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Boreal fire regimes are intensifying because of climate change, and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean=127.3 cm, standard deviation (SD) = 27.7 cm) than in unburned (98.1 cm, SD=26.9 cm) landscapes 1 year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained upland, in open and mature larch forest often intermixed with Scots pine, and in high-severity burns. The environmental drivers basal area, vegetation density, and burn depth explained 73.3 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced normalized burn ratio (dNBR), and the pre-fire normalized difference vegetation index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Moreover, land surface temperature (LST) displayed particularly strong correlations with post-fire thaw depth (r=0.65, p). Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar. Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate post-fire thaw depth at a landscape scale.

The Arctic region has undergone warming at a rate more than 3 times higher than the global average. This warming has led to the degradation of near-surface permafrost, resulting in decreased ground stability. This instability not only poses a primary hazard to Arctic infrastructure and human-impacted areas but can also lead to secondary ecological hazards from infrastructure failure associated with hazardous materials. This development underscores the need for a comprehensive inventory of critical infrastructure and human-impacted areas. The inventory should be linked to environmental data to assess their susceptibility to permafrost degradation as well as the ecological consequences that may arise from infrastructure failure. Here, we provide such an inventory for Alaska, a vast state covering approximately 1.7 × 106 km2, with a population of over 733 000 people and a history of industrial development on permafrost. Our Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS) integrates data from (i) the Sentinel-1/2-derived Arctic Coastal Human Impact dataset (SACHI); (ii) OpenStreetMap (OSM); (iii) the pan-Arctic Catchment Database (ARCADE); (iv) a dataset of permafrost extent, probability and mean annual ground temperatures; and (v) the Contaminated Sites Database and reports to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The integration process included harmonizing spatial references, extents and geometries across all the datasets as well as incorporating a uniform usage type classification scheme for the infrastructure data. Additionally, we employed text-mining techniques to generate complementary geospatial data from textual reports on contaminated sites, including details on contaminants, cleanup duration and the affected media. The combination of SACHI and OSM enhanced the detail of the usage type classification for infrastructure from 5 to 13 categories, allowing the identification of elements critical to Arctic communities beyond industrial sites. Further, the new inventory integrates the high spatial detail of OSM with the unbiased infrastructure detection capability of SACHI, accurately representing 94 % of the polygonal infrastructure and 78 % of the linear infrastructure, respectively. The SIRIUS dataset is presented as a GeoPackage, enabling spatial analysis and queries of its components, either as a function of or in combination with one another. The dataset is available on Zenodo at https://doi.org/10.5281/zenodo.8311243 (Kaiser et al., 2023).

Tropical reefs encompass tremendous biodiversity yet are imperiled by increasing natural and anthropogenic disturbances worldwide. Meiobenthic biota on coral reefs, for example, ostracods, may experience substantial diversity loss and compositional changes even before being examined. In this study, we investigated the reefal ostracod assemblages from the highly diverse and productive ecosystem in the Zanzibar Archipelago (Pemba, Zanzibar, and Mafia islands), Tanzania, to understand how their diversity and faunal structure vary in response to water depth, benthic community type, and human impacts. We characterized four distinct ostracod faunas associated with different benthic habitats, which were deep fore reefs, shallow fringing reefs, degraded fringing reefs, and algae-covered intertidal flats. We identified typical ostracod associations, i.e., Bairdiidae versus Loxoconchidae–Xestoleberididae, that showed affinities towards hard corals or algae on the reef platforms, respectively. The highest diversity was found on shallow fringing reefs where coral-affined and algae-affined taxa exhibited maximum overlap of their distributional ranges, while the sand flats, mangrove, and marginal reefs within the intertidal zone had much lower diversity with a high dominance of euryhaline taxa. Along the western coast of Zanzibar Island, coastal development likely resulted in a unique faunal composition and comparatively low diversity of ostracod assemblages among those in reefal habitats, in conjunction with overall reef ecosystem degradation. This study represents the first large-scale assessment of shallow-marine ostracods in the Zanzibar Archipelago. It lays a solid foundation for future research into the ecological significance of ostracods on coral reefs.

Abstract Landscape drying associated with permafrost thaw is expected to enhance microbial methane oxidation in arctic soils. Here we show that ice-rich, Yedoma permafrost deposits, comprising a disproportionately large fraction of pan-arctic soil carbon, present an alternate trajectory. Field and laboratory observations indicate that talik (perennially thawed soils in permafrost) development in unsaturated Yedoma uplands leads to unexpectedly large methane emissions (35–78 mg m−2 d−1 summer, 150–180 mg m−2 d−1 winter). Upland Yedoma talik emissions were nearly three times higher annually than northern-wetland emissions on an areal basis. Approximately 70% emissions occurred in winter, when surface-soil freezing abated methanotrophy, enhancing methane escape from the talik. Remote sensing and numerical modeling indicate the potential for widespread upland talik formation across the pan-arctic Yedoma domain during the 21st and 22nd centuries. Contrary to current climate model predictions, these findings imply a positive and much larger permafrost-methane-climate feedback for upland Yedoma.

Abstract Background Cognitive assessment is a required component of the Medicare Annual Wellness Visit (AWV). In this prospective study, we evaluated acceptability and usefulness of a patient-reported outcome measure (the PROMIS® Cognitive Function Screener, or PRO-CS) to screen for cognitive impairment during the AWV. We compared two versions of the PRO-CS: Abilities and Concerns. Methods We developed PRO-CS Abilities and PRO-CS Concerns using items from the PROMIS Cognitive Function item banks. We partnered with a large health system in Pennsylvania to implement an electronic health record (EHR)-integrated version of the 4-item PRO-CS into their AWV workflow. PRO-CS Abilities was implemented in June 2022 and then replaced with PRO-CS Concerns in October 2022. We used EHR data to evaluate scores on Abilities versus Concerns and their association with patient characteristics. We gathered feedback from providers on experiences with the PRO-CS and conducted cognitive interviews with patients to evaluate their preferences for Abilities versus Concerns. Results Between June 2022 and January 2023, 3,088 patients completed PRO-CS Abilities and 2,614 patients completed PRO-CS Concerns. Mean T-scores for Abilities (54.8) were slightly higher (indicating better cognition) than for Concerns (52.6). 10% of scores on Abilities and 13% of scores on Concerns indicated concern for cognitive impairment (T-score

Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three “hotspot” regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3 m>0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling.

Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.

The ground ice content in cold environments influences the permafrost thermal regime and the thaw trajectories in a warming climate, especially for soils containing excess ice. Despite their importance, the amount and distribution of ground ice are often unknown due to lacking field observations. Hence, modeling the thawing of ice-rich permafrost soils and associated thermokarst is challenging as ground ice content has to be prescribed in the model setup. In this study, we present a model scheme, capable of simulating segregated ice formation during a model spinup together with associated ground heave. It provides the option to add a constant sedimentation rate throughout the simulation. Besides ice segregation, it can represent thaw consolidation processes and ground subsidence under a warming climate. The computation is based on soil mechanical processes, soil hydrology by the Richards equation and soil freezing characteristics. The code is implemented in the CryoGrid community model (version 1.0), a modular land surface model for simulations of the ground thermal regime. The simulation of ice segregation and thaw consolidation with the new model scheme allows us to analyze the evolution of ground ice content in both space and time. To do so, we use climate data from two contrasting permafrost sites to run the simulations. Several influencing factors are identified, which control the formation and thaw of segregated ice. (i) Model results show that high temperature gradients in the soil as well as moist conditions support the formation of segregated ice. (ii) We find that ice segregation increases in fine-grained soils and that especially organic-rich sediments enhance the process. (iii) Applying external loads suppresses ice segregation and speeds up thaw consolidation. (iv) Sedimentation leads to a rise of the ground surface and the formation of an ice-enriched layer whose thickness increases with sedimentation time. We conclude that the new model scheme is a step forward to improve the description of ground ice distributions in permafrost models and can contribute towards the understanding of ice segregation and thaw consolidation in permafrost environments under changing climatic conditions.

Abstract Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new therapies, however, few mouse models represent the complexity of TNBC. Here, we develop a female TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl model develops heterogeneous triple-negative mammary tumors that display histological and molecular features commonly found in human TNBC. Our research involves deep molecular and spatial analyses on Myc;Ptenfl tumors including bulk and single-cell RNA-sequencing, and multiplex tissue-imaging. Through comparison with human TNBC, we demonstrate that this genetic mouse model develops mammary tumors with differential survival and therapeutic responses that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC, providing a pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response.

Long-term measurements of permafrost temperatures do not provide a complete picture of the Arctic subsurface thermal regime. Regions with warmer permafrost often show little to no long-term change in ground temperature due to the uptake and release of latent heat during freezing and thawing. Thus, regions where the least warming is observed may also be the most vulnerable to permafrost degradation. Since direct measurements of ice and liquid water contents in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we first analyze trends in observed air and permafrost temperatures at four sites within the continuous permafrost zone, where we find substantial variation in the apparent relationship between long-term changes in permafrost temperatures (0.02–0.16 K yr−1) and air temperature (0.09–0.11 K yr−1). We then apply recently developed Bayesian inversion methods to link observed changes in borehole temperatures to unobserved changes in latent heat and active layer thickness using a transient model of heat conduction with phase change. Our results suggest that the degree to which recent warming trends correlate with permafrost thaw depends strongly on both soil freezing characteristics and historical climatology. At the warmest site, a 9 m borehole near Ny-Ålesund, Svalbard, modeled active layer thickness increases by an average of 13 ± 1 cm K−1 rise in mean annual ground temperature. In stark contrast, modeled rates of thaw at one of the colder sites, a borehole on Samoylov Island in the Lena River delta, appear far less sensitive to temperature change, with a negligible effect of 1 ± 1 cm K−1. Although our study is limited to just four sites, the results urge caution in the interpretation and comparison of warming trends in Arctic boreholes, indicating significant uncertainty in their implications for the current and future thermal state of permafrost.

The Arctic is warming at an above-average rate, and small, shallow waterbodies such as ponds are vulnerable to this warming due to their low thermal inertia compared to larger lakes. While ponds are a relevant landscape-scale source of methane under the current climate, the response of pond methane emissions to warming is uncertain. We employ a new, process-based model for methane emissions from ponds (MeEP) to investigate the methane emission response of polygonal-tundra ponds in northeastern Siberia to warming. MeEP is the first dedicated model of pond methane emissions which differentiates between the three main pond types of the polygonal-tundra, ice-wedge, polygonal-center, and merged polygonal ponds and resolves the three main pathways of methane emissions – diffusion, ebullition, and plant-mediated transport. We perform idealized warming experiments, with increases in the mean annual temperature of 2.5, 5, and 7.5 ∘C on top of a historical simulation. The simulations reveal an approximately linear increase in emissions from ponds of 1.33 g CH4 yr−1 ∘C−1 m−2 in this temperature range. Under annual temperatures 5 ∘C above present temperatures, pond methane emissions are more than 3 times higher than now. Most of this emission increase is due to the additional substrate provided by the increased net productivity of the vascular plants. Furthermore, plant-mediated transport is the dominating pathway of methane emissions in all simulations. We conclude that vascular plants as a substrate source and efficient methane pathway should be included in future pan-Arctic assessments of pond methane emissions.

Abstract Subsea permafrost carbon pools below the Arctic shelf seas are a major unknown in the global carbon cycle. We combine a numerical model of sedimentation and permafrost evolution with simplified carbon turnover to estimate accumulation and microbial decomposition of organic matter on the pan-Arctic shelf over the past four glacial cycles. We find that Arctic shelf permafrost is a globally important long-term carbon sink storing 2822 (1518–4982) Pg OC, double the amount stored in lowland permafrost. Although currently thawing, prior microbial decomposition and organic matter aging limit decomposition rates to less than 48 Tg OC/yr (25–85) constraining emissions due to thaw and suggesting that the large permafrost shelf carbon pool is largely insensitive to thaw. We identify an urgent need to reduce uncertainty in rates of microbial decomposition of organic matter in cold and saline subaquatic environments. Large emissions of methane more likely derive from older and deeper sources than from organic matter in thawing permafrost.

Heat storage within the Earth system is a fundamental metric for understanding climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage, combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.8 ± 2.0 × 1021 J during the period 1960–2020, but the distribution of heat among the three components is not homogeneous. The sensible diffusion of heat through the ground accounts for ∼90 % of the continental heat storage, with inland water bodies and permafrost degradation (i.e. latent heat) accounting for ∼0.7 % and ∼9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the solid ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory.

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice–water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also call for urgently needed actions for enabling continuity, archiving, rescuing, and calibrating efforts to assure improved and long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.

Arctic river deltas define the interface between the terrestrial Arctic and the Arctic Ocean. They are the site of sediment, nutrient, and soil organic carbon discharge to the Arctic Ocean. Arctic deltas are unique globally because they are underlain by permafrost and acted on by river and sea ice, and many are surrounded by a broad shallow ramp. Such ramps may buffer the delta from waves, but as the climate warms and permafrost thaws, the evolution of Arctic deltas will likely take a different course, with implications for both the local scale and the wider Arctic Ocean. One important way to understand and predict the evolution of Arctic deltas is through numerical models. Here we present ArcDelRCM.jl, an improved reduced-complexity model (RCM) of arctic delta evolution based on the DeltaRCM-Arctic model (Lauzon et al., 2019), which we have reconstructed in Julia language using published information. Unlike previous models, ArcDelRCM.jl is able to replicate the ramp around the delta. We have found that the delayed breakup of the so-called “bottom-fast ice” (i.e. ice that is in direct contact with the bed of the channel or the sea, also known as “bed-fast ice”) on and around the deltas is ultimately responsible for the appearance of the ramp feature in our models. However, changes made to the modelling of permafrost erosion and the protective effects of bottom-fast ice are also important contributors. Graph analyses of the delta network performed on ensemble runs show that deltas produced by ArcDelRCM.jl have more interconnected channels and contain less abandoned subnetworks. This may suggest a more even feeding of sediments to all sections of the delta shoreline, supporting ramp growth. Moreover, we showed that the morphodynamic processes during the summer months remain active enough to contribute significant sediment input to the growth and evolution of Arctic deltas and thus should not be neglected in simulations gauging the multi-year evolution of delta features. Finally, we tested a strong climate-warming scenario on the simulated deltas of ArcDelRCM.jl, with temperature, discharge, and ice conditions consistent with RCP7–8.5. We found that the ramp features degrade on the timescale of centuries and effectively disappear in under 1 millennium. Ocean processes, which are not included in these models, may further shorten the timescale. With the degradation of the ramps, any dissipative effects on wave energy they offered would also decrease. This could expose the sub-aerial parts of the deltas to increased coastal erosion, thus impacting permafrost degradation, nutrients, and carbon releases.

Background. A portion of the Functional Assessment of Cancer Therapy-Lung (FACT-L) instrument contributed to a previously published utility index, the FACT Lung Utility Index or FACT-LUI. Six FACT items representing lung cancer quality of life covered fatigue, pain, dyspnea, cough, anxiety, and depression. Two FACT items had been previously combined by the index authors into one for nausea and/or appetite loss, resulting in 7 final domains. Methods. The objective was to perform measurement invariance testing within a confirmatory factor analysis (CFA) framework to support the feasibility of using the FACT-LUI for non–preference-based psychometric applications. The original index patients comprised group 1, and similar FACT patient data ( n = 249) from another published study comprised group 2. One 2-factor model and two 1-factor CFA models were evaluated to assess measurement invariance across groups, using varying degrees of item parceling and a small number of residual covariances, all justified by the literature. Results. The 1-factor models were most optimal. A 1-factor model with 1 pair of items parceled showed invariance to the partial scalar level using usual fit criteria across groups, requiring 2 unconstrained intercepts. A 1-factor model with 3 pairs of justified parcels showed full configural, metric, and scalar invariance across groups. Conclusions. The FACT-LUI items fit a partially to fully invariant 1-factor model, suggesting feasibility for non–preference-based applications. Implications. Results suggest useful incorporation of the FACT-LUI into clinical trials with no substantial increased respondent burden, allowing preference-based and other psychometric applications from the same index items. Highlights This work suggests that in addition to being originally designed for use as a utility index, the 7 FACT-LUI items together also fit simple CFA and measurement invariance models. This less expected result indicates that these items as a group are also potentially useful in non–preference-based applications. Clinical trials can make for challenging decisions concerning which patient-reported outcome measures to include without being burdensome. However, the literature suggests a need for improved reporting of quality of life in lung cancer in particular as well as cancer in general. Inclusion of more disease-specific items such as the FACT-LUI may allow for information gathering of both preference-based and non–preference-based data with less demand on patients, similar to what has been done with some generic instruments.

Pancreatic cancer remains a formidable challenge due to limited treatment options and its aggressive nature. In recent years, the naturally occurring anticancer compound juglone has emerged as a potential therapeutic candidate, showing promising results in inhibiting tumor growth and inducing cancer cell apoptosis. However, concerns over its toxicity have hampered juglone’s clinical application. To address this issue, we have explored the use of polymeric micelles as a delivery system for juglone in pancreatic cancer treatment. These micelles, formulated using Poloxamer 407 and D-α-Tocopherol polyethylene glycol 1000 succinate, offer an innovative solution to enhance juglone’s therapeutic potential while minimizing toxicity. In-vitro studies have demonstrated that micelle-formulated juglone (JM) effectively decreases proliferation and migration and increases apoptosis in pancreatic cancer cell lines. Importantly, in-vivo, JM exhibited no toxicity, allowing for increased dosing frequency compared to free drug administration. In mice, JM significantly reduced tumor growth in subcutaneous xenograft and orthotopic pancreatic cancer models. Beyond its direct antitumor effects, JM treatment also influenced the tumor microenvironment. In immunocompetent mice, JM increased immune cell infiltration and decreased stromal deposition and activation markers, suggesting an immunomodulatory role. To understand JM’s mechanism of action, we conducted RNA sequencing and subsequent differential expression analysis on tumors that were treated with JM. The administration of JM treatment reduced the expression levels of the oncogenic protein MYC, thereby emphasizing its potential as a focused, therapeutic intervention. In conclusion, the polymeric micelles-mediated delivery of juglone holds excellent promise in pancreatic cancer therapy. This approach offers improved drug delivery, reduced toxicity, and enhanced therapeutic efficacy.

Boreal forests of Siberia play a relevant role in the global carbon cycle. However, global warming threatens the existence of summergreen larch-dominated ecosystems, likely enabling a transition to evergreen tree taxa with deeper active layers. Complex permafrost–vegetation interactions make it uncertain whether these ecosystems could develop into a carbon source rather than continuing atmospheric carbon sequestration under global warming. Consequently, shedding light on the role of current and future active layer dynamics and the feedbacks with the apparent tree species is crucial to predict boreal forest transition dynamics and thus for aboveground forest biomass and carbon stock developments. Hence, we established a coupled model version amalgamating a one-dimensional permafrost multilayer forest land-surface model (CryoGrid) with LAVESI, an individual-based and spatially explicit forest model for larch species (Larix Mill.), extended for this study by including other relevant Siberian forest species and explicit terrain. Following parameterization, we ran simulations with the coupled version to the near future to 2030 with a mild climate-warming scenario. We focus on three regions covering a gradient of summergreen forests in the east at Spasskaya Pad, mixed summergreen–evergreen forests close to Nyurba, and the warmest area at Lake Khamra in the southeast of Yakutia, Russia. Coupled simulations were run with the newly implemented boreal forest species and compared to runs allowing only one species at a time, as well as to simulations using just LAVESI. Results reveal that the coupled version corrects for overestimation of active layer thickness (ALT) and soil moisture, and large differences in established forests are simulated. We conclude that the coupled version can simulate the complex environment of eastern Siberia by reproducing vegetation patterns, making it an excellent tool to disentangle processes driving boreal forest dynamics.

The study of phase formation in the U-Te-O systems with mono and divalent cations under high-temperature high-pressure (HT/HP) conditions has resulted in four new inorganic compounds: K2 [(UO2) (Te2O7)], Mg [(UO2) (TeO3)2], Sr [(UO2) (TeO3)2] and Sr [(UO2) (TeO5)]. Tellurium occurs as TeIV, TeV, and TeVI in these phases which demonstrate the high chemical flexibility of the system. Uranium VI) adopts a variety of coordinations, namely, UO6 in K2 [(UO2) (Te2O7), UO7 in Mg [(UO2) (TeO3)2] and Sr [(UO2) (TeO3)2], and UO8 in Sr [(UO2) (TeO5)]. The structure of K2 [(UO2) (Te2O7)] is featured with one dimensional (1D) [Te2O7]4- chains along the c-axis. The Te2O7 chains are further linked by UO6 polyhedra, forming the 3D [(UO2) (Te2O7)]2- anionic frameworks. In Mg [(UO2) (TeO3)2], TeO4 disphenoids share common corners with each other resulting in infinite 1D chains of [(TeO3)2]4- propagating along the a-axis. These chains link the uranyl bipyramids by edge sharing along two edges of the disphenoids, resulting in the 2D layered structure of [(UO2) (Te2O6)]2-. The structure of Sr [(UO2) (TeO3)2] is based on 1D chains of [(UO2) (TeO3)2]∞2− propagating into the c-axis. These chains are formed by edge-sharing uranyl bipyramids which are additionally fused together by two TeO4 disphenoids, which also share two edges. The 3D framework structure of Sr [(UO2) (TeO5)] is composed of 1D [TeO5]4− chains sharing edges with UO7 bipyramids. Three tunnels based on 6-Membered rings (MRs) are propagating along [001] [010] and [100] directions. The HT/HP synthetic conditions for the preparation of single crystalline samples and their structural aspects are discussed in this work.

At the early days of organ transplantation before the diagnosis of brain death came in use only organs of non-heart-beating persons - who were the first donors after cardiac death (DCD) - could be taken for organ transplantation beside the living donors.Organs from the first brain-dead donor were transplanted in 1963 in Belgium, five years later the famous Harvard Committee criteria were published. Following that in the Western world DCD was not used for organ transplantation, just in exceptional cases for several decades.However, organ scarceness lead the way back to the idea of DCD. The original Maastricht classification discerns uncontrolled and controlled DCD in 4 categories from dead upon arrival through unsuccessful resuscitation attempt and awaiting cardiac death to cardiac arrest of a brain-dead person.The time between the cessation of the of the circulation and the perfusion of the organ with the specific storage fluid is crucial for the possible use of the organs as transplants.The recent studies explore the possibilities of the reconditioning of the organs to allow more and more of them to use for transplantation. The applicability is mostly needed in DCD cases. It is still open whether cold or warm perfusion, static or machine perfusion in continuous or pulsatile form would be the best for which organ.Despite the evidence of the usefulness of this donor category, DCD is still not used universally. For example in Europe only one-third of the countries use DCD, the main reason is the underdevelopment of the infrastructural criteria in some of the countries, and the unsolved ethical and organizational challenges in others.

In this study we assessed the utility of self-reported cognitive function using two PROMIS ® Cognitive Function (PROMIS-CF) items in an observational clinical sample of patients aged 65 and older ( n = 16,249) at a large health system. We evaluated the association of PROMIS-CF scores with clinical characteristics and Montreal Cognitive Assessment (MoCA) scores, and we used logistic regression to examine predictors of 1-year decline in PROMIS-CF scores among patients with available data. PROMIS-CF scores were associated with clinical characteristics as hypothesized, with lower (more impaired) scores for patients with cognitive impairment (CI) diagnoses, multiple comorbidities, and those taking cognitive enhancing or interfering medications. PROMIS-CF scores were also positively associated with MoCA scores. Predictors of 1-year decline in PROMIS-CF scores included CI diagnoses, use of cognitive enhancing medications, higher depression scores, and lower social role function. Our findings suggest potential utility of PROMIS-CF items in a brief patient-administered screening tool for CI.

Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon. Our ability to quantify the evolution of such permafrost landscapes in numerical models is critical for providing robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions has so far been hampered by small-scale physical processes that create a high spatial variability of thermal surface conditions, affecting the ground thermal regime and thus permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (northern Norway) over a three-year period using drone-based repeat high-resolution photogrammetry. Our results show thermokarst degradation is concentrated on the edges of the plateau, representing 77 % of observed subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to an annual volume change of 0.13±0.07 m3 yr−1 per meter of retreating edge (orthogonal to the retreat direction). Using the CryoGrid3 land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. By performing a sensitivity test for snow depths on the plateau under steady-state climate forcing, we obtain a threshold behavior for the start of edge degradation. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. For plateau snow depths in the range of field measurements, the simulated annual volume changes are broadly in agreement with the results of the drone survey. As snow depths are clearly correlated with ground surface temperatures, our results indicate that the approach can potentially be used to simulate climate-driven dynamics of edge degradation observed at our study site and other peat plateaus worldwide. Thus, the model approach represents a first step towards simulating climate-driven landscape development through thermokarst in permafrost peatlands.

Permafrost degradation in steep rock walls and associated slope destabilization have been studied increasingly in recent years. While most studies focus on mountainous and sub-Arctic regions, the occurring thermo-mechanical processes also play an important role in the high Arctic. A more precise understanding is required to assess the risk of natural hazards enhanced by permafrost warming in high-Arctic rock walls. This study presents one of the first comprehensive datasets of rock surface temperature measurements of steep rock walls in the high Arctic, comparing coastal and near-coastal settings. We applied the surface energy balance model CryoGrid 3 for evaluation, including adjusted radiative forcing to account for vertical rock walls. Our measurements comprise 4 years of rock surface temperature data from summer 2016 to summer 2020. Mean annual rock surface temperatures ranged from −0.6 in a coastal rock wall in 2017/18 to −4.3 ∘C in a near-coastal rock wall in 2019/20. Our measurements and model results indicate that rock surface temperatures at coastal cliffs are up to 1.5 ∘C higher than at near-coastal rock walls when the fjord is ice-free in winter, resulting from additional energy input due to higher air temperatures at the coast and radiative warming by relatively warm seawater. An ice layer on the fjord counteracts this effect, leading to similar rock surface temperatures to those in near-coastal settings. Our results include a simulated surface energy balance with shortwave radiation as the dominant energy source during spring and summer with net average seasonal values of up to 100 W m−2 and longwave radiation being the main energy loss with net seasonal averages between 16 and 39 W m−2. While sensible heat fluxes can both warm and cool the surface, latent heat fluxes are mostly insignificant. Simulations for future climate conditions result in a warming of rock surface temperatures and a deepening of active layer thickness for both coastal and near-coastal rock walls. Our field data present a unique dataset of rock surface temperatures in steep high-Arctic rock walls, while our model can contribute towards the understanding of factors influencing coastal and near-coastal settings and the associated surface energy balance.

Infrastructure built on perennially frozen ice-rich ground relies heavily on thermally stable subsurface conditions. Climate-warming-induced deepening of ground thaw puts such infrastructure at risk of failure. For better assessing the risk of large-scale future damage to Arctic infrastructure, improved strategies for model-based approaches are urgently needed. We used the laterally coupled 1D heat conduction model CryoGrid3 to simulate permafrost degradation affected by linear infrastructure. We present a case study of a gravel road built on continuous permafrost (Dalton highway, Alaska) and forced our model under historical and strong future warming conditions (following the RCP8.5 scenario). As expected, the presence of a gravel road in the model leads to higher net heat flux entering the ground compared to a reference run without infrastructure and thus a higher rate of thaw. Further, our results suggest that road failure is likely a consequence of lateral destabilisation due to talik formation in the ground beside the road rather than a direct consequence of a top-down thawing and deepening of the active layer below the road centre. In line with previous studies, we identify enhanced snow accumulation and ponding (both a consequence of infrastructure presence) as key factors for increased soil temperatures and road degradation. Using differing horizontal model resolutions we show that it is possible to capture these key factors and their impact on thawing dynamics with a low number of lateral model units, underlining the potential of our model approach for use in pan-Arctic risk assessments. Our results suggest a general two-phase behaviour of permafrost degradation: an initial phase of slow and gradual thaw, followed by a strong increase in thawing rates after the exceedance of a critical ground warming. The timing of this transition and the magnitude of thaw rate acceleration differ strongly between undisturbed tundra and infrastructure-affected permafrost ground. Our model results suggest that current model-based approaches which do not explicitly take into account infrastructure in their designs are likely to strongly underestimate the timing of future Arctic infrastructure failure. By using a laterally coupled 1D model to simulate linear infrastructure, we infer results in line with outcomes from more complex 2D and 3D models, but our model's computational efficiency allows us to account for long-term climate change impacts on infrastructure from permafrost degradation. Our model simulations underline that it is crucial to consider climate warming when planning and constructing infrastructure on permafrost as a transition from a stable to a highly unstable state can well occur within the service lifetime (about 30 years) of such a construction. Such a transition can even be triggered in the coming decade by climate change for infrastructure built on high northern latitude continuous permafrost that displays cold and relatively stable conditions today.

In continuous permafrost lowlands, thawing of ice-rich deposits and melting of massive ground ice lead to abrupt landscape changes called thermokarst, which have widespread consequences on the thermal, hydrological, and biogeochemical state of the subsurface. However, macro-scale land surface models (LSMs) do not resolve such localized subgrid-scale processes and could hence miss key feedback mechanisms and complexities which affect permafrost degradation and the potential liberation of soil organic carbon in high latitudes. Here, we extend the CryoGrid 3 permafrost model with a multi-scale tiling scheme which represents the spatial heterogeneities of surface and subsurface conditions in ice-rich permafrost lowlands. We conducted numerical simulations using stylized model setups to assess how different representations of micro- and meso-scale heterogeneities affect landscape evolution pathways and the amount of permafrost degradation in response to climate warming. At the micro-scale, the terrain was assumed to be either homogeneous or composed of ice-wedge polygons, and at the meso-scale it was assumed to be either homogeneous or resembling a low-gradient slope. We found that by using different model setups and parameter sets, a multitude of landscape evolution pathways could be simulated which correspond well to observed thermokarst landscape dynamics across the Arctic. These pathways include the formation, growth, and gradual drainage of thaw lakes; the transition from low-centred to high-centred ice-wedge polygons; and the formation of landscape-wide drainage systems due to melting of ice wedges. Moreover, we identified several feedback mechanisms due to lateral transport processes which either stabilize or destabilize the thermokarst terrain. The amount of permafrost degradation in response to climate warming was found to depend primarily on the prevailing hydrological conditions, which in turn are crucially affected by whether or not micro- and/or meso-scale heterogeneities were considered in the model setup. Our results suggest that the multi-scale tiling scheme allows for simulating ice-rich permafrost landscape dynamics in a more realistic way than simplistic one-dimensional models and thus facilitates more robust assessments of permafrost degradation pathways in response to climate warming. Our modelling work improves the understanding of how micro- and meso-scale processes affect the evolution of ice-rich permafrost landscapes, and it informs macro-scale modellers focusing on high-latitude land surface processes about the necessities and possibilities for the inclusion of subgrid-scale processes such as thermokarst within their models.

Boreal forests in permafrost regions make up around one-third of the global forest cover and are an essential component of regional and global climate patterns. Further, climatic change can trigger extensive ecosystem shifts such as the partial disappearance of near-surface permafrost or changes to the vegetation structure and composition. Therefore, our aim is to understand how the interactions between the vegetation, permafrost and the atmosphere stabilize the forests and the underlying permafrost. Existing model setups are often static or are not able to capture important processes such as the vertical structure or the leaf physiological properties. There is a need for a physically based model with a robust radiative transfer scheme through the canopy. A one-dimensional land surface model (CryoGrid) is adapted for the application in vegetated areas by coupling a multilayer canopy model (CLM-ml v0; Community Land Model) and is used to reproduce the energy transfer and thermal regime at a study site (63.18946∘ N, 118.19596∘ E) in mixed boreal forest in eastern Siberia. An extensive comparison between measured and modeled energy balance variables reveals a satisfactory model performance justifying its application to investigate the thermal regime; surface energy balance; and the vertical exchange of radiation, heat and water in this complex ecosystem. We find that the forests exert a strong control on the thermal state of permafrost through changing the radiation balance and snow cover phenology. The forest cover alters the surface energy balance by inhibiting over 90 % of the solar radiation and suppressing turbulent heat fluxes. Additionally, our simulations reveal a surplus in longwave radiation trapped below the canopy, similar to a greenhouse, which leads to a magnitude in storage heat flux comparable to that simulated at the grassland site. Further, the end of season snow cover is 3 times greater at the forest site, and the onset of the snow-melting processes are delayed.

Abstract Analyzing movement is essential for understanding complex behavioral interactions. Up to date, there are different movement analysis methods. Previous studies working with 2D movement analysis systems provided first insights into this field. 2D systems can capture only two of three spatial dimensions and thus allow analyses of movements on surfaces. 3D systems include all three spatial dimensions. Thus, 3D movement analysis is essential for analyzing movements in air or in water. Especially during the last years, 3D movement analysis has progressed a lot thanks to technological advances. Unfortunately, these technological advanced systems are often very expensive and handling flexibility is limited. Therefore, we implemented a customizable 3D movement analysis system for aquatic organisms. The system is composed of an experimental arena, backgrounds with different contrasts, three visible light diode spots, one infrared diode spot for analysis in the dark, and two Raspberry Pi 3 Model B V 1.2 equipped with cameras without infrared filters. We assembled a user‐friendly system that is controlled directly via a graphical user interface (GUI). It not only delivers raw data but also runs detailed analyses of common behavioral parameters. The accuracy of those analyses can be enhanced by the integrated manual error‐correction method that allows to interrupt the automatic track analysis and to correct detection errors by hand. In our case study, we show the spectrum of opportunities of the system by determining the swimming behavior of the freshwater crustacean Daphnia magna. We successfully recorded its swimming behavior under visible light and infrared light (simulating darkness for the daphniids) conditions and analyzed swimming velocity, motion patterns, depth selection, and tendency of clustering. In our case study, we show that using the integrated manual error‐correction method leads to a clear accuracy improvement. Our movement analysis system is not restricted to aquaria but can be adopted to movements in cages and even open areas of different sizes both in water or air, which emphasizes its potential. Together with a user manual, the open‐source MATLAB algorithm and the recommended hardware components, the here presented system enables semi‐automatic 3D movement analysis, which can be adapted to the individual needs.

The PI3K-related kinase (PIKK) SMG1 monitors the progression of metazoan nonsense-mediated mRNA decay (NMD) by phosphorylating the RNA helicase UPF1. Previous work has shown that the activity of SMG1 is impaired by small molecule inhibitors, is reduced by the SMG1 interactors SMG8 and SMG9, and is downregulated by the so-called SMG1 insertion domain. However, the molecular basis for this complex regulatory network has remained elusive. Here, we present cryo-electron microscopy reconstructions of human SMG1-9 and SMG1-8-9 complexes bound to either a SMG1 inhibitor or a non-hydrolyzable ATP analog at overall resolutions ranging from 2.8 to 3.6 Å. These structures reveal the basis with which a small molecule inhibitor preferentially targets SMG1 over other PIKKs. By comparison with our previously reported substrate-bound structure (Langer et al.,2020), we show that the SMG1 insertion domain can exert an autoinhibitory function by directly blocking the substrate-binding path as well as overall access to the SMG1 kinase active site. Together with biochemical analysis, our data indicate that SMG1 autoinhibition is stabilized by the presence of SMG8. Our results explain the specific inhibition of SMG1 by an ATP-competitive small molecule, provide insights into regulation of its kinase activity within the NMD pathway, and expand the understanding of PIKK regulatory mechanisms in general.

The human RNA exosome contains a nuclear co-factor MTR4, which unwinds structural RNAs and recruits adaptors for different RNA processing and decay pathways. Here the authors uncover new variations of the arch-interacting motif (AIM) in NVL and ZCCHC8 and characterise their interaction with MTR4.

Abstract Background This paper is part of a series comparing different psychometric approaches to evaluate patient-reported outcome (PRO) measures using the same items and dataset. We provide an overview and example application to demonstrate 1) using item response theory (IRT) to identify poor and well performing items; 2) testing if items perform differently based on demographic characteristics (differential item functioning, DIF); and 3) balancing IRT and content validity considerations to select items for short forms. Methods Model fit, local dependence, and DIF were examined for 51 items initially considered for the Patient-Reported Outcomes Measurement Information System® (PROMIS®) Depression item bank. Samejima’s graded response model was used to examine how well each item measured severity levels of depression and how well it distinguished between individuals with high and low levels of depression. Two short forms were constructed based on psychometric properties and consensus discussions with instrument developers, including psychometricians and content experts. Calibrations presented here are for didactic purposes and are not intended to replace official PROMIS parameters or to be used for research. Results Of the 51 depression items, 14 exhibited local dependence, 3 exhibited DIF for gender, and 9 exhibited misfit, and these items were removed from consideration for short forms. Short form 1 prioritized content, and thus items were chosen to meet DSM-V criteria rather than being discarded for lower discrimination parameters. Short form 2 prioritized well performing items, and thus fewer DSM-V criteria were satisfied. Short forms 1–2 performed similarly for model fit statistics, but short form 2 provided greater item precision. Conclusions IRT is a family of flexible models providing item- and scale-level information, making it a powerful tool for scale construction and refinement. Strengths of IRT models include placing respondents and items on the same metric, testing DIF across demographic or clinical subgroups, and facilitating creation of targeted short forms. Limitations include large sample sizes to obtain stable item parameters, and necessary familiarity with measurement methods to interpret results. Combining psychometric data with stakeholder input (including people with lived experiences of the health condition and clinicians) is highly recommended for scale development and evaluation.

Ice-wedge polygons are common features of lowland tundra in the continuous permafrost zone and prone to rapid degradation through melting of ground ice. There are many interrelated processes involved in ice-wedge thermokarst and it is a major challenge to quantify their influence on the stability of the permafrost underlying the landscape. In this study we used a numerical modelling approach to investigate the degradation of ice wedges with a focus on the influence of hydrological conditions. Our study area was Samoylov Island in the Lena River delta of northern Siberia, for which we had in situ measurements to evaluate the model. The tailored version of the CryoGrid 3 land surface model was capable of simulating the changing microtopography of polygonal tundra and also regarded lateral fluxes of heat, water, and snow. We demonstrated that the approach is capable of simulating ice-wedge degradation and the associated transition from a low-centred to a high-centred polygonal microtopography. The model simulations showed ice-wedge degradation under recent climatic conditions of the study area, irrespective of hydrological conditions. However, we found that wetter conditions lead to an earlier onset of degradation and cause more rapid ground subsidence. We set our findings in correspondence to observed types of ice-wedge polygons in the study area and hypothesized on remaining discrepancies between modelled and observed ice-wedge thermokarst activity. Our quantitative approach provides a valuable complement to previous, more qualitative and conceptual, descriptions of the possible pathways of ice-wedge polygon evolution. We concluded that our study is a blueprint for investigating thermokarst landforms and marks a step forward in understanding the complex interrelationships between various processes shaping ice-rich permafrost landscapes.

Ground-based observations of land–atmosphere fluxes are necessary to progressively improve global climate models. Observed data can be used for model evaluation and to develop or tune process models. In arctic permafrost regions, climate–carbon feedbacks are amplified. Therefore, increased efforts to better represent these regions in global climate models have been made in recent years. We present a multi-annual time series of land–atmosphere carbon dioxide fluxes measured in situ with the eddy covariance technique in the Siberian Arctic (72°22′ N, 126°30′ E). The site is part of the international network of eddy covariance flux observation stations (FLUXNET; site ID: Ru-Sam). The data set includes consistently processed fluxes based on concentration measurements of closed-path and open-path gas analyzers. With parallel records from both sensor types, we were able to apply a site-specific correction to open-path fluxes. This correction is necessary due to a deterioration of data, caused by heat generated by the electronics of open-path gas analyzers. Parameterizing this correction for subperiods of distinct sensor setups yielded good agreement between open- and closed-path fluxes. We compiled a long-term (2002 to 2017) carbon dioxide flux time series that we additionally gap-filled with a standardized approach. The data set was uploaded to the Pangaea database and can be accessed through https://doi.org/10.1594/PANGAEA.892751.

Earth system models (ESMs) are our primary tool for projecting future climate change, but their ability to represent small-scale land surface processes is currently limited. This is especially true for permafrost landscapes in which melting of excess ground ice and subsequent subsidence affect lateral processes which can substantially alter soil conditions and fluxes of heat, water, and carbon to the atmosphere. Here we demonstrate that dynamically changing microtopography and related lateral fluxes of snow, water, and heat can be represented through a tiling approach suitable for implementation in large-scale models, and we investigate which of these lateral processes are important to reproduce observed landscape evolution. Combining existing methods for representing excess ground ice, snow redistribution, and lateral water and energy fluxes in two coupled tiles, we show that the model approach can simulate observed degradation processes in two very different permafrost landscapes. We are able to simulate the transition from low-centered to high-centered polygons, when applied to polygonal tundra in the cold, continuous permafrost zone, which results in (i) a more realistic representation of soil conditions through drying of elevated features and wetting of lowered features with related changes in energy fluxes, (ii) up to 2 ∘C reduced average permafrost temperatures in the current (2000–2009) climate, (iii) delayed permafrost degradation in the future RCP4.5 scenario by several decades, and (iv) more rapid degradation through snow and soil water feedback mechanisms once subsidence starts. Applied to peat plateaus in the sporadic permafrost zone, the same two-tile system can represent an elevated peat plateau underlain by permafrost in a surrounding permafrost-free fen and its degradation in the future following a moderate warming scenario. These results demonstrate the importance of representing lateral fluxes to realistically simulate both the current permafrost state and its degradation trajectories as the climate continues to warm. Implementing laterally coupled tiles in ESMs could improve the representation of a range of permafrost processes, which is likely to impact the simulated magnitude and timing of the permafrost–carbon feedback.

Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3 ∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) −32.7 ∘C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20.75 m. At this depth, the temperature has increased from −9.1 ∘C in 2006 to −7.7 ∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.

PI3K-related kinases (PIKKs) are large Serine/Threonine (Ser/Thr)-protein kinases central to the regulation of many fundamental cellular processes. PIKK family member SMG1 orchestrates progression of an RNA quality control pathway, termed nonsense-mediated mRNA decay (NMD), by phosphorylating the NMD factor UPF1. Phosphorylation of UPF1 occurs in its unstructured N- and C-terminal regions at Serine/Threonine-Glutamine (SQ) motifs. How SMG1 and other PIKKs specifically recognize SQ motifs has remained unclear. Here, we present a cryo-electron microscopy (cryo-EM) reconstruction of a human SMG1-8-9 kinase complex bound to a UPF1 phosphorylation site at an overall resolution of 2.9 Å. This structure provides the first snapshot of a human PIKK with a substrate-bound active site. Together with biochemical assays, it rationalizes how SMG1 and perhaps other PIKKs specifically phosphorylate Ser/Thr-containing motifs with a glutamine residue at position +1 and a hydrophobic residue at position -1, thus elucidating the molecular basis for phosphorylation site recognition.

PIN1 is a phosphorylation-directed member of the peptidyl-prolyl cis/trans isomerase (PPIase) family that facilitates conformational changes in phosphorylated targets such as c-MYC (MYC). Following signaling events that mediate phosphorylation of MYC at Serine 62, PIN1 establishes structurally distinct pools of MYC through its trans-cis and cis-trans isomerization activity at Proline 63. Through these isomerization steps, PIN1 functionally regulates MYC’s stability, the molecular timing of its DNA binding and transcriptional activity, and its subnuclear localization. Recently, our group showed that Serine 62 phosphorylated MYC can associate with the inner basket of the nuclear pore (NP) in a PIN1-dependent manner. The poised euchromatin at the NP basket enables rapid cellular response to environmental signals and cell stress, and PIN1-mediated trafficking of MYC calibrates this response. In this perspective, we describe the molecular aspects of PIN1 target recognition and PIN1’s function in the context of its temporal and spatial regulation of MYC.

The shortage of information on snow properties in high latitudes places a major limitation on permafrost and more generally climate modelling. A dedicated field program was therefore carried out to investigate snow properties and their spatial variability at a polygonal tundra permafrost site. Notably, snow samples were analysed for surface-normal thermal conductivity (Keff − z) based on X-ray microtomography. Also, the detailed snow model SNOWPACK was adapted to these Arctic conditions to enable relevant simulations of the ground thermal regime. Finally, the sensitivity of soil temperatures to snow spatial variability was analysed.Within a typical tundra snowpack composed of depth hoar overlain by wind slabs, depth hoar samples were found more conductive (Keff − z = 0.22±0.05 W m−1 K−1) than in most previously published studies, which could be explained by their high density and microstructural anisotropy. Spatial variations in the thermal properties of the snowpack were well explained by the microtopography and ground surface conditions of the polygonal tundra, which control depth hoar growth and snow accumulation. Our adaptations to SNOWPACK, phenomenologically taking into account the effects of wind compaction, basal vegetation, and water vapour flux, yielded realistic density and Keff − z profiles that greatly improved simulations of the ground thermal regime. Also, a density- and anisotropy-based parameterization for Keff − z lead to further slight improvements. Soil temperatures were found to be particularly sensitive to snow conditions during the early winter and polar night, highlighting the need for improved snow characterization and modelling over this period.

Resistance to therapy can be driven by intratumoral heterogeneity. Here, the authors show that drug tolerant persistent cell populations emerge during treatment, and these emergent populations arise through epigenetically mediated cell state transitions rather than sub population selection.

The impact of plastic debris floating at the sea surface on the lowest trophic levels of the food web remains unknown. Here, using leaching experiments, the authors show that plastics release dissolved organic carbon into the ambient seawater that is rapidly taken up by marine microbes stimulating their growth.

Herein, the novel uranyl selenate and selenite compounds Rb2[(UO2)2(SeO4)3], Rb2[(UO2)3(SeO3)2O2], Rb2[UO2(SeO4)2(H2O)]·2H2O, and (UO2)2(HSeO3)2(H2SeO3)2Se2O5 have been synthesized using either slow evaporation or hydrothermal methods under acidic conditions and their structures were refined using single crystal X-ray diffraction. Rb2[(UO2)2(SeO4)3] synthesized hydrothermally adopts a layered 2D tetragonal structure in space group P42/ncm with a = 9.8312(4) Å, c = 15.4924(9) Å, and V = 1497.38(15) Å, where it consists of UO7 polyhedra coordinated via SeO4 units to create units UO2(SeO4)58− moieties which interlink to create layers in which Rb+ cations reside in the interspace. Rb2[(UO2)3(SeO3)2O2] synthesized hydrothermally adopts a layered 2D triclinic structure in space group P1¯ with a = 7.0116(6) Å, b = 7.0646(6) Å, c = 8.1793(7) Å, α = 103.318(7)°, β = 105.968(7)°, γ = 100.642(7)° and V = 365.48(6) Å3, where it consists of edge sharing UO7, UO8 and SeO3 polyhedra that form [(UO2)3(SeO3)2O2] layers in which Rb+ cations are found in the interlayer space. Rb2[UO2(SeO4)2(H2O)]·2H2O synthesized hydrothermally adopts a chain 1D orthorhombic structure in space group Pmn21 with a = 13.041(3) Å, b = 8.579(2) Å, c = 11.583(2) Å, and V = 1295.9(5) Å3, consisting of UO7 polyhedra that corner share with one H2O and four SeO42− ligands, creating infinite chains. (UO2)2(HSeO3)2(H2SeO3)2Se2O5 synthesized under slow evaporation conditions adopts a 0D orthorhombic structure in space group Cmc21 with a = 28.4752(12) Å, b = 6.3410(3) Å, c = 10.8575(6) Å, and V = 1960.45(16) Å3, consisting of discrete rings of [(UO2)2(HSeO3)2(H2SeO3)2Se2O5]2. (UO2)2(HSeO3)2(H2SeO3)2Se2O5 is apparently only the second example of a uranyl diselenite compound to be reported. A combination of single crystal X-ray diffraction and bond valance sums calculations are used to characterise all samples obtained in this investigation. The structures uncovered in this investigation are discussed together with the broader family of uranyl selenates and selenites, particularly in the context of the role acidity plays during synthesis in coercing specific structure, functional group, and topology formations.

It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.

Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104 m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002–2013) high-resolution aerial and satellite imagery with a resolution of 5 m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6 m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106 km2 across the Arctic, about 17 % of the Arctic lowland ( 6 m2 down to 1. 0 × 102 m2 contributed up to 21 % to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101 km−2. Ponds are the dominant waterbody type by number in all landscapes representing 45–99 % of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area boundaries, and maps of regional permafrost landscapes including detailed metadata are available at https://doi.pangaea.de/10.1594/PANGAEA.868349.

Permafrost is a sensitive element of the cryosphere, but operational monitoring of the ground thermal conditions on large spatial scales is still lacking. Here, we demonstrate a remote-sensing-based scheme that is capable of estimating the transient evolution of ground temperatures and active layer thickness by means of the ground thermal model CryoGrid 2. The scheme is applied to an area of approximately 16 000 km2 in the Lena River delta (LRD) in NE Siberia for a period of 14 years. The forcing data sets at 1 km spatial and weekly temporal resolution are synthesized from satellite products and fields of meteorological variables from the ERA-Interim reanalysis. To assign spatially distributed ground thermal properties, a stratigraphic classification based on geomorphological observations and mapping is constructed, which accounts for the large-scale patterns of sediment types, ground ice and surface properties in the Lena River delta. A comparison of the model forcing to in situ measurements on Samoylov Island in the southern part of the study area yields an acceptable agreement for the purpose of ground thermal modeling, for surface temperature, snow depth, and timing of the onset and termination of the winter snow cover. The model results are compared to observations of ground temperatures and thaw depths at nine sites in the Lena River delta, suggesting that thaw depths are in most cases reproduced to within 0.1 m or less and multi-year averages of ground temperatures within 1–2 °C. Comparison of monthly average temperatures at depths of 2–3 m in five boreholes yielded an RMSE of 1.1 °C and a bias of −0.9 °C for the model results. The highest ground temperatures are calculated for grid cells close to the main river channels in the south as well as areas with sandy sediments and low organic and ice contents in the central delta, where also the largest thaw depths occur. On the other hand, the lowest temperatures are modeled for the eastern part, which is an area with low surface temperatures and snow depths. The lowest thaw depths are modeled for Yedoma permafrost featuring very high ground ice and soil organic contents in the southern parts of the delta. The comparison to in situ observations indicates that transient ground temperature modeling forced by remote-sensing data is generally capable of estimating the thermal state of permafrost (TSP) and its time evolution in the Lena River delta. The approach could hence be a first step towards remote detection of ground thermal conditions and active layer thickness in permafrost areas.

La sarcoidosis es una enfermedad de etiología desconocida, que afecta con mayor frecuencia a adultos jóvenes. El diagnóstico es clínico, radiológico e histopatológico y la presentación frecuente es la de síntomas constitucionales y respiratorios, acompañados de linfoadenopatías hiliares y mediastinales, con o sin compromiso del parénquima pulmonar. La confirmación es anatomopatológica con la demostración de granulomas no caseificantes en los tejidos afectados, con cultivos negativos para micobacterias y hongos. Presentamos los hallazgos clínicos, radiológicos e histopatológicos en 8 pacientes. Cuatro eran de sexo masculino y la edad promedio fue de 44 años (28-59). Dos de los pacientes se encontraban asintomáticos al momento del diagnóstico, cinco de ellos se presentaron con hallazgos toráxicos (tos, dolor retroesternal y arritmias) y uno con síntomas extratoráxicos (artritis de ambos tobillos). Todos presentaron adenopatías mediastinales en los estudios radiológicos y tomográficos y seis de ellos se encontraban en el estadio 2. En cuanto al tratamiento, dos pacientes remitieron espontáneamente, cuatro Todos presentaron adenopatías mediastinales en los estudios radiológicos y tomográficos y seis de ellos se encontraban en el estadio 2. En cuanto al tratamiento, dos pacientes remitieron espontáneamente, cuatro pacientes recibieron prednisona, uno requirió prednisona y azatioprina y de un paciente no contamos con registros de tratamiento. Uno de nuestros pacientes evolucionó desfavorablemente con aparición y recidiva de lesiones de eritema nodoso severas y bilaterales que motivaron la necesidad de tratamiento permanente con esteroides e inmunosupresores. Si bien se trata de una enfermedad de baja prevalencia en nuestro país, deberíamos mantener un alto índice de sospecha ante la presentación de síntomas respiratorios con lesiones parenquimatosas y adenopatías mediastinales, con o sin manifestaciones extratorácicas.