Satellite orbit propagation involves predicting a satellite’s future position and velocity based on initial conditions. Traditional physical models, such as SGDP4, simplify the forces that act on the satellite to achieve high computational efficiency at the cost of reduced prediction accuracy, especially over longer time intervals where error accumulates. More sophisticated models like HPOP offer improved accuracy at the cost of high prediction times, rendering them unusable for realtime long-term predictions. Recent advancements have introduced machine learning techniques to refine these predictions and reduce errors. However, they often lack an analysis of model design choices, such as input feature selection and architectural configurations. Existing models do not incorporate features related to the state of celestial bodies, such as the positions of the Moon or Sun, which can influence the satellite’s trajectory. This paper proposes a novel model that integrates such features at both the initial time and throughout the prediction interval, leveraging their potential impact on the orbit of the satellite. The model is based on a neural network architecture employing GRU layers for encoding sequential data about the celestial conditions. Our results demonstrate that the inclusion of these sequential features significantly reduces prediction errors. Additionally, we have evaluated a variety of design choices such as independent sub-models for specific spatial coordinates and time intervals, further enhancing performance. These innovations lead to substantial improvements in both short- and long-term orbit predictions, providing a more robust and accurate alternative for satellite orbit propagation. • Hybrid model integrates SGDP4 with neural networks for improved orbit predictions. • Celestial body positions included as sequences are key features to enhance accuracy. • Separate models per coordinate and time window boost overall predictive performance. • Improved long-term forecast (10h+) while matching or exceeding short-term accuracy.

MOTIVATION: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation: MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/. Contact: [email protected] . Supplementary information: Supplementary data are available at Bioinformatics online.

This paper is devoted to state and prove a Digital Index Theorem for digital (n - 1)-manifolds in a digital space (Rn, f), where f belongs to a large family of lighting functions on the standard cubical decomposition Rn of the n-dimensional Euclidean space. As an immediate consequence we obtain the corresponding theorems for all (α, β)-surfaces of Kong–Roscoe, with α, β ∈ {6, 18, 26} and (α, β) ≠ (6, 6), (18, 26), (26, 26), as well as for the strong 26-surfaces of Bertrand–Malgouyres.