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Context. Wind dynamics play a pivotal role in governing transport processes within planetary atmospheres, influencing atmospheric chemistry, cloud formation, and the overall energy budget. Understanding the strength and patterns of winds is crucial for comprehensive insights into the physics of ultra-hot-Jupiter atmospheres. Current research has proposed different mechanisms that limit wind speeds in these atmospheres. Aims. This study focuses on unraveling the wind dynamics and the chemical composition in the atmosphere of the ultra-hot Jupiter TOI-1518 b. Methods. Two transit observations using the high-resolution (R_λ∼ 85 000) optical (spectral coverage between 490 and 920 nm) spectrograph MAROON-X were obtained and analyzed to explore the chemical composition and wind dynamics using the cross-correlation techniques, global circulation models (GCMs), and atmospheric retrieval. Results. We report the detection of 14 species in the atmosphere of TOI-1518 b through cross-correlation analysis. VO was detected only with the new HyVO line list, whereas TiO was not detected. Additionally, we measured the time-varying cross-correlation trails for six different species, compared them with predictions from GCMs, and conclude that a strong drag is slowing the winds in TOI-1518 b’s atmosphere (τ_drag≈ 10³−10⁴s). We find that the trails are species dependent. Fe+ favors stronger drag than Fe, which we interpret as a sign of magnetic effects being responsible for the observed strong drag. Furthermore, we show that Ca+ probes layers above the Roche lobe, leading to a qualitatively different trail than the other species. Finally, We used a retrieval analysis to further characterize the abundances of the different species detected. Our analysis is refined thanks to the updated planetary mass of 1.83 ± 0.47 M_Jupwe derived from new Sophie radial-velocity observations. We measure an abundance of Fe of log₁₀Fe = −4.88_−0.76^+0.63corresponding to 0.07 to 1.62 solar enrichment. For the other elements, the retrievals appear to be biased, probably due to the different K_p/V_sysshifts between Fe and the other elements, which we demonstrate for the case of VO. 

Abstract Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG . 

Ultra-hot Jupiters are tidally locked with their host stars, dividing their atmospheres into a hot dayside and a colder nightside. As the planet moves through transit, different regions of the atmosphere rotate into view, revealing different chemical regimes. Highresolution spectrographs can observe asymmetries and velocity shifts and offer the possibility for time-resolved spectroscopy. The ultra-hot Jupiter WASP-189 b has recently been found to possess a rich transmission spectrum with evidence for atmospheric dynamics and chemical inhomogeneity. In this study, we search for other atoms and molecules in the planet’s transmission spectrum and investigate asymmetric signals. We analysed and combined eight transits of the ultra-hot Jupiter WASP-189 b collected with the HARPS, HARPS-N, ESPRESSO, and MAROON-X high-resolution spectrographs. Using the cross-correlation technique, we searched for neutral and ionised atoms as well as oxides, and we compared the obtained signals to model predictions. We report significant detections for H, Na, Mg, Ca, Ca⁺, Ti, Ti⁺, TiO, V, Cr, Mn, Fe, Fe⁺, Ni, Sr, Sr⁺, and Ba⁺. Of these, Sr, Sr⁺, and Ba⁺are detected for the first time in the transmission spectrum of WASP-189 b. In addition, we robustly confirm the detection of titanium oxide based on observations with HARPS and HARPS-N using the follow-up observations performed with MAROON-X and ESPRESSO. By fitting the orbital traces of the detected species by means of time-resolved spectroscopy using a Bayesian framework, we inferred posterior distributions for orbital parameters as well as line shapes. Our results indicate that different species must originate from different regions of the atmosphere to be able to explain the observed time dependence of the signals. Throughout the course of the transit, most signal strengths are expected to increase due to the larger atmospheric scale height at the hotter trailing terminator. For some species, however, we instead observed that the signals weaken, either due to the ionisation of atoms and their ions or the dissociation of molecules on the dayside. 

The two known planets in the planetary system of Teegarden’s Star are among the most Earth-like exoplanets currently known. Revisiting this nearby planetary system with two planets in the habitable zone aims at a more complete census of planets around very low-mass stars. A significant number of new radial velocity measurements from CARMENES, ESPRESSO, MAROON-X, and HPF, as well as photometry from TESS motivated a deeper search for additional planets. We confirm and refine the orbital parameters of the two know planets Teegarden’s Star b and c. We also report the detection of a third planet d with an orbital period of 26.13 ± 0.04 days and a minimum mass of 0.82 ± 0.17M_⊕. A signal at 96 days is attributed to the stellar rotation period. The interpretation of a signal at 172 days remains open. The TESS data exclude transiting short-period planets down to about half an Earth radius. We compare the planetary system architecture of very low-mass stars. In the currently known configuration, the planetary system of Teegarden’s star is dynamically quite different from that of TRAPPIST-1, which is more compact, but dynamically similar to others such as GJ 1002. 

Abstract Background The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Results Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the use of transfer learning. We estimate a cell-cycle embedding using a fixed reference dataset and project new data into this reference embedding, an approach that overcomes key limitations of learning a dataset-dependent embedding. Tricycle then predicts a cell-specific position in the cell cycle based on the data projection. The accuracy of tricycle compares favorably to gold-standard experimental assays, which generally require specialized measurements in specifically constructed in vitro systems. Using internal controls which are available for any dataset, we show that tricycle predictions generalize to datasets with multiple cell types, across tissues, species, and even sequencing assays. Conclusions Tricycle generalizes across datasets and is highly scalable and applicable to atlas-level single-cell RNA-seq data. 

We present the detection of three exoplanets orbiting the early M dwarf TOI-663 (TIC 54962195;V= 13.7 mag,J= 10.4 mag,R_★= 0.512 ± 0.015R_⊙,M_★= 0.514 ± 0.012M_⊙,d= 64 pc). TOI-663 b, c, and d, with respective radii of 2.27 ± 0.10R_⊕, 2.26 ± 0.10R_⊕, and 1.92 ± 0.13R_⊕and masses of 4.45 ± 0.65M_⊕, 3.65 ± 0.97M_⊕, and <5.2M_⊕at 99%, are located just above the radius valley that separates rocky and volatile-rich exoplanets. The planet candidates are identified in two TESS sectors and are validated with ground-based photometric follow-up, precise radial-velocity measurements, and high-resolution imaging. We used the software package juliet to jointly model the photometric and radial-velocity datasets, with Gaussian processes applied to correct for systematics. The three planets discovered in the TOI-663 system are low-mass mini-Neptunes with radii significantly larger than those of rocky analogs, implying that volatiles, such as water, must predominate. In addition to this internal structure analysis, we also performed a dynamical analysis that confirmed the stability of the system. The three exoplanets in the TOI-663 system, similarly to other sub-Neptunes orbiting M dwarfs, have been found to have lower densities than planets of similar sizes orbiting stars of different spectral types. 

Abstract Background DNA methylation dynamics in the brain are associated with normal development and neuropsychiatric disease and differ across functionally distinct brain regions. Previous studies of genome-wide methylation differences among human brain regions focus on limited numbers of individuals and one to two brain regions. Results Using GTEx samples, we generate a resource of DNA methylation in purified neuronal nuclei from 8 brain regions as well as lung and thyroid tissues from 12 to 23 donors. We identify differentially methylated regions between brain regions among neuronal nuclei in both CpG (181,146) and non-CpG (264,868) contexts, few of which were unique to a single pairwise comparison. This significantly expands the knowledge of differential methylation across the brain by 10-fold. In addition, we present the first differential methylation analysis among neuronal nuclei from basal ganglia tissues and identify unique CpG differentially methylated regions, many associated with ion transport. We also identify 81,130 regions of variably CpG methylated regions, i.e., variable methylation among individuals in the same brain region, which are enriched in regulatory regions and in CpG differentially methylated regions. Many variably methylated regions are unique to a specific brain region, with only 202 common across all brain regions, as well as lung and thyroid. Variably methylated regions identified in the amygdala, anterior cingulate cortex, and hippocampus are enriched for heritability of schizophrenia. Conclusions These data suggest that epigenetic variation in these particular human brain regions could be associated with the risk for this neuropsychiatric disorder. 

Extensive floodplains and numerous lakes in the Amazon basin are well suited to examine the role of floodable lands within the context of the sources and processing of carbon within inland waters. We measured diel, seasonal and inter-annual variations of carbon dioxide concentrations and related environmental variables in open water and flooded vegetation and estimated the extension of these habitats using remote sensing in a representative central Amazon floodplain lake, Lake Janauacá. Depth-averaged values of CO2 in the open water of the lake, 157± 91 µM (mean ± SD), were less than those in an embayment near aquatic vegetation, 285±116 µM, and were variable over 24-h periods at both sites. Within floating herbaceous plant mats, mean concentration (without one outlier) was 275±77 µM and in flooded forests mean concentration was 217±78 µM. Variability in CO2 concentrations in open water resulted from changes in the extent of inundation and exchange with vegetated habitats. The best statistical model, including CO2 in aquatic plant mats, Secchi depth, rate of change in water level and chlorophyll concentrations, explained around 90% of the variability in CO2 concentration. Three-dimensional hydrodynamic modeling demonstrated that diel differences in water temperature between plant mats and open water and basin-scale motions caused lateral exchanges of CO2 linking vegetated habitats to open water. Our findings extend understanding of CO2 in tropical lakes and floodplains with measurements and models that emphasize the importance of flooded forests and aquatic herbaceous plants fringing floodplain lakes as sources of carbon dioxide to the open waters.