Dynamic changes on the Wilkins Ice Shelf during the 2006–2009 retreat derived from satellite observations

The vast ice shelves around Antarctica provide significant restraint to the outflow from adjacent tributary glaciers. This important buttressing effect became apparent in the last decades, when outlet glaciers accelerated considerably after several ice shelves were lost around the Antarctic Peninsula (AP). The present study aims to assess dynamic changes on the Wilkins Ice Shelf (WIS) during different stages of ice-front retreat and partial collapse between early 2008 and 2009. The total ice-shelf area lost in these events was 2135 ± 75 km2 ( ∼  15 % of the ice-shelf area relative to 2007). Here, we use time series of synthetic aperture radar (SAR) satellite observations (1994–1996, 2006–2010) in order to derive variations in surface-flow speed from intensity-offset tracking. Spatial patterns of horizontal strain-rate, stress and stress-flow angle distributions are determined during different ice-front retreat stages. Prior to the final break up of an ice bridge in 2008, a strong speed up is observed, which is also discernible from other derived quantities. We identify areas that are important for buttressing and areas prone to fracturing using in-flow and first principal strain rates as well as principal stress components. Further propagation of fractures can be explained as the first principal components of strain rates and stresses exceed documented threshold values. Positive second principal stresses are another scale-free indicator for ice-shelf areas, where fractures preferentially open. Second principal strain rates are found to be insensitive to ice-front retreat or fracturing. Changes in stress-flow angles highlight similar areas as the in-flow strain rates but are difficult to interpret. Our study reveals the large potential of modern SAR satellite time series to better understand dynamic and structural changes during ice-shelf retreat but also points to uncertainties introduced by the methods applied

Curse and Blessing of Non-proteinogenic Parts in Computational Enzyme Engineering

Enzyme engineering aims to improve or install a new function in biocatalysts for applications ranging from chemical synthesis to biomedicine. For decades, computational techniques have been developed to predict the effect of protein changes and design new enzymes. However, these techniques may have been optimized to deal with proteins composed of the standard amino acid alphabet, while the function of many enzymes relies on non-proteogenic parts like cofactors, nucleic acids, and post-translational modifications. Enzyme systems containing such molecules might be handled or modeled improperly by computational tools, and thus be unsuitable, or require additional tweaking, parameterization, or preparation. In this review, we give an overview of common and recent tools and workflows available to computational enzyme engineers. We highlight the various pitfalls that come with including non-proteogenic compounds in computations and outline potential ways to address common issues. Finally, we showcase successful examples from literature that computationally engineered such enzymes.</p

Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%

Uncertainty Assessment of Ice Discharge Using GPR-Derived Ice Thickness from Gourdon Glacier, Antarctic Peninsula

Ice cliffs within a glacier represent a challenge for the continuity equations used in many glacier models by interrupting the validity of input parameters. In the case of Gourdon Glacier on James Ross Island, Antarctica, a ∼300–500 m high, almost vertical cliff, separates the outlet glacier from its main accumulation area on the plateau of the island. In 2017 and 2018 we conducted ice thickness measurements during two airborne ground penetrating radar campaigns in order to evaluate differences to older measurements from the 1990s. The observed differences are mostly smaller than the estimated error bars. In comparison to the in situ data, the published “consensus ice thickness estimate” strongly overestimates the ice thickness at the outlet. We analyse three different interpolation and ice thickness reconstruction methods. One approach additionally includes the mass input from the plateau. Differences between the interpolation methods have a minor impact on the ice discharge estimation if the used flux gates are in areas with a good coverage of in situ measurements. A much stronger influence was observed by uncertainties in the glacier velocities derived from remote sensing, especially in the direction of the velocity vector in proximity to the ice cliff. We conclude that the amount of in situ measurements should be increased for specific glacier types in order to detect biases in modeled ice thickness and ice discharge estimations

The importance of the gut microbiome and its signals for a healthy nervous system and the multifaceted mechanisms of neuropsychiatric disorders

Increasing evidence links the gut microbiome and the nervous system in health and disease. This narrative review discusses current views on the interaction between the gut microbiota, the intestinal epithelium, and the brain, and provides an overview of the communication routes and signals of the bidirectional interactions between gut microbiota and the brain, including circulatory, immunological, neuroanatomical, and neuroendocrine pathways. Similarities and differences in healthy gut microbiota in humans and mice exist that are relevant for the translational gap between non-human model systems and patients. There is an increasing spectrum of metabolites and neurotransmitters that are released and/or modulated by the gut microbiota in both homeostatic and pathological conditions. Dysbiotic disruptions occur as consequences of critical illnesses such as cancer, cardiovascular and chronic kidney disease but also neurological, mental, and pain disorders, as well as ischemic and traumatic brain injury. Changes in the gut microbiota (dysbiosis) and a concomitant imbalance in the release of mediators may be cause or consequence of diseases of the central nervous system and are increasingly emerging as critical links to the disruption of healthy physiological function, alterations in nutrition intake, exposure to hypoxic conditions and others, observed in brain disorders. Despite the generally accepted importance of the gut microbiome, the bidirectional communication routes between brain and gut are not fully understood. Elucidating these routes and signaling pathways in more detail offers novel mechanistic insight into the pathophysiology and multifaceted aspects of brain disorders

Acceleration of dynamic ice loss in Antarctica from satellite gravimetry

The dynamic stability of the Antarctic Ice Sheet is one of the largest uncertainties in projections of future global sea-level rise. Essential for improving projections of the ice sheet evolution is the understanding of the ongoing trends and accelerations of mass loss in the context of ice dynamics. Here, we examine accelerations of mass change of the Antarctic Ice Sheet from 2002 to 2020 using data from the GRACE (Gravity Recovery and Climate Experiment; 2002–2017) and its follow-on GRACE-FO (2018-present) satellite missions. By subtracting estimates of net snow accumulation provided by re-analysis data and regional climate models from GRACE/GRACE-FO mass changes, we isolate variations in ice-dynamic discharge and compare them to direct measurements based on the remote sensing of the surface-ice velocity (2002–2017). We show that variations in the GRACE/GRACE-FO time series are modulated by variations in regional snow accumulation caused by large-scale atmospheric circulation. We show for the first time that, after removal of these surface effects, accelerations of ice-dynamic discharge from GRACE/GRACE-FO agree well with those independently derived from surface-ice velocities. For 2002–2020, we recover a discharge acceleration of -5.3 ± 2.2 Gt yr−2 for the entire ice sheet; these increasing losses originate mainly in the Amundsen and Bellingshausen Sea Embayment regions (68%), with additional significant contributions from Dronning Maud Land (18%) and the Filchner-Ronne Ice Shelf region (13%). Under the assumption that the recovered rates and accelerations of mass loss persisted independent of any external forcing, Antarctica would contribute 7.6 ± 2.9 cm to global mean sea-level rise by the year 2100, more than two times the amount of 2.9 ± 0.6 cm obtained by linear extrapolation of current GRACE/GRACE-FO mass loss trends

Linkage analysis of alcohol dependence using MOD scores

Alcohol dependence is a typical example of a complex trait that is governed by several genes and for which the mode of inheritance is unknown. We analyzed the microsatellite markers and the Affymetrix single-nucleotide polymorphisms (SNPs) for a subset of the Collaborative Study on the Genetics of Alcoholism family sample, 93 pedigrees of Caucasian ancestry comprising 919 persons, 390 of whom are affected according to DSM III-R and Feighner criteria. In particular, we performed parametric single-marker linkage analysis using MLINK of the LINKAGE package (for the microsatellite data), as well as multipoint MOD-score analysis with GENEHUNTER-MODSCORE (for the microsatellite and SNP data). By use of two liability classes, different penetrances were assigned to males and females. In order to investigate parent-of-origin effects, we calculated MOD scores under trait models with and without imprinting. In addition, for the microsatellite data, the MOD-score analysis was performed with sex-averaged as well as sex-specific maps. The highest linkage peaks were obtained on chromosomes 1, 2, 7, 10, 12, 13, 15, and 21. There was evidence for paternal imprinting at the loci on chromosomes 2, 10, 12, 13, 15, and 21. A tendency to maternal imprinting was observed at two loci on chromosome 7. Our findings underscore the fact that an adequate modeling of the genotype-phenotype relation is crucial for the genetic mapping of a complex trait

A consensus estimate for the ice thickness distribution of all glaciers on Earth

Knowledge of the ice thickness distribution of the world’s glaciers is a fundamental prerequisite for a range of studies. Projections of future glacier change, estimates of the available freshwater resources or assessments of potential sea-level rise all need glacier ice thickness to be accurately constrained. Previous estimates of global glacier volumes are mostly based on scaling relations between glacier area and volume, and only one study provides global-scale information on the ice thickness distribution of individual glaciers. Here we use an ensemble of up to five models to provide a consensus estimate for the ice thickness distribution of all the about 215,000 glaciers outside the Greenland and Antarctic ice sheets. The models use principles of ice flow dynamics to invert for ice thickness from surface characteristics. We find a total volume of 158 ± 41 × 103 km3, which is equivalent to 0.32 ± 0.08 m of sea-level change when the fraction of ice located below present-day sea level (roughly 15%) is subtracted. Our results indicate that High Mountain Asia hosts about 27% less glacier ice than previously suggested, and imply that the timing by which the region is expected to lose half of its present-day glacier area has to be moved forward by about one decade