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At least 55 records · Page 3

Efficient Total Chemical Synthesis of 13 C= 18 O Isotopomers of Human Insulin for Isotope-Edited FTIR

Isotope–edited two–dimensional Fourier transform infrared spectroscopy (2 D FTIR) can potentially provide a unique probe of protein structure and dynamics. However, general methods for the site–specific incorporation of stable 13 C= 18 O labels into the polypeptide backbone of the protein molecule have not yet been established. Here we describe, as a prototype for the incorporation of specific arrays of isotope labels, the total chemical synthesis—via a key ester insulin intermediate—of 97 % enriched [(1– 13 C= 18 O)Phe B24 ] human insulin: stable–isotope labeled at a single backbone amide carbonyl. The amino acid sequence as well as the positions of the disulfide bonds and the correctly folded structure were unambiguously confirmed by the X–ray crystal structure of the synthetic protein molecule. In vitro assays of the isotope labeled [(1– 13 C= 18 O)Phe B24 ] human insulin showed that it had full insulin receptor binding activity. Linear and 2 D IR spectra revealed a distinct red–shifted amide I carbonyl band peak at 1595 cm –1 resulting from the (1– 13 C= 18 O)Phe B24 backbone label. Furthermore, this work illustrates the utility of chemical synthesis to enable the application of advanced physical methods for the elucidation of the molecular basis of protein function.

2D IR spectra↗

Uncertainty-refined image segmentation under domain shift

Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Martinez, Carianne↗

CCAAT/enhancer-binding protein delta activates insulin-like growth factor-I gene transcription in osteoblasts. Identification of a novel cyclic AMP signaling pathway in bone

Insulin-like growth factor-I (IGF-I) plays a key role in skeletal growth by stimulating bone cell replication and differentiation. We previously showed that prostaglandin E2 (PGE2) and other cAMP-activating agents enhanced IGF-I gene transcription in cultured primary rat osteoblasts through promoter 1, the major IGF-I promoter, and identified a short segment of the promoter, termed HS3D, that was essential for hormonal regulation of IGF-I gene expression. We now demonstrate that CCAAT/enhancer-binding protein (C/EBP) delta is a major component of a PGE2-stimulated DNA-protein complex involving HS3D and find that C/EBPdelta transactivates IGF-I promoter 1 through this site. Competition gel shift studies first indicated that a core C/EBP half-site (GCAAT) was required for binding of a labeled HS3D oligomer to osteoblast nuclear proteins. Southwestern blotting and UV-cross-linking studies showed that the HS3D probe recognized a approximately 35-kDa nuclear protein, and antibody supershift assays indicated that C/EBPdelta comprised most of the PGE2-activated gel-shifted complex. C/EBPdelta was detected by Western immunoblotting in osteoblast nuclear extracts after treatment of cells with PGE2. An HS3D oligonucleotide competed effectively with a high affinity C/EBP site from the rat albumin gene for binding to osteoblast nuclear proteins. Co-transfection of osteoblast cell cultures with a C/EBPdelta expression plasmid enhanced basal and PGE2-activated IGF-I promoter 1-luciferase activity but did not stimulate a reporter gene lacking an HS3D site. By contrast, an expression plasmid for the related protein, C/EBPbeta, did not alter basal IGF-I gene activity but did increase the response to PGE2. In osteoblasts and in COS-7 cells, C/EBPdelta, but not C/EBPbeta, transactivated a reporter gene containing four tandem copies of HS3D fused to a minimal promoter; neither transcription factor stimulated a gene with four copies of an HS3D mutant that was unable to bind osteoblast nuclear proteins. These results identify C/EBPdelta as a hormonally activated inducer of IGF-I gene transcription in osteoblasts and show that the HS3D element within IGF-I promoter 1 is a high affinity binding site for this protein.

NASA Discipline Musculoskeletal↗

Unraveling the 13C NMR Chemical Shifts in Single-Walled Carbon Nanotubes: Dependence on Diameter and Electronic Structure

The atomic specificity afforded by nuclear magnetic resonance (NMR) spectroscopy could enable detailed mechanistic information about single-walled carbon nanotube (SWCNT) functionalization as well as the noncovalent molecular interactions that dictate ground-state charge transfer and separation by electronic structure and diameter. However, to date, the polydispersity present in as-synthesized SWCNT populations has obscured the dependence of the SWCNT {sup 13}C chemical shift on intrinsic parameters such as diameter and electronic structure, meaning that no information is gleaned for specific SWCNTs with unique chiral indices. In this article, we utilize a combination of {sup 13}C labeling and density gradient ultracentrifugation (DGU) to produce an array of {sup 13}C-labeled SWCNT populations with varying diameter, electronic structure, and chiral angle. We find that the SWCNT isotropic {sup 13}C chemical shift decreases systematically with increasing diameter for semiconducting SWCNTs, in agreement with recent theoretical predictions that have heretofore gone unaddressed. Furthermore, we find that the {sup 13}C chemical shifts for small diameter metallic and semiconducting SWCNTs differ significantly, and that the full-width of the isotropic peak for metallic SWCNTs is much larger than that of semiconducting nanotubes, irrespective of diameter.

14 SOLAR ENERGY↗

Detecting carbon uptake and cellular allocation by individual algae in multispecies assemblages: Tracking carbon into single algal cells

Algal species vary in carbon (C) need and uptake rates. Understanding differences in C uptake and cellular allocation among species from natural communities will bring new insight into many ecosystem process questions including how species changes will alter energy availability and C sequestration in aquatic ecosystems. A major limitation of current methods that measure algal C incorporation is the inability to separate the response of individual species from mixed-species assemblages. I used Fourier-transform infrared microspectroscopy to qualitatively measure inorganic 13 C isotope incorporation into individual algal cells in single species, two species, and natural phytoplankton assemblages. Lateral shifts in spectral peaks from 13 C treatments were observed in all species. Comparison of peaks associated with carbohydrates, proteins, and lipids allowed for the detection of which individuals took in C, and which macromolecules the C was used to make. For example, shifts in Spirogyra spectral peaks showed substantial C incorporation in carbohydrates. Further, shifts in peaks at 1160 cm -1 , 1108 cm -1 , 1080 cm -1 , 1048 cm -1 , and 1030 cm -1 suggested C was being allocated into cellulose. The natural phytoplankton assemblage demonstrated how C could be tracked into co-occurring species. A diatom had large shifts in protein and carbohydrate peaks, while a green alga and euglenoid had only a few shifts in protein related peaks. Fourier-transform infrared microspectroscopy is an established, label free method for measuring the chemical composition of algal cells. However, adding a label such as 13 C isotope can greatly expand the technique's capabilities by qualitatively tracking C movement between inorganic and organic states within single cells.

54 ENVIRONMENTAL SCIENCES↗

Cellulose Structural Polymorphism in Plant Primary Cell Walls Investigated by High-Field 2D Solid-State NMR Spectroscopy and Density Functional Theory Calculations

The native cellulose of bacterial, algal, and animal origins has been well studied structurally using X-ray and neutron diffraction and solid-state NMR spectroscopy, and is known to consist of varying proportions of two allomorphs, Iα and Iβ, which differ in hydrogen bonding, chain packing, and local conformation. In comparison, cellulose structure in plant primary cell walls is much less understood because plant cellulose has lower crystallinity and extensive interactions with matrix polysaccharides. Here we have combined two-dimensional magic-angle-spinning (MAS) solid-state nuclear magnetic resonance (solid-state NMR) spectroscopy at high magnetic fields with density functional theory (DFT) calculations to obtain detailed information about the structural polymorphism and spatial distributions of plant primary-wall cellulose. 2D 13 C– 13 C correlation spectra of uniformly 13 C-labeled cell walls of several model plants resolved seven sets of cellulose chemical shifts. Among these, five sets (denoted a–e) belong to cellulose in the interior of the microfibril while two sets (f and g) can be assigned to surface cellulose. Importantly, most of the interior cellulose 13 C chemical shifts differ significantly from the 13 C chemical shifts of the Iα and Iβ allomorphs, indicating that plant primary-wall cellulose has different conformations, packing, and hydrogen bonding from celluloses of other organisms. 2D 13 C– 13 C correlation experiments with long mixing times and with water polarization transfer revealed the spatial distributions and matrix-polysaccharide interactions of these cellulose structures. Celluloses f and g are well mixed chains on the microfibril surface, celluloses a and b are interior chains that are in molecular contact with the surface chains, while cellulose c resides in the core of the microfibril, outside spin diffusion contact with the surface. Interestingly, cellulose d, whose chemical shifts differ most significantly from those of bacterial, algal, and animal cellulose, interacts with hemicellulose, is poorly hydrated, and is targeted by the protein expansin during wall loosening. To obtain information about the C6 hydroxymethyl conformation of these plant celluloses, we carried out DFT calculations of 13 C chemical shifts, using the Iα and Iβ crystal structures as templates and varying the C5–C6 torsion angle. Comparison with the experimental chemical shifts suggests that all interior cellulose favor the tg conformation, but cellulose d also has a similar propensity to adopt the gt conformation. In conclusion, these results indicate that cellulose in plant primary cell walls, due to their interactions with matrix polysaccharides, and has polymorphic structures that are not a simple superposition of the Iα and Iβ allomorphs, thus distinguishing them from bacterial and animal celluloses.

59 BASIC BIOLOGICAL SCIENCES↗

Unraveling metabolism underpinning biomass composition shift in Scenedesmus obliquus under simulated outdoor conditions using 13 C-fluxomics

To render the resulting biomass more attractive and amenable for utilization as the basis for low-carbon intensity bioproducts, single-celled algae need to be biochemically and metabolically poised to assimilate and store the delivered carbon in the fastest and most efficient manner. Accelerating biochemical carbon storage, as primarily carbohydrates or lipids, is critical to achieve the high carbon capture potential that is assigned to algae. To guide strain optimization and engineering for maximizing carbon capture and storage, it is essential to elucidate the link between carbon metabolism and biomass composition. Most published metabolomics work in algae remains largely restricted to ideal and simplified environmental conditions in model organisms, thereby limiting their translation to outdoor implementation. In this work, we utilize 13 C isotopic labeling to characterize distinct intracellular metabolic fluxes before, during, and after nitrogen depletion-induced compositional shifts in Scenedesmus obliquus UTEX 393. The results indicate that a transition to carbohydrates is characterized by diverting flux to starch instead of replenishing the Calvin cycle for CO 2 fixation whereas the subsequent transition to lipids is fueled by NADPH produced by upregulating the phosphoenolpyruvate carboxylase (PEPC)–malic enzyme (ME) cycle flux. Our work highlights bottlenecks to carbohydrate- and lipid-rich biomass and can guide implementable strategies to control the fate of fixed carbon in S. obliquus.

09 BIOMASS FUELS↗

Is the Kok effect a respiratory phenomenon? Metabolic insight using 13 C labeling in Helianthus annuus leaves

The Kok effect is a well-known phenomenon in which the quantum yield of photosynthesis changes abruptly at low light. This effect has often been interpreted as a shift in leaf respiratory metabolism and thus used widely to measure day respiration. However, there is still no formal evidence that the Kok effect has a respiratory origin. Here, both gas exchange and isotopic labeling were carried out on sunflower leaves, using glucose that was 13 C-enriched at specific C-atom positions. Position-specific decarboxylation measurements and NMR analysis of metabolites were used to trace the fate of C-atoms in metabolism. Decarboxylation rates were significant at low light (including above the Kok break point) and increased with decreasing irradiance below 100 µmol photons m –2 s –1 . The variation in several metabolite pools such as malate, fumarate or citrate, and flux calculations suggest the involvement of several decarboxylating pathways in the Kok effect, including the malic enzyme. Our results show that day respiratory CO 2 evolution plays an important role in the Kok effect. Furthermore, the increase in the apparent quantum yield of photosynthesis below the Kok break point is also probably related to malate metabolism, which participates in maintaining photosynthetic linear electron flow.

54 ENVIRONMENTAL SCIENCES↗

A VLSI decomposition of the deBruijn graph

The nth order deBruijn graph Bn is the state diagram for an n-stage binary shift register. It is a directed graph with 2 to the n vertices, each labeled with an n-bit binary string, and 2 to the n+1 edges, each labeled with an (n+1)-bit binary string. It is shown that Bn can be built by appropriately connecting together with extra edges many isomorphic copies of a fixed graph, which is called a building block for Bn. The efficiency of such a building block is refined as the fraction of the edges of Bn which are present in the copies of the building block. It is then shown that for any alpha less than 1, there exists a graph which is a building block for Bn of efficiency greater than alpha for all sufficiently large n. The results are illustrated by showing how a special hierarchical family of building blocks has been used to construct a very large Viterbi decoder which will be used on the Galileo mission.

Collins, Oliver↗

Uncertainty-refined image segmentation under domain shift

A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. The label of any image element with an uncertainty value above a predefined threshold is replaced with a new label corresponding to the class with the next highest value.

Martinez, Carianne↗

Hydra: Computer Vision for Online Data Quality Monitoring

Hydra is a system utilizing computer vision for near real-time data quality monitoring. Currently operational across all of Jefferson Lab’s experimental halls, it reduces the workload of shift takers by autonomously monitoring diagnostic plots during experiments. Hydra uses "off-the-shelf" supervised learning technologies and is supported by a comprehensive MySQL database. To simplify access, web apps have been developed to facilitate both labeling and monitoring of Hydra’s inferences. Hydra can connect with the alarm system and incorporates complete historical tracking, enabling it to identify issues that shift takers could miss. When issues are detected, a natural first question is: "Why does Hydra think there is a problem?" To answer, Hydra employs Gradient-weighted Class Activation Maps (GradCAM) to identify regions of the image that are important for the specific classification. This interpretive layer enhances transparency and trustworthiness, which is essential for integration with experiment workflows and operation. The Hydra system, results, and sociological considerations for deployment will be discussed.

Jeske, Torri↗

Design, synthesis and evaluation of a potent substrate analog inhibitor identified by scanning Ala/Phe mutagenesis, mimicking substrate co-evolution, against multidrug-resistant HIV-1 protease

Highlights: •Inhibitors against MDR HIV-1 protease were designed, synthesized and evaluated. •Lead peptide (6a) showed potent inhibition (IC{sub 50}: 4.4 nM) of MDR HIV-1 protease. •(6a) Showed favorable binding isotherms against NL4-3 and MDR proteases. •(6a) Induced perturbations in the {sup 15}N-HSQC spectrum of MDR HIV-1 protease. •Molecular modeling suggested that (6a) may induce total flap closure inMDR protease. -- Abstract: Multidrug-resistant (MDR) clinical isolate-769, human immunodeficiency virus type-1 (HIV-1) protease (PDB ID: (1TW7)), was shown to exhibit wide-open flaps and an expanded active site cavity, causing loss of contacts with protease inhibitors. In the current study, the expanded active site cavity of MDR769 HIV-1 protease was screened with a series of peptide-inhibitors that were designed to mimic the natural substrate cleavage site, capsid/p2. Scanning Ala/Phe chemical mutagenesis approach was incorporated into the design of the peptide series to mimic the substrate co-evolution. Among the peptides synthesized and evaluated, a lead peptide (6a) with potent activity (IC{sub 50}: 4.4 nM) was identified against the MDR769 HIV-1 protease. Isothermal titration calorimetry data showed favorable binding profile for 6aagainst both wild type and MDR769 HIV-1 protease variants. Nuclear magnetic resonance spectrum of {sup 15}N-labeled MDR769 HIV-1 protease in complex with 6a showed some major perturbations in chemical shift, supporting the peptide induced conformational changes in protease. Modeling analysis revealed multiple contacts between 6a and MDR769 HIV-1 protease. The lead peptide-inhibitor, 6a, with high potency and good binding profile can be used as the basis for developing potent small molecule inhibitors against MDR variants of HIV.

60 APPLIED LIFE SCIENCES↗

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data. Typically, models are evaluated on unseen classes and datasets that are defined by the same fundamental task as they are trained for (e.g. category membership). One can also ask how well a model can generalize to fundamentally different tasks within a fixed dataset (for example: moving from category membership to tasks that involve detecting object orientation or quantity). To formalize this kind of shift we define a notion of “independence of tasks” and identify three new sets of labels for established computer vision datasets that test a model's ability to generalize to tasks which draw on orthogonal attributes in the data. We use these datasets to investigate the failure modes of metric-based few-shot models. Based on our findings, we introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data. In particular, FSN models can not only form multiple representations for a given class but can also begin to capture the low-dimensional structure which characterizes class manifolds in the encoded space of deep networks. We show that FSN outperforms state-of-the-art models on the challenging tasks we introduce in this paper while remaining competitive on standard few-shot benchmarks.

deep learning↗

SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks

Modern neural networks (NNs) often do not generalize well in the presence of a ‘covariate shift’; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels given the data remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more robust, domain-invariant features. Domain adaptation (DA) methods include a broad range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SInkhorn Dynamic Domain Adaptation (SIDDA), an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, real astronomical observations, and remote sensing data. These datasets exhibit covariate shifts due to noise, blurring, differences between telescopes, and variations in imaging wavelengths. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with symmetry-aware equivariant NNs (ENNs). We find that SIDDA consistently enhances the generalization capabilities of NNs, achieving up to a ${\approx}40\%$ improvement in classification accuracy on unlabeled target data, while also providing a more modest performance gain of $\lesssim 1\%$ on labeled source data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, if SIDDA achieves proper domain alignment, it also enhances model calibration on both source and target data, with the most significant gains in the unlabeled target domain—achieving over an order of magnitude improvement in the expected calibration error and Brier score. SIDDA’s versatility across various NN models and datasets, combined with its automated approach to domain alignment, has the potential to significantly advance multi-dataset studies by enabling the development of highly generalizable models.

79 ASTRONOMY AND ASTROPHYSICS↗

Acetic Acid Reforming over Rh Supported on La2O3/CeO2-ZrO2: Catalytic Performance and Reaction Pathway Analysis

Reforming of acetic acid was investigated on Rh supported on CeO2-ZrO2 modified with 3 wt % La. The active catalyst converted acetic acid to H-2-rich gas and hardly formed coke. The low rate of coke formation is concluded to be related to the presence of redox-active oxygen limiting the concentration of coke precursors. Temperature-programmed O-18(2)) isotope exchange measurements showed that the La2O3 and Rh enhanced the mobility of lattice oxygen compared with that of the parent CeO2-ZrO2. Ketonization and decarboxylation of acetic acid are the dominating reactions over the latter up to 600 degrees C, whereas above 600 degrees C, steam reforming and water gas shift also contribute. Over 0.5 wt % Rh on La2O3/CeO2-ZrO2, reforming and water gas shift reactions dominate, even below 300 degrees C, producing mostly H-2 and CO2. Using isotope labeling, it is shown that acetic acid adsorbs dissociatively on Rh, forming acetates, which sequentially decarboxylate and form surface methyl groups. The latter are in turn converted to CO, CO2, and H-2.

acetic acid↗

Automated Registration of Vector Data to Overhead Imagery

The availability of open source, remote sensing-derived vector data has increased exponentially in recent years. Unfortunately, these vector data are rarely made available with the corresponding source images from which objects were extracted. As such, these derived datasets are commonly used in combination with target images which differ from the source. Satellite viewing geometry can cause objects, extracted from a source image, to appear shifted when overlaid with a target image. Whether for purely cartographic purposes, reusability of preexisting training labels, or any spatial analysis where spatial correspondence between vector and image are required, the following paragraphs outline a method to address this challenge through the automated registration of vector data to overhead imagery, including existing literature regarding this challenge, followed by a case study of Sioux Falls, South Dakota.

McKee, Jacob↗

Impact of methionine oxidation on calmodulin structural dynamics

Highlights: • We measured the distance distribution between two spin labels on calmodulin by DEER. • Two structural states, open and closed, were resolved at both low and high Ca. • Ca shifted the equilibrium toward the open state by a factor of 13. • Methionine oxidation, simulated by glutamine substitution, decreased the Ca effect. • These results have important implications for aging in muscle and other tissues. - Abstract: We have used electron paramagnetic resonance (EPR) to examine the structural impact of oxidizing specific methionine (M) side chains in calmodulin (CaM). It has been shown that oxidation of either M109 or M124 in CaM diminishes CaM regulation of the muscle calcium release channel, the ryanodine receptor (RyR), and that mutation of M to Q (glutamine) in either case produces functional effects identical to those of oxidation. Here we have used site-directed spin labeling and double electron–electron resonance (DEER), a pulsed EPR technique that measures distances between spin labels, to characterize the structural changes resulting from these mutations. Spin labels were attached to a pair of introduced cysteine residues, one in the C-lobe (T117C) and one in the N-lobe (T34C) of CaM, and DEER was used to determine the distribution of interspin distances. Ca binding induced a large increase in the mean distance, in concert with previous X-ray crystallography and NMR data, showing a closed structure in the absence of Ca and an open structure in the presence of Ca. DEER revealed additional information about CaM’s structural heterogeneity in solution: in both the presence and absence of Ca, CaM populates both structural states, one with probes separated by ∼4 nm (closed) and another at ∼6 nm (open). Ca shifts the structural equilibrium constant toward the open state by a factor of 13. DEER reveals the distribution of interprobe distances, showing that each of these states is itself partially disordered, with the width of each population ranging from 1 to 3 nm. Both mutations (M109Q and M124Q) decrease the effect of Ca on the structure of CaM, primarily by decreasing the closed-to-open equilibrium constant in the presence of Ca. We propose that Met oxidation alters CaM’s functional interaction with its target proteins by perturbing this Ca-dependent structural shift.

60 APPLIED LIFE SCIENCES↗

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗