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Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Plain Language Summary: Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost‐a type of ground that remains at or below 0°C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze‐thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Key Points: Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrainsMachine learning models predict full‐coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm)Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions [ABSTRACT FROM AUTHOR]

We report on the development of direct diode laser source with high-power and high reliability. The laser source was realized by the polarization and wavelength combination of four diode laser stacks. When at the operating current of 122 A, the source was capable of producing 10,120 W output while maintaining 46% electro-optical conversion efficiency. The maximum temperature on the lens was decreased from 442.2 K to 320 K by utilizing an efficient thermal dissipation structure, and the corresponding maximum von Mises stress was reduced from 75.4 MPa to 14 MPa. In addition, a reliability test demonstrated that our laser source was reliable and potential in the applications of laser cladding and heat treatment. [ABSTRACT FROM AUTHOR]

Throughout the natural aging process from new to aged white tea, the flavor evolves into a 'stale flavor', despite the initial umami diminishes. The flowering process, inoculation of Eurotium cristatum to white tea, improves the flavor. The impact on sensory qualities and underlying chemical basis of flowering in aged white tea warrant investigation. Sensory analysis, non-targeted metabolomics and volatilomics together deciphered flavor modifications of flowering in aged white tea from different aging years (FAWTs). Findings indicate the flowering process can recover the umami of aged white tea, enhancing the 'stale flavor'. These changes primarily stem from oxidations of catechins and free amino acids, enrichments of flavonols and soluble sugars, and 16 pivotal aroma compounds from degradations of lipids and glycosides. Additionally, 15 volatile and 39 non-volatile compounds were identified as potential biomarkers for FAWTs. These findings offer a viable strategy to improving the quality of aged white tea., Competing Interests: Declaration of competing interest We declare that we have no commercial or financial interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R 2 >0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R 2 >0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)