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Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr−1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr−1) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr−1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decad

Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simula

Shuaiwei Song,1– 5,* Xintian Cai,1– 5,* Junli Hu,1– 5 Qing Zhu,1– 5 Di Shen,1– 5 Huimin Ma,1– 5 Yingying Zhang,1– 5 Rui Ma,1– 5 Wenbo Yang,1– 5 Jing Hong,1– 5 Delian Zhang,1– 5 Nanfang Li1– 5 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, 830001, People’s Republic of China; 2Xinjiang Hypertension Institute, Urumqi, Xinjiang, 830001, People’s Republic of China; 3NHC Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang, 830001, People’s Republic of China; 4Key Laboratory of Xinjiang Uygur Autonomous Region ”Hypertension Research Laboratory”, Urumqi, Xinjiang, 830001, People’s Republic of China; 5Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, Xinjiang, 830001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Urumqi, Xinjiang, 830001, People’s Republic of China, Tel +86 8,564,818, Email lnanfang2016@sina.comObjective: While the role of aldosterone in bone metabolism is well established, the specific effects of the widely used aldosterone antagonist, spironolactone, on bone health are not fully understood. This study aimed to investigate the effects of spironolactone on osteoporosis and future fracture risk in middle-aged and elderly hypertensive patients, revealing its potential benefits for bone health.Methods: Propensity score matching was employed in this study to create matched groups of spironolactone users and non-users at a 1:4 ratio. We investigated the association between spironolactone use and the risk of osteoporosis using multivariate logistic regression analysis. Furthermore, we conducted multivariate linear regression analysis to explore the relationship between cumulative dosage and the FRAX score. Subgroup analysis was al

Jianwen Zhao,1– 5,* Xintian Cai,1– 5,* Junli Hu,1– 5 Shuaiwei Song,1– 5 Qing Zhu,1– 5 Di Shen,1– 5 Wenbo Yang,1– 5 Qin Luo,1– 5 Xiaoguang Yao,1– 5 Delian Zhang,1– 5 Jing Hong,1– 5 Nanfang Li1– 5 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, 830001, People’s Republic of China; 2Xinjiang Hypertension Institute, Urumqi, Xinjiang, 830001, People’s Republic of China; 3NHC Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang 830001 People’s Republic of China; 4Key Laboratory of Xinjiang Uygur Autonomous Region ”Hypertension Research Laboratory”, Urumqi, Xinjiang, 830001, People’s Republic of China; 5Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, Xinjiang, 830001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Urumuqi, Xinjiang, 830001, People’s Republic of China, Tel +86 8564818, Email lnanfang2016@sina.comBackground: A newly introduced obesity-related index, the weight-adjusted-waist index (WWI), emerges as a promising predictor of cardiovascular disease (CVD). Given the known synergistic effects of hypertension and obstructive sleep apnea (OSA) on cardiovascular risk, we aimed to explore the relationship between the WWI and CVD risk specifically within this high-risk cohort.Methods: A total of 2265 participants with hypertension and OSA were included in the study. Multivariate Cox regression analysis was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD events. The restricted cubic spline (RCS) was used to further evaluate the nonlinear dose-response relationship.Results: During a median follow-up period of 6.8 years, 324 participants experienced a CVD event. Multivariate Cox regression analysis revealed

Qing Zhu,1,2,* Yijia He,1,2,* Sijia Chen,1,2 Guoqiang Zhang1,2 1Department of Dermatology, The First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 2Hebei Provincial Innovation Center of Dermatology and Medical Cosmetology Technology, Shijiazhuang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guoqiang Zhang, Department of Dermatology, The First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China, Email 57702800@hebmu.edu.cnAbstract: Vitiligo has been reported to occur in association with lupus erythematosus (LE) and other autoimmune diseases. However, it remains unclear whether this association occurs because of shared immunopathogenesis. We hereby describe a case of discoid lupus erythematosus (DLE) in a 51-year-old man with a 3 years history of skin lesions on his face, arms, and the V zone of the neck, and with the coexistence of vitiligo for 12 years, who developed from DLE to hypertrophic discoid lupus erythematosus (HDLE) after 10 months. We reviewed the previously reported cases to summarize the clinical characteristics of these patients and hope it may provide a reference for dermatologists.Keywords: hypertrophic discoid lupus erythematosus, lupus erythematosus, vitiligo, association

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750-2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottomup (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr-1) in the past 4 decades (1980-2020). Direct agricultural emissions in 2020 (3.9 Tg N yr-1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr-1), and indirect anthropogenic sources (1.3 Tg N yr-1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower-upper bounds: 10.6-27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6-17.4) Tg N yr-1. For the 2010-2019 period, the annual BU decada

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40 % (or 1.9 Tg N yr-1) in the past four decades (1980–2020). Direct agricultural emissions in 2020, 3.9 Tg N yr−1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including ‘Fossil fuel and industry’, ‘Waste and wastewater’, and ‘Biomass burning’ (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5–27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr-1. For the period 2010–2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4–25.9) Tg N yr-1 and TD emissions were 17.4 (15.8–19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by

The Black Sea is a permanently anoxic, marine basin serving as model system for the deposition of organic-rich sediments in a highly stratified ocean. In such systems, archaeal lipids are widely used as paleoceanographic and biogeochemical proxies; however, the diverse planktonic and benthic sources as well as their potentially distinct diagenetic fate may complicate their application. To track the flux of archaeal lipids and to constrain their sources and turnover, we quantitatively examined the distributions and stable carbon isotopic compositions (delta 13C) of intact polar lipids (IPLs) and core lipids (CLs) from the upper oxic water column into the underlying sediments, reaching deposits from the last glacial. The distribution of IPLs responded more sensitively to the geochemical zonation than the CLs, with the latter being governed by the deposition from the chemocline. The isotopic composition of archaeal lipids indicates CLs and IPLs in the deep anoxic water column have negligible influence on the sedimentary pool. Archaeol substitutes tetraether lipids as the most abundant IPL in the deep anoxic water column and the lacustrine methanic zone. Its elevated IPL/CL ratios and negative delta 13C values indicate active methane metabolism. Sedimentary CL- and IPL-crenarchaeol were exclusively derived from the water column, as indicated by non-variable delta 13C values that are identical to those in the chemocline and by the low BIT (branched isoprenoid tetraether index). By contrast, in situ production accounts on average for 22% of the sedimentary IPL-GDGT-0 (glycerol dibiphytanyl glycerol tetraether) based on isotopic mass balance using the fermentation product lactate as an endmember for the dissolved substrate pool. Despite the structural similarity, glycosidic crenarchaeol appears to be more recalcitrant in comparison to its non-cycloalkylated counterpart GDGT-0, as indicated by its consistently higher IPL/CL ratio in sediments. The higher TEX86, CCaT, and GD

Wetland methane (CH4) emissions over the Boreal-Arctic region are vulnerable to climate change and linked to climate feedbacks, yet understanding of their long-term dynamics remains uncertain. Here, we upscaled and analysed two decades (2002-2021) of Boreal-Arctic wetland CH4 emissions, representing an unprecedented compilation of eddy covariance and chamber observations. We found a robust increasing trend of CH4 emissions (+8.9%) with strong inter-annual variability. The majority of emission increases occurred in early summer (June and July) and were mainly driven by warming (52.3%) and ecosystem productivity (40.7%). Moreover, a 2 °C temperature anomaly in 2016 led to the highest recorded annual CH4 emissions (22.3 Tg CH4 yr-1) over this region, driven primarily by high emissions over Western Siberian lowlands. However, current-generation models from the Global Carbon Project failed to capture the emission magnitude and trend, and may bias the estimates in future wetland CH4 emission driven by amplified Boreal-Arctic warming and greening.

Recent evidence of direct detection of stochastic gravitational waves reported by pulsar timing array collaborations might open a new window for studying cosmology and astrophysical phenomena. In addition to signals from gravitational waves, there is motivation to explore residual signals from oscillating dark matter, which might partially comprise the galactic halo. We investigate fluctuations in pulsar timing originating from the coherent oscillation of scalar dark matter up to the subleading order of $\mathcal{O}(k/m)$, as well as from acoustic oscillations of non-adiabatic perfect fluid dark matter. Both types of dark matter can generate oscillating Newtonian potential perturbations and curvature perturbations, thereby affecting pulsar timing. Our results show distinctive signatures in pulsar timing residuals and angular correlations for these dark matters. Specifically, pulsar timing residuals from non-adiabatic perfect fluid dark matter exhibit different directional dependence and are shown to be more sensitive to the distance to a pulsar. We also study the angular correlation patterns from these dark matters in the NANOGrav 15-year data set. The best fit might suggest that the composition of non-adiabatic perfect fluid dark matter in our galaxy is much greater than that of ultralight scalar dark matter., Comment: 20 pages, 10 figures

Recent pulsar timing array collaborations have reported evidence of the stochastic gravitational wave background. The gravitational waves induced by primordial curvature perturbations, referred to as scalar-induced gravitational waves (SIGWs), could potentially be the physical origins of this gravitational wave background. In addition to the statistical properties of SIGWs derived from the primordial fluctuations, SIGWs also have intrinsic non-Gaussianity originating from the non-linear interactions of Einstein's gravity, which, however, is rarely explored. In this paper, we study the intrinsic non-Gaussianity of SIGWs with the bispectrum and the skewness under the assumption of the Gaussian primordial curvature perturbations, phenomenologically modeled as a lognormal spectrum. The bispectrum is shown to be vanishing in the collinear limit, which is independent of the initial conditions and the dynamics of SIGWs. For the SIGWs generated during the radiation-dominated era, the bispectrum is of flatten-type non-Gaussianity, with only four polarization components left to be non-vanishing. Additionally, in order to obtain the correct bispectrum, we also propose a time-oscillation average scheme and a regularization scheme. Utilizing the skewness for quantifying the degree of non-Gaussianity, it is found that the curvature power spectrum with a narrow width can result in an enhancement of the third-order non-Gaussianity. The conclusion holds for both the SIGWs generated in the radiation-dominated era and the matter-dominated era., Comment: 31 pages, 11 figures

Starting from more than 11,200 short-period (less than 0.5 days) EW-type eclipsing binary candidates with the All-Sky Automated Survey for Supernovae (ASAS-SN) V-band light curves, we use MCMC and neural networks (NNs) to obtain the mass ratio ($q$), orbital inclination ($incl$), fill-out factor ($f$) and temperature ratio ($T_s/T_p$). After cross-matching with the Gaia DR3 database, the final sample contains parameters of 2,399 A-type and 8,712 W-type contact binaries (CBs). We present the distributions of parameters of these 11,111 short-period CBs. The mass ratio ($q$) and fill-out factor ($f$) are found to obey log-normal distributions, and the remaining parameters obey normal distributions. There is a significant period-temperature correlation of these CBs. Additionally, the temperature ratio (${T_s}$/${T_p}$) tends to increase as the orbital period decreases for W-type CBs. There is no significant correlation between them for A-type CBs. The mass ratio and fill-out factor ($q-f$) diagram suggest there is no significant correlation between these two parameters. A clear correlation exists between the mass ratio and radius ratio. The radius ratio increases with the mass ratio. Moreover, the deep fill-out CBs tend to fall on the upper boundary of the $q$$-$${R_s}$/${R_p}$ distribution, while the shallow fill-out CBs fall on the lower boundary., Comment: 15 pages, 8 figures, 1 table.Accepted for publication in ApJS. The full dataset will be available with the ApJS article

Six new species of the genus Rhaphidophora Serville, 1838 are described from China: R. hexagoniproctalis Wang, Di & He sp. nov., R. heterodentis Shen, Wang & He sp. nov., R. imbricofurca Shen, Wang & He sp. nov., R. glenoides Qin, Wang & He sp. nov., R. impressa Wang, Qin & He sp. nov., R. stenoterminata Zhang, Wang & He sp. nov. A key with previously described species from China is provided.

3D object detection is a fundamental task in scene understanding. Numerous research efforts have been dedicated to better incorporate Hough voting into the 3D object detection pipeline. However, due to the noisy, cluttered, and partial nature of real 3D scans, existing voting-based methods often receive votes from the partial surfaces of individual objects together with severe noises, leading to sub-optimal detection performance. In this work, we focus on the distributional properties of point clouds and formulate the voting process as generating new points in the high-density region of the distribution of object centers. To achieve this, we propose a new method to move random 3D points toward the high-density region of the distribution by estimating the score function of the distribution with a noise conditioned score network. Specifically, we first generate a set of object center proposals to coarsely identify the high-density region of the object center distribution. To estimate the score function, we perturb the generated object center proposals by adding normalized Gaussian noise, and then jointly estimate the score function of all perturbed distributions. Finally, we generate new votes by moving random 3D points to the high-density region of the object center distribution according to the estimated score function. Extensive experiments on two large scale indoor 3D scene datasets, SUN RGB-D and ScanNet V2, demonstrate the superiority of our proposed method. The code will be released at https://github.com/HHrEtvP/DiffVote.

The Black Sea is a permanently anoxic, marine basin serving as model system for the deposition of organic-rich sediments in a highly stratified ocean. In such systems, archaeal lipids are widely used as paleoceanographic and biogeochemical proxies; however, the diverse planktonic and benthic sources as well as their potentially distinct diagenetic fate may complicate their application. To track the flux of archaeal lipids and to constrain their sources and turnover, we quantitatively examined the distributions and stable carbon isotopic compositions (delta 13C) of intact polar lipids (IPLs) and core lipids (CLs) from the upper oxic water column into the underlying sediments, reaching deposits from the last glacial. The distribution of IPLs responded more sensitively to the geochemical zonation than the CLs, with the latter being governed by the deposition from the chemocline. The isotopic composition of archaeal lipids indicates CLs and IPLs in the deep anoxic water column have negligible influence on the sedimentary pool. Archaeol substitutes tetraether lipids as the most abundant IPL in the deep anoxic water column and the lacustrine methanic zone. Its elevated IPL/CL ratios and negative delta 13C values indicate active methane metabolism. Sedimentary CL- and IPL-crenarchaeol were exclusively derived from the water column, as indicated by non-variable delta 13C values that are identical to those in the chemocline and by the low BIT (branched isoprenoid tetraether index). By contrast, in situ production accounts on average for 22% of the sedimentary IPL-GDGT-0 (glycerol dibiphytanyl glycerol tetraether) based on isotopic mass balance using the fermentation product lactate as an endmember for the dissolved substrate pool. Despite the structural similarity, glycosidic crenarchaeol appears to be more recalcitrant in comparison to its non-cycloalkylated counterpart GDGT-0, as indicated by its consistently higher IPL/CL ratio in sediments. The higher TEX86, CCaT, and GD

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model's accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.

Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (<15 days); (b) most of the models can capture the CH4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH4 production). Our evaluation suggests the need to accurately replicate FCH4 variability, especially at short time scales, in future wetland CH4 model developments.

African and South American (ASA) wildfires account for more than 70 % of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire–climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire–climate relationships in historical and future predictions.

Wenbo Yang,1,&ast; Xintian Cai,1,2,&ast; Junli Hu,1 Wen Wen,1 Heizhati Mulalibieke,1 Xiaoguang Yao,1 Ling Yao,1 Qing Zhu,1 Jing Hong,1 Qin Luo,1 Shasha Liu,1 Nanfang Li1 1Hypertension Center, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Medical Research Center for Hypertension Diseases, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, People’s Republic of China; 2Graduate School, Xinjiang Medical University, Urumqi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Medical Research Center for Hypertension Diseases, People’s Hospital of Xinjiang Uygur Autonomous Region, NO. 91 TianChi Road, Urumqi, 830001, People’s Republic of China, Tel +86 8564818, Email lnanfang2016@sina.comObjective: We aimed to evaluate the METS-IR (metabolic score for insulin resistance) index for the prediction of incident cardiovascular disease (CVD) and its subtypes (coronary artery disease and stroke) in patients with hypertension and obstructive sleep apnea (OSA).Methods: A retrospective cohort study was conducted with 2031 adults with hypertension and OSA, participants from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH). The hazard ratios and 95% CIs (credibility interval) for CVD and its subtypes were estimated using multivariate Cox proportional hazards regression models.Results: After a median follow-up of 6.80 years (interquartile range: 5.90– 8.00 years), a total of 317 (15.61%) participants developed new-onset CVD, including 198 (9.75%) incident coronary heart disease (CHD) and 119 (5.86%) incident stroke. After adjusting for as many relevant confounding factors as possible, each SD incr

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (Vcmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year-1 while enhanced photosynthesis by 6.9-8.2 PgC year-1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year-1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosp

BackgroundDevelopment of safe and effective SARS-CoV-2 therapeutics is a high priority. Amubarvimab and romlusevimab are noncompeting anti-SARS-CoV-2 monoclonal antibodies with an extended half-life.ObjectiveTo assess the safety and efficacy of amubarvimab plus romlusevimab.DesignRandomized, placebo-controlled, phase 2 and 3 platform trial. (ClinicalTrials.gov: NCT04518410).SettingNonhospitalized patients with COVID-19 in the United States, Brazil, South Africa, Mexico, Argentina, and the Philippines.PatientsAdults within 10 days onset of symptomatic SARS-CoV-2 infection who are at high risk for clinical progression.InterventionCombination of monoclonal antibodies amubarvimab plus romlusevimab or placebo.MeasurementsNasopharyngeal and anterior nasal swabs for SARS-CoV-2, COVID-19 symptoms, safety, and progression to hospitalization or death.ResultsEight-hundred and seven participants who initiated the study intervention were included in the phase 3 analysis. Median age was 49 years (quartiles, 39 to 58); 51% were female, 18% were Black, and 50% were Hispanic or Latino. Median time from symptom onset at study entry was 6 days (quartiles, 4 to 7). Hospitalizations and/or death occurred in 9 (2.3%) participants in the amubarvimab plus romlusevimab group compared with 44 (10.7%) in the placebo group, with an estimated 79% reduction in events (P < 0.001). This reduction was similar between participants with 5 or less and more than 5 days of symptoms at study entry. Grade 3 or higher treatment-emergent adverse events through day 28 were seen less frequently among participants randomly assigned to amubarvimab plus romlusevimab (7.3%) than placebo (16.1%) (P < 0.001), with no severe infusion reactions or drug-related serious adverse events.LimitationThe study population was mostly unvaccinated against COVID-19 and enrolled before the spread of Omicron variants and subvariants.ConclusionAmubarvimab plus romlusevimab was safe and significantly reduce

The recent rise in atmospheric methane (CH₄) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH₄ source, estimates of global wetland CH₄ emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH₄ emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH₄ emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH₄ emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH₄ year⁻¹) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH₄ models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Xintian Cai,1,&ast; Shuaiwei Song,1,&ast; Junli Hu,2 Lei Wang,2 Di Shen,1 Qing Zhu,2 Wenbo Yang,2 Qin Luo,2 Jing Hong,2 Nanfang Li2 1Graduate School, Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 2Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, Urumqi, Xinjiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, NHC Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Hypertension Research Laboratory, Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, No. 91 Tianchi Road, Urumqi, Xinjiang, 830001, People’s Republic of China, Tel +86 8564818, Email lnanfang2016@sina.comObjective: This study aimed to evaluate the relationship between the systemic inflammation response index (SIRI) and the risk of stroke and its subtypes in elderly patients with hypertension and to explore its predictive accuracy and any potential effect modifiers.Methods: The study included 4749 participants with no history of stroke at baseline. Cox regression was used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CIs). Interaction tests and subgroup analyses were conducted. The predictive performance of various inflammatory indicators for stroke was compared using the area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).Results: During a median follow-up period of 3.2 years, 640 strokes were recorded, of which

Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15N-labelled inorganic N over five years following 15N addition at the permafrost table far below the main root zone. The 15N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau.

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40 % (or 1.9 Tg N yr-1) in the past four decades (1980–2020). Direct agricultural emissions in 2020, 3.9 Tg N yr−1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including ‘Fossil fuel and industry’, ‘Waste and wastewater’, and ‘Biomass burning’ (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5–27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr-1. For the period 2010–2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4–25.9) Tg N yr-1 and TD emissions were 17.4 (15.8–19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40 % (or 1.9 Tg N yr-1) in the past four decades (1980–2020). Direct agricultural emissions in 2020, 3.9 Tg N yr−1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including ‘Fossil fuel and industry’, ‘Waste and wastewater’, and ‘Biomass burning’ (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5–27.0) Tg N yr-1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr-1. For the period 2010–2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4–25.9) Tg N yr-1 and TD emissions were 17.4 (15.8–19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by

Strong evidence for a gravitational-wave background (GWB) has been reported in the nano-Hertz band. Interpreting the origin of this background to be scalar-induced gravitational waves (SIGWs), we explore the equation of state (EoS) of the early universe by performing Bayes parameter inferences across the big-bang nucleosynthesis (BBN), cosmic microwave background (CMB), and pulsar timing array (PTA) joint observations for the first time. Assuming a monochromatic power spectrum for primordial curvature perturbations, we obtain the spectral amplitude $A\sim10^{-3}-10^{-1}$ and spectral peak frequency $f_\ast\sim10^{-7}-10^{-6}$ Hz. We find that the radiation domination with EoS $w=1/3$ is compatible with the current observational data, the kination domination with EoS $w=1$ is not forbidden, while the early matter domination with EoS $w=0$ is excluded at more than $2\sigma$ confidence level. These results can be tested with future observations., Comment: 13 pages, 3 figures

We investigate the second order energy density perturbation $\delta^{(2)}$ induced by small-scale Gaussian and local-type non-Gaussian primordial curvature perturbations. The relative abundance of primordial black hole is calculated in terms of the probability density function of total energy density perturbation $\delta_r=\delta^{(1)}+\frac{1}{2}\delta^{(2)}$. The effects of second order density perturbation greatly reduce the upper bounds of small-scale power spectra of primordial curvature perturbations by one to two orders of magnitude. For log-normal primordial power spectrum, its amplitude $A_{\zeta}$ is constrained to be about $A_{\zeta}\sim 3\times10^{-3}$. And for local-type non-Gaussianity with $f_{\mathrm{NL}}=10$, the upper bound of $A_{\zeta}$ is about $2.5\times10^{-4}$., Comment: 5 pages, 4 figures

Multiple pulsar-timing-array collaborations have reported strong evidence for the existence of a gravitational-wave background. We study physical implications of this signal for cosmology, assuming that it is attributed to scalar-induced gravitational waves. By incorporating primordial non-Gaussianity $f_{\mathrm{NL}}$, we specifically examine the nature of primordial curvature perturbations and primordial black holes. We find that the signal allows for a primordial non-Gaussianity $f_{\mathrm{NL}}$ in the range of $-4.1\lesssim f_{\mathrm{NL}} \lesssim 4.1$ (68\% confidence intervals) and a mass range for primordial black holes $m_{\mathrm{pbh}}$ spanning from $\sim10^{-5}M_{\odot}$ to $\sim10^{-2}M_{\odot}$. Furthermore, we find that the signal favors a negative non-Gaussianity, which can suppress the abundance of primordial black holes. We also demonstrate that the anisotropies of scalar-induced gravitational waves serve as a powerful tool to probe the non-Gaussianity $f_{\mathrm{NL}}$. We conduct a comprehensive analysis of the angular power spectrum within the nano-Hertz band. Looking ahead, we anticipate that future projects, such as the Square Kilometre Array, will have the potential to measure these anisotropies and provide further insights into the primordial universe., Comment: Major revisions, 2 columns, 8 pages, 4 figures, 1 appendix

Recently, strong evidence for a gravitational wave background has been reported by collaborations of pulsar timing arrays (PTA). In the framework of scalar-induced gravitational waves (SIGWs), we concurrently investigate the second and third order gravitational waves by jointly analyzing PTA data, alongside big-bang nucleosynthesis (BBN), and cosmic microwave background (CMB) datasets. We determine the primordial curvature spectral amplitude as $0.021

Massive stars are formed in molecular clouds, and produce H II regions when they evolve onto the main sequence. The expansion of H II region can both suppress and promote star formation in the vicinity. M17 H II region is a giant cometary H II region near many massive clumps containing starless and protostellar sources. It is an appropriate target to study the effect of feedback from previously formed massive stars on the nearby star-forming environments. Observations of SiO 2-1, HCO$^+$ 1-0, H$^{13}$CO$^+$ 1-0, HC$_3$N 10-9, and H41$\alpha$ lines are performed toward M17 H II region with ambient candidates of massive clumps. In the observations, the widespread shocked gas surrounding M17 H II region is detected: it probably originates from the collision between the expanding ionized gas and the ambient neutral medium. Some massive clumps are found in the overlap region of the shock and dense-gas tracing lines while the central velocities of shocked and high-density gases are similar. This suggests that part of massive clumps are located in the shell of H II region, and may be formed from the accumulated neutral materials in the shell. In addition, by comparing the observations toward M17 H II region with the simulation of cometary H II region, we infer the presence of one or more massive stars travelling at supersonic velocity with respect to the natal molecular cloud in the H II region., Comment: 13 pages, 11 figures

Shocks are abundant in star-forming regions, and are often related with star formation. In our previous observations toward 100 starless clump candidates (SCCs) in the Galaxy, a sample of 34 SCCs associated with shocks is identified. In this work, we perform mapping observations of the SiO 2-1, 3-2, HC$_3$N 10-9, HCO$^+$ 1-0, H$^{13}$CO$^+$ 1-0, and H41$\alpha$ lines toward 9 out of the detected sources by using IRAM 30-m radio telescope to study the origins of the shocks in the SCCs. We find shocks in three sources (BGPS 3110, 3114, and 3118) are produced by collisions between the expanding ionized gas and ambient molecular gas, instead of by the star formation activity inside SCCs. On the other hand, shocks in the other six sources are related to star formation activity of SCCs. The signatures of protostellar outflows are clearly shown in the molecular lines toward BGPS 4029, 4472, 5064. Comparing our results with the previous ALMA observations performed in the same region, the shocks in BGPS 3686 and 5114 are also likely to be due to protostellar activity. The origin of shock in BGPS 5243 is still unclear although some features in the SiO spectra imply the presence of protostellar activity., Comment: 21 pages, 21 figues

Lulu Xv,&ast; Bin Wang,&ast; Qing Zhu, Guoqiang Zhang&ast; The First Hospital of Hebei Medical University, Shijiazhuang, China&ast;These authors contributed equally to this workCorrespondence: Guoqiang Zhang, No. 89, DongGang Road, Shijiazhuang, Hebei Province, People’s Republic of China, Tel +8618633888122, Email zlx090702@163.comAbstract: This is a 58-year-old woman who has had itching on her right back for 10 days; a month ago she developed blisters on her right back with no apparent cause and pain, and was diagnosed with “herpes zoster”. Erythema and blisters with itching reappeared 10 days ago on the right side of the back, where the original blisters had subsided. The histopathological manifestations of the lesions were: mild hyperkeratosis of the epidermis, pustules within the stratum corneum, irregular proliferation of the spinous layer, spongiosis, local destruction of the basal layer, infiltration of eosinophils, neutrophils, histiocytes and scattered plasma cells in the dermis and subcutis. According to the medical history, clinical manifestations and histopathology of the lesions, the diagnosis was Wolf’s isotopic response after herpes zoster.Keywords: Wolf’s isotopic response, herpes zoster, eosinophilic pustular folliculitis

Junli Hu,1– 5,&ast; Xintian Cai,1– 6,&ast; Qing Zhu,1– 6 Mulalibieke Heizhati,1– 5 Wen Wen,1– 6 Qin Luo,1– 5 Jing Hong,1– 5 Yujie Dang,1– 6 Wenbo Yang,1– 5 Nanfang Li1– 5 1Hypertension Center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China; 2Xinjiang Hypertension Institute, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China; 3NHC Key Laboratory of Hypertension Clinical Research, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China; 4Key Laboratory of Xinjiang Uygur Autonomous Region, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China; 5Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of China; 6Graduate School, Xinjiang Medical University, Urumqi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Nanfang Li, Hypertension Center, People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Urumuqi, Xinjiang, 830001, People’s Republic of China, Tel +86 8564818, Email lnanfang2016@sina.comObjective: To investigate the association between plasma aldosterone concentration (PAC) and non-alcoholic fatty liver disease (NAFLD) diagnosis in Chinese hypertensive patients.Methods: We conducted a retrospective study of all patients diagnosed with hypertension between January 1, 2010, and December 31, 2021. We included 3713 hypertensive patients based on the criteria for inclusion and exclusion. PAC measurement was performed using a radioimmunoassay. NAFLD was diagnosed using abdominal ultrasonography. Cox regression analysis was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for univariable and multivariable

Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15N-labelled inorganic N over five years following 15N addition at the permafrost table far below the main root zone. The 15N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau., Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15N-labelled inorganic N over five years following 15N addition at the permafrost table far below the main root zone. The 15N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau.

Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15N-labelled inorganic N over five years following 15N addition at the permafrost table far below the main root zone. The 15N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau., Permafrost-affected ecosystems are subject to warming and thawing, which can increase the availability of subsurface nitrogen (N) with consequences in otherwise N-limited tundra and alpine vegetation. Here, we quantify the extent of warming and permafrost thawing and the corresponding effects on nitrogen availability and plant growth based on a 20-year survey across 14 sites on the Tibetan Plateau. The survey showed that most sites have been subject to warming and thawing and that the upper permafrost zone across all sites was rich in inorganic N, mainly as ammonium. We further explore the efficiency of plants to utilize 15N-labelled inorganic N over five years following 15N addition at the permafrost table far below the main root zone. The 15N experiment showed that deep-rooted plant species were able to utilize the labelled N. A SEM model suggests that changes in vegetation can be explained by both active layer warming and permafrost thawing and the associated changes in inorganic nitrogen availability. Our results highlight a feedback mechanism of climate warming, in which released plant-available N may favour deep-rooted plants. This can explain important changes in plant composition and growth across the sites on the Tibetan Plateau.

The lack of fa\c{c}ade structures in photogrammetric mesh models renders them inadequate for meeting the demands of intricate applications. Moreover, these mesh models exhibit irregular surfaces with considerable geometric noise and texture quality imperfections, making the restoration of structures challenging. To address these shortcomings, we present StructuredMesh, a novel approach for reconstructing fa\c{c}ade structures conforming to the regularity of buildings within photogrammetric mesh models. Our method involves capturing multi-view color and depth images of the building model using a virtual camera and employing a deep learning object detection pipeline to semi-automatically extract the bounding boxes of fa\c{c}ade components such as windows, doors, and balconies from the color image. We then utilize the depth image to remap these boxes into 3D space, generating an initial fa\c{c}ade layout. Leveraging architectural knowledge, we apply binary integer programming (BIP) to optimize the 3D layout's structure, encompassing the positions, orientations, and sizes of all components. The refined layout subsequently informs fa\c{c}ade modeling through instance replacement. We conducted experiments utilizing building mesh models from three distinct datasets, demonstrating the adaptability, robustness, and noise resistance of our proposed methodology. Furthermore, our 3D layout evaluation metrics reveal that the optimized layout enhances precision, recall, and F-score by 6.5%, 4.5%, and 5.5%, respectively, in comparison to the initial layout., Comment: 30 pages,15 figures

Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However, the architecture of most existing approaches for change extraction attempts to learn features related to changes but ignores objectives related to buildings. This inevitably leads to the generation of significant pseudo-changes, due to factors such as seasonal changes in images and the inclination of building fa\c{c}ades. To alleviate the above-mentioned problems, we developed a contrastive learning approach by validating historical building footprints against single up-to-date remotely sensed images. This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings. In addition, to reduce the effects of inconsistencies between historical building polygons and buildings in up-to-date images, we employed a deformable convolutional neural network to learn offsets intuitively. In summary, we formulated a multi-branch building extraction method that identifies newly constructed and removed buildings, respectively. To validate our method, we conducted comparative experiments using the public Wuhan University building change detection dataset and a more practical dataset named SI-BU that we established. Our method achieved F1 scores of 93.99% and 70.74% on the above datasets, respectively. Moreover, when the data of the public dataset were divided in the same manner as in previous related studies, our method achieved an F1 score of 94.63%, which surpasses that of the state-of-the-art method.

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. Drawing inspiration from object-based classification techniques, we first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects, while simultaneously producing semantic segmentation of the buildings. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. Utilizing minimal clicks and straightforward strokes, we efficiently accomplish accurate segmentation outcomes, eliminating the necessity for editing polygon vertices. Our proposed approach demonstrates superior precision and efficacy, as validated by experimental assessments on various public benchmark datasets. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results.

The accurate representation of 3D building models in urban environments is significantly hindered by challenges such as texture occlusion, blurring, and missing details, which are difficult to mitigate through standard photogrammetric texture mapping pipelines. Current image completion methods often struggle to produce structured results and effectively handle the intricate nature of highly-structured fa\c{c}ade textures with diverse architectural styles. Furthermore, existing image synthesis methods encounter difficulties in preserving high-frequency details and artificial regular structures, which are essential for achieving realistic fa\c{c}ade texture synthesis. To address these challenges, we introduce a novel approach for synthesizing fa\c{c}ade texture images that authentically reflect the architectural style from a structured label map, guided by a ground-truth fa\c{c}ade image. In order to preserve fine details and regular structures, we propose a regularity-aware multi-domain method that capitalizes on frequency information and corner maps. We also incorporate SEAN blocks into our generator to enable versatile style transfer. To generate plausible structured images without undesirable regions, we employ image completion techniques to remove occlusions according to semantics prior to image inference. Our proposed method is also capable of synthesizing texture images with specific styles for fa\c{c}ades that lack pre-existing textures, using manually annotated labels. Experimental results on publicly available fa\c{c}ade image and 3D model datasets demonstrate that our method yields superior results and effectively addresses issues associated with flawed textures. The code and datasets will be made publicly available for further research and development., Comment: 31 pages, 16 figures

Expected to be of the highest survey power telescope in the northern hemisphere, the Wide Field Survey Telescope (WFST) will begin its routine observations of the northern sky since 2023. WFST will produce a lot of scientific data to support the researches of time-domain astronomy, asteroids and the solar system, galaxy formation and cosmology and so on. We estimated that the 5 $\sigma$ limiting magnitudes of WFST with 30 second exposure are $u=22.31$ mag, $g=23.42$ mag, $r=22.95$ mag, $i=22.43$ mag, $z=21.50$ mag, $w=23.61$ mag. The above values are calculated for the conditions of $airmass=1.2$, seeing = 0.75 arcsec, precipitable water vapour (PWV) = 2.5 mm and Moon-object separation = $45^{\circ}$ at the darkest New Moon night of the Lenghu site (V=22.30 mag, Moon phase $\theta=0^{\circ}$). The limiting magnitudes in different Moon phase conditions are also calculated. The calculations are based on the empirical transmittance data of WFST optics, the vendor provided CCD quantum efficiency, the atmospherical model transmittance and spectrum of the site. In the absence of measurement data such as sky transmittance and spectrum, we use model data., Comment: Figure 5 (a,b,c) has been updated in this latest version. 12 pages, 5 figures, published in RAA (Research in Astronomy and Astrophysics)

The images of supermassive black holes in M87 and our galaxy captured by the Event Horizon Telescope (EHT) might open up a new way for exploring black hole physics at the horizon scale. Theoretically, this could provide insights into studying the emission sources around a black hole or the geometries of the black hole itself. This paper investigates the two-point correlations of intensity fluctuations on the photon ring, resulting from the existence of shock waves or gravitational fluctuations around a black hole. Following approaches used in the field of gravitational wave detectors, we introduce response functions of very long baseline interferometry (VLBI) for detecting gravitational fluctuations and study the shape of the overlap reduction functions. It is found that the shape of the correlations here differs from that resulting from stochastic emission sources. By providing an order-of-magnitude estimate for the signal-to-noise ratio (SNR), we obtain sensitivity curves for the detection of gravitational fluctuations. This might reveal the potential for detecting gravitational fluctuations with future VLBI observations in the LIGO frequency band., Comment: v2: accepted version, 24 pages, 11 figures, 11pt

We analyze the energy density spectrum of \acp{SIGW} using the NANOGrav 15-year data set, thereby constraining the primordial non-Gaussian parameter $f_{\mathrm{NL}}$. For the first time, we calculate the seventeen missing two-loop diagrams proportional to $f_{\mathrm{NL}}A_{\zeta}^3$ that correspond to the two-point correlation function $\langle h^{\lambda,(3)}_{\mathbf{k}} h^{\lambda',(2)}_{\mathbf{k}'} \rangle$ for local-type primordial non-Gaussianity. The total energy density spectrum of \acp{SIGW} can be significantly suppressed by these two-loop diagrams. If \acp{SIGW} dominate the \acp{SGWB} observed in \ac{PTA} experiments, the parameter interval $f_{\mathrm{NL}}\in [-5,-1]$ is notably excluded based on NANOGrav 15-year data set. After taking into account abundance of \acp{PBH} and the convergence of the cosmological perturbation expansion, we find that the only possible parameter range for $f_{\mathrm{NL}}$ might be $-1\le f_{\mathrm{NL}}< 0$., Comment: 8 pages, 4 figures

For a long time, due to the high heterogeneity in structure and semantics among various spatiotemporal modal data, the joint interpretation of multimodal spatiotemporal data has been an extremely challenging problem. The primary challenge resides in striking a trade-off between the cohesion and autonomy of diverse modalities, and this trade-off exhibits a progressively nonlinear nature as the number of modalities expands. We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities. We propose a multimodal spatiotemporal general artificial intelligence model, called AllSpark. Our model integrates thirteen different modalities into a unified framework, including 1D (text, code), 2D (RGB, infrared, SAR, multispectral, hyperspectral, tables, graphs, trajectory, oblique photography), and 3D (point clouds, videos) modalities. To achieve modal cohesion, AllSpark uniformly maps diverse modal features to the language modality. In addition, we design modality-specific prompts to guide multi-modal large language models in accurately perceiving multimodal data. To maintain modality autonomy, AllSpark introduces modality-specific encoders to extract the tokens of various spatiotemporal modalities. And modal bridge is employed to achieve dimensional projection from each modality to the language modality. Finally, observing a gap between the model's interpretation and downstream tasks, we designed task heads to enhance the model's generalization capability on specific downstream tasks. Experiments indicate that AllSpark achieves competitive accuracy in modalities such as RGB and trajectory compared to state-of-the-art models., Comment: 49 pages, 16 tables, 3 figures

Methane (CH4) is the second most critical greenhouse gas after carbon dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. ML enables the computational time to be shortened significantly from 6 CPU hours to 0.72 milliseconds, achieving reduced computational costs. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques like Bayesian optimization., Comment: 24 pages, 9 figures and 2 tables

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases., Comment: 11 pages,3 figures

Landslide susceptibility assessment (LSA) is of paramount importance in mitigating landslide risks. Recently, there has been a surge in the utilization of data-driven methods for predicting landslide susceptibility due to the growing availability of aerial and satellite data. Nonetheless, the rapid oscillations within the landslide-inducing environment (LIE), primarily due to significant changes in external triggers such as rainfall, pose difficulties for contemporary data-driven LSA methodologies to accommodate LIEs over diverse timespans. This study presents dynamic landslide susceptibility mapping that simply employs multiple predictive models for annual LSA. In practice, this will inevitably encounter small sample problems due to the limited number of landslide samples in certain years. Another concern arises owing to the majority of the existing LSA approaches train black-box models to fit distinct datasets, yet often failing in generalization and providing comprehensive explanations concerning the interactions between input features and predictions. Accordingly, we proposed to meta-learn representations with fast adaptation ability using a few samples and gradient updates; and apply SHAP for each model interpretation and landslide feature permutation. Additionally, we applied MT-InSAR for LSA result enhancement and validation. The chosen study area is Lantau Island, Hong Kong, where we conducted a comprehensive dynamic LSA spanning from 1992 to 2019. The model interpretation results demonstrate that the primary factors responsible for triggering landslides in Lantau Island are terrain slope and extreme rainfall. The results also indicate that the variation in landslide causes can be primarily attributed to extreme rainfall events, which result from global climate change, and the implementation of the Landslip Prevention and Mitigation Programme (LPMitP) by the Hong Kong government.

Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details. Existing methods in the field of clothing animation are limited to either static behavior or specific network models for individual garments, which hinders their applicability in real-world scenarios where diverse animated garments are required. Our proposed method overcomes these limitations by providing a unified framework that predicts dynamic behavior for different garments with arbitrary topology and looseness, resulting in versatile and realistic deformations. First, we observe that the problem of bias towards low frequency always hampers supervised learning and leads to overly smooth deformations. To address this issue, we introduce a frequency-control strategy from a spectral perspective that enhances the generation of high-frequency details of the deformation. In addition, to make the network highly generalizable and able to learn various clothing deformations effectively, we propose a spectral descriptor to achieve a generalized description of the global shape information. Building on the above strategies, we develop a dynamic clothing deformation estimator that integrates frequency-controllable attention mechanisms with long short-term memory. The estimator takes as input expressive features from garments and human bodies, allowing it to automatically output continuous deformations for diverse clothing types, independent of mesh topology or vertex count. Finally, we present a neural collision handling method to further enhance the realism of garments. Our experimental results demonstrate the effectiveness of our approach on a variety of free-swinging garments and its superiority over state-of-the-art methods.