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Single‐cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in‐depth characterization of individual cells by mass spectrometry (MS)‐based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow‐rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10‐fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS‐isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single‐cell proteomes to transcriptome data revealed a stable‐core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra‐high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease. Synopsis A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable. A highly optimized data independent acquisition powered single‐cell proteomics workflow including sub‐µl sample preparation, very low flow chromatography and trapped ion mobility mass spectrometry (diaPASEF) is presented. Single‐cell proteome analysis is performed by injecting cells one‐by‐one across the cell cycle into the LC‐MS and correctly identifies cell states. Single‐cell proteome information is highly complementary to single‐cell transcriptome information. At the single‐cell level the proteome is quantitatively and qualitatively stable, while the transcriptome is stochastic. Graphical Abstract A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable.

Low-flow low-gradient (LF-LG) aortic stenosis (AS) may occur with preserved or depressed left ventricular ejection fraction (LVEF). Both situations represent the most challenging subset of patients to manage and generally have a poor prognosis. Few and controversial data exist on the outcomes of these patients compared with normal flow-high gradient (NF-HG) AS after transcatheter aortic valve replacement (TAVR). We sought to characterize different transvalvular flow-gradient patterns and to examine their prognostic value after TAVR. We enrolled 1,208 patients with severe AS and categorized as follow: 976 patients NF-HG (mean aortic pressure gradient [MPG] ≥40 mm Hg), 107 paradoxical LF-LG (pLF-LG, MPG <40 mm Hg, LVEF ≥50%, stroke volume index <35 ml/m2), and 125 classical LF-LG (cLF-LG) (MPG <40 mm Hg, LVEF <50%, stroke volume index <35 ml/m2). When compared with NF-HG and pLF-LG, cLF-LG had a worse symptomatic status (New York Heart Association III to IV 86% vs 62% and 67%, p <0.001), a higher prevalence of eccentric hypertrophy and a higher level of LV global afterload reflected by a higher valvuloarterial impedance. Valvular function after TAVR was excellent over time in all patients. While 30-day mortality (p = 0.911) did not differ significantly among groups, cLF-LG had a lower 5-year survival rate (LF-LG 50% vs pLF-LG 62% and NF-HG 68%, p <0.05). cLF-LG was associated with a hazard ratio for mortality of 2.41 (95% confidence interval 1.65 to 3.52, p <0.001). In conclusion, TAVR is an effective procedure regardless of transvalvular flow-gradient patterns. However, special care should be given to characterized hemodynamic of AS, as patients with pLF-LG had similar survival rates than patients with NF-HG, whereas cLF-LG is associated with a twofold increased risk of mortality at 5-year follow-up.

Transcatheter aortic valve replacement (TAVR) is a standard treatment for severe aortic stenosis (AS), but outcomes vary based on flow state. Low-flow, low-gradient aortic stenosis (LFLG AS) is a heterogenous condition and growing evidence suggests that response to TAVR differs by subtype. However, the generalizability of these studies to U.S. populations remains uncertain. This single-center, US-based retrospective study compared mortality outcomes from TAVR versus conservative management strategies in patients with classical (cLFLG) and paradoxical (pLFLG) LFLG AS. Adults with severe LFLG AS (valve area ≤1.0 cm2, stroke volume index ≤35 mL/m2, and mean pressure gradient <40 mmHg) evaluated for TAVR between 2019 and 2021 were included. Patients were stratified by subtype (cLFLG: left ventricular ejection fraction [LVEF] <50%; pLFLG: LVEF ≥50%) and treatment strategy (TAVR or conservative management). Of 490 patients included (207 cLFLG, 283 pLFLG), 67% underwent TAVR. Median follow-up was 19 months. TAVR was associated with lower mortality than conservative management (adjusted hazard ratio [HR] 0.47; 95% CI 0.33 to 0.69; p <0.001). In cLFLG AS, TAVR significantly reduced mortality (adjusted HR 0.37; 95% CI 0.23 to 0.60; p <0.001). In pLFLG AS, a nonsignificant trend towards benefit was observed (adjusted HR 0.62; 95% CI 0.33 to 1.15; p = 0.127). Among patients managed conservatively, those with pLFLG AS had lower mortality than cLFLG AS (adjusted HR 0.50; 95% CI 0.25 to 0.99; p = 0.046). In conclusion, TAVR is associated with improved survival in LFLG AS, particularly in patients with cLFLG AS. Comparable outcomes in conservatively managed pLFLG AS patients support a more individualized, phenotype-driven treatment approach.

Paired‐catchment studies are widely used to examine the effects of land management practices (“treatments”) on hydrologic processes. Catchments are matched and a pretreatment calibration regression is used to identify the hydrological relationship between the reference and treated catchments. This method assumes that the calibration regression represents the actual relationship between the catchments (assumption of representativeness) and that the relationship will remain stable over time (assumption of stability). Errors are assumed to be small and similar between reference and treated catchments. Thus, observed differences between the catchments following treatment are assumed to result from that treatment alone. However, calibration periods are often short and it is impossible to know if the calibration period is representative. Further, because the study is unreplicated, it is impossible to determine if stability is maintained. Consequently, it is difficult to determine a minimum detectable effect sizes (MDES) below which estimates of changes in streamflow are statistically uncertain. Here, we use bootstrapped sampling from reference‐by‐reference (RxR) comparisons in a paired‐catchment study design to evaluate the MDES. We generate frequency distributions of the potential changes in flow—changes that cannot be caused by treatment effects. From these, we estimate bootstrapped ±95% confidence intervals encompassing the non‐treatment effects which we use as the MDES. We apply this method to long‐term paired‐catchment studies and reexamine changes in both annual water yields and late summer low flows at the HJA Experimental Forest. This bootstrapping method is widely transferable to any long‐term paired catchment study sites where multiple reference catchments exist.

•Classical low-flow low-gradient (C-LFLG) aortic stenosis (AS) was associated with worse outcomes after transcatheter aortic valve replacement (TAVR) compared with high-gradient AS.•Patients with normal-flow low-gradient AS had similar mortality at 1 year but higher mortality at 2 years after TAVR compared with those with high-gradient AS.•Multiple clinical characteristics, including tricuspid regurgitation and mitral regurgitation, correlated with C-LFLG AS.•Nontransfemoral access, anemia, and the degree of LV dysfunction, in addition to other clinical characteristics, correlated with higher mortality in patients with C-LFLG AS.•Ejection fraction and transvalvular flow improve after TAVR in patients with C-LFLG AS. This study sought to explore the clinical factors associated with classical low-flow low-gradient (C-LFLG) and normal-flow low-gradient (NFLG) aortic stenosis (AS) compared with high-gradient (HG) AS. We also compared clinical and echocardiographic outcomes after transcatheter aortic valve replacement (TAVR) across flow-gradient patterns. Patients with C-LFLG AS have a higher mortality rate after TAVR than those with HG AS. However, what leads to C-LFLG AS and the predictors of mortality in this population remain unclear. In this retrospective, single-center study involving 1,415 patients with severe AS, patients were classified as having (1) HG AS (aortic valve mean gradient [MG] >40 mm Hg), (2) C-LFLG AS (MG <40 mm Hg, stroke volume index <35 ml/m2, left ventricular ejection fraction <50%), and (3) NFLG AS (MG <40 mm Hg, stroke volume index ≥35 ml/m2, left ventricular ejection fraction ≥50%). Logistic regression was used for predictors of C-LFLG AS. Cox regression was used for predictors of mortality in the C-LFLG AS population. Male gender, multiple co-morbidities, and moderate to severe mitral and tricuspid regurgitation correlated with the C-LFLG AS group. Patients with C-LFLG AS had a higher mortality risk compared with patients with HG AS at 2 years after TAVR. Patients with NFLG AS had similar mortality at 1 year, but higher mortality at 2 years after TAVR compared with patients with HG AS. End-stage renal disease, atrial fibrillation, and other co-morbidities were predictors of 2-year mortality in patients with C-LFLG AS. In conclusion, the mortality rate after TAVR was higher among patients with C-LFLG AS than those with HG AS. Male gender and multiple co-morbidities were predictors of C-LFLG AS. Multiple co-morbidities were predictors of mortality among those patients.

Prognostic implications of pulmonary hypertension (PH) in low-flow low-gradient (LG) aortic stenosis (AS) after transcatheter aortic valve replacement (TAVR) remains unexplored. We aimed to investigate the impact of baseline and changes in PH after TAVR. In this single-center retrospective study, we included patients who underwent TAVR for low-flow LG AS. Patients were categorized into 2 groups: baseline pulmonary artery systolic pressure (PASP) <46 mm Hg (no-to-mild PH) and PASP ≥46 mm Hg (moderate-to-severe PH). On the basis of changes in PASP after TAVR, patients were stratified into increased (ΔPASP ≥ + 5 mm Hg), no change (−4 to +4 mm Hg), and decreased (≤ −5 mm Hg) groups. Primary end point was a composite of all-cause mortality and heart failure rehospitalization. In total, 210 patients were included, 148 in the no-to-mild PH group and 62 in the moderate-to-severe PH group. Median follow-up was 13.2 months. The moderate-to-severe PH group was at an increased risk of composite end point (adjusted hazard ratio [HR] 3.5, 95% confidence interval [CI] 1.8 to 6.9), all-cause mortality (HR 2.4, 95% CI 1.1 to 5.6), and heart failure rehospitalization (HR 8.3, 95% CI 2.9 to 23.7). There were no differences in clinical outcomes among those with increased (32%), no change (28%), and decreased (39%) PASP after TAVR. In conclusion, moderate-to-severe PH at baseline is an independent predictor of worse clinical outcomes in patients with low-flow LG AS who undergo TAVR, and this cohort of patients do not seem to derive the benefits of postoperative reduction of PASP.

Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning.

Low flows result from the interplay of climatic variability and catchment storage dynamics, but it is unclear which of these variables is more relevant for explaining low flow spatial patterns. Here, we develop a new conceptual model that integrates process‐based hydrological knowledge with statistics and test it for 1,400 Brazilian catchments. Through comparative hydrology, we isolate the low flow generating mechanisms and estimate their components using linear model trees. The model explains 58% of the spatial variance in 7‐day minimum annual flows (Qmin) based on climate and catchment characteristics. The primary Qmin controls depend on the spatial scale of analysis. Catchment characteristics govern Qmin up to the continental scale (107 km2), where their relative importance matches that of climate. At subcontinental scales, catchment characteristics are twice as important as climate in predicting Qmin, suggesting that low flows are governed by the catchment's capacity to attenuate the climatic variability through water storage. Geological properties are the most important catchment characteristics, particularly bedrock type, lithology and topographic slope, determining streamflow recession rates in the dry season. Soil properties, primarily soil class and depth, are half as important as geology. Climate impacts Qmin mainly through mean annual rainfall minus evaporation, representing the potential groundwater recharge, while dry‐season length has the lowest impact. These results hold mainly for highly seasonal and snow‐free climates. Low flow hydrology that combines statistics with process understanding offers a promising framework for understanding regional low flow generating mechanisms and could support other estimation models than that presented here. Plain Language Summary Water availability in the dry season depends mainly on a combination of water storage, rainfall and evaporation variability between years. However, the relative importance of catchment characteristics and climatic variability for the regional patterns of low flows remains unclear. In this study, we develop a new method that takes advantage of machine learning and process‐based hydrology to shed light on the main controls of low flows in Brazilian rivers. We find that the relative importance of climate and catchment characteristics in generating low flows depends on the spatial scale of analysis. At local and regional scales, catchment characteristics are twice as important as climate, governed mainly by geological properties. At continental scales, climate and catchment characteristics are equally important. The method proposed here offers a promising framework for understanding how low flows are generated and helps increase the efficiency of drought prevention measures by focusing on water processes on the land surface and subsurface. Key Points We use comparative hydrology to analyze the controls of spatial low flow variability in Brazil We propose a new conceptual model that integrates process‐based hydrological knowledge with statistics Geology related to flow recession controls spatial low flow variability regionally; geology and climate control it at continental scales

This study addresses significant prediction inaccuracies in aviation fuel centrifugal pumps under low-flow conditions, where strong rotational and separated flows dominate. A Very Large-Eddy Simulation-Reynolds-Averaged Navier–Stokes (VLES-RANS) hybrid modeling approach is used to evaluate the applicability of turbulence models across external performance, internal flow characteristics, and computational cost. Traditional Reynolds-Averaged Navier–Stokes (RANS) models, limited by time-averaging, fail to resolve secondary flows and vortex breakdown induced by intensified centrifugal forces under 0.2 Q d –0.4 Q d conditions. In contrast, the Very Large-Eddy Simulation-Shear Stress Transport k-ω (VLES-SST k-ω) model reduces the maximum head deviation from 2.76% to 0.32% and the efficiency deviation from 6.45% to 3.32% across 0.2 Q d –0.4 Q d flow rates, significantly improving small-flow performance prediction. For complex flow features, the hybrid model outperforms RANS in resolving entropy production, pressure distributions, and vortex structures, achieving 90.4% agreement in velocity components with Large-Eddy Simulation (LES) results. At 0.2 Q d and 0.4 Q d , pressure fluctuation amplitude and frequency match 80% of those from LES results, enabling precise quantification of irreversible losses and momentum transfer. In the RANS-VLES interface region, the hybrid models show good transition characteristics. Computationally, it reduces runtime by 30–50% compared to LES, with costs at low-flow rates decreasing by 55%—effectively balancing accuracy and efficiency effectively. By resolving large-scale vortices in the main flow while maintaining near-wall computational efficiency, the VLES-SST k-ω model overcomes RANS limitations. It represents an optimal choice for simulating high-Reynolds-number flows in complex rotating machinery, such as aviation fuel pumps, particularly under challenging low-flow operating conditions.

Low-flow events significantly impact water users and ecosystems due to reduced flow rates and deteriorating water quality. Elevated water temperatures during these periods have led to economic and ecological consequences. Therefore, water temperature is a key aspect in the context of low-flow risk analysis, and it is essential to model it accurately. This study introduces a one-dimensional water temperature model optimized for integration into low-flow risk analysis frameworks. Results demonstrate good performance in simulating water temperatures for both rivers, with Nash–Sutcliffe efficiency values of 0.85–0.98 and root mean square errors of 0.96–1.96 K. The model was evaluated on two contrasting river systems: the small Selke River and the large Elbe River. The model effectively captures anthropogenic influences and altered environmental conditions. Key factors influencing water temperature varied by river size, with tributaries and shading having more impact on smaller rivers, while air temperature was the primary driver for larger rivers. The model’s computational efficiency enables the practical implementation of long-term risk assessments. This temperature model fulfills the requirements for integration into low-flow risk management frameworks, providing a valuable tool for assessing temperature-related impacts and evaluating mitigation strategies across diverse river systems.