA is a Python package that provides a modular, instrument-independent interface for working with high-resolution stellar spectra. Designed to support data from multiple spectrographs, including ESPRESSO (Pepe et al., 2021), HARPS (Mayor et al., 2003; Pepe et al., 2002), MAROON-X (Seifahrt et al., 2022), and CARMENES (Quirrenbach et al., 2014). ASTRA offers a unified abstraction over their data formats, enabling consistent access to fluxes, wavelengths, uncertainties, and metadata across instruments. Furthermore, it applies the necessary wavelength and flux calibrations that are needed, as described by the official pipelines of each instrument.