https://www.selleckchem.com/products/sr18662.html Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003-2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses.Extensive surgical spinopelvic fusion for patients with adult spinal deformity (ASD) to achieve optimal radiological parameters should be avoided. The aim of this study was to review clinical and imaging findings in patients with ASD with postural and radiological abnormalities who underwent a novel three-level limited lumbar fusion as two-stage surgery in an attempt to propose a better tolerated alternative to spinopelvic long fusion to the pelvis. The subjects were 26 patients with a minimum follow-up period of 2 years. Cobb angle, C7 sagittal vertical axis, and pelvic incidence (PI) minus lumbar lordosis (LL) were significantly improved after surgery and maintained at follow-up. Most radiological parameters were corrected with lateral interbody fusio