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The ocean serves as a significant contributor of atmospheric Hydrogen (H2) with indirect greenhouse effects. However, uncertainties persist regarding internal production and consumption processes of marine H2, as well as controlling factors. Our study examined the spatial distribution and source‐sink dynamics of marine H2 in the Eastern Indian Ocean. H2 concentrations in surface seawater exhibited a range of 2.95–21.96 nmol L−1. High concentrations of H2 were observed in the anoxic water in the Bay of Bengal. Rates of H2 photo‐production and microbial consumption in surface seawater ranged from 1.80 to 17.78 nmol L−1 h−1 and 1.02–9.18 nmol L−1 h−1, respectively. When considering the entire mixed layer, photo‐production contribute to approximately 31%–43% of the total H2 removal, with cyanobacteria potentially serving as another source in the mixed layer. Compared with the sea‐to‐air exchange, microbial consumption was the primary removal pathway of H2 in seawater. Plain Language Summary Atmospheric hydrogen (H2) can influence the environment and climate by consuming hydroxyl radicals (OH·) and indirectly raising greenhouse gas concentrations. Although the ocean serves as a significant source of atmospheric H2, the biogeochemical processes governing its presence in seawater remain poorly understood. The Eastern Indian Ocean, characterized by a substantial inflow of freshwater, exerts a distinct impact on the local ecosystem. We conducted a field investigation in the Eastern Indian Ocean to clarify the sources, sinks, and controlling factors of H2, including the Bay of Bengal with relatively higher primary productivity and the Eastern Equatorial Indian Ocean with low primary productivity, respectively. Our study involved the quantitative assessment of H2 photo‐production, microbial consumption, and sea‐to‐air exchange in seawater, along with the calculation of the H2 budget in the mixed layer. This investigation enhances our understanding of H2 cycling processes in seawater and contributes to the assessment of H2 emissions from the ocean and their impact on the atmospheric budget. Key Points The distribution of H2 was significantly affected by river input in the Eastern Indian Ocean Photo‐production was an important source of H2 in the mixed layer Microbial consumption was the primary sink for H2 in the mixed layer

High gas‐uptake capacity is desirable for many reasons such as gas storage and sequestration. Moreover, ultrahigh capacity can enable a practical separation process by mitigating the selectivity factor that sometimes compromises separation efficiency. Herein, a single‐walled nickel–organic framework with an exceptionally high gas capture capability is reported. For example, C2H4 and C2H6 uptake capacities are at record‐setting levels of 224 and 289 cm3 g−1 at 273 K and 1 bar (169 and 110 cm3 g−1 at 298 K and 1 bar), respectively. Such ultrahigh capacities for both gases give rise to an excellent separation performance, as shown for C2H6/C2H4 with breakthrough times of 100, 60 and 30 min at 273, 283 and 298 K and under 1 atm. This new material is also shown to readily remove ethylene released from fruits, and once again, its ultrahigh capacity plays a key role in the extraordinary length of time achieved in the preservation of the fruit freshness. Long live bananas! A stable nickel metal–organic framework with a voracious and unsurpassed appetite for ethane can also perform direct air capture of ethylene to keep bananas going on and on.

Recent research has shown that visual–text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers. Thanks to its excellent visual representation capabilities, many recent studies have used CLIP for pixel-level tasks. We explore the potential abilities of CLIP in the field of few-shot segmentation. The current mainstream approach is to utilize support and query features to generate class prototypes and then use the prototype features to match image features. We propose a new method that utilizes CLIP to extract text features for a specific class. These text features are then used as training samples to participate in the model’s training process. The addition of text features enables model to extract features that contain richer semantic information, thus making it easier to capture potential class information. To better match the query image features, we also propose a new prototype generation method that incorporates multi-modal fusion features of text and images in the prototype generation process. Adaptive query prototypes were generated by combining foreground and background information from the images with the multi-modal support prototype, thereby allowing for a better matching of image features and improved segmentation accuracy. We provide a new perspective to the task of few-shot segmentation in multi-modal scenarios. Experiments demonstrate that our proposed method achieves excellent results on two common datasets, PASCAL-5i and COCO-20i.

Hot film sensors detect the flow shear stress based on the forced convection heat transfer to the fluid. Current hot film sensors have been significantly hindered by the relatively low sensitivity due to the massive heat conduction to the substrate. This paper describes the design, fabrication, simulation, and testing of a novel flow sensor with dual-layer hot film structures. More specifically, the heat conduction was insulated from the sensing heater to the substrate by controlling both sensing and guarding heaters working at the same temperature, resulting in a higher sensitivity. The experiment and simulation results showed that the sensitivity of the dual-layer hot film sensor was significantly improved in comparison to the single-layer sensor. Additionally, the dual-layer sensor was designed and fabricated in an integrated, flexible, and miniaturized manner. Its small size makes it an excellent candidate for flow detection.

Abstract Hydrogen (H2) influences the climate by prolonging the lifetime of greenhouse gases via consuming hydroxyl radicals. Despite the ocean's crucial role in regulating atmospheric H2, its marine biogeochemical cycle is poorly constrained. To bridge this gap, our research in the Western Tropical Pacific Ocean (WTPO) revealed a north–south increasing gradient in the surface (1.40–23.4 nmol L−1). Key processes encompassing photo‐production (1.43–4.36 nmol L−1 h−1), microbial consumption (0.57–1.92 nmol L−1 h−1), and sea‐to‐air exchange (1.19–11.3 × 10−3 nmol L−1 h−1) were conducted to refine the budget estimation for the mixed layer. The highest photo‐production and consumption rates occurred at equatorial stations driven by La Niña–induced upwelling that supplied nutrients and organic matter. The sea–air flux in the WTPO was large enough to influence the turnover of atmospheric H2 above the ocean. This research provides critical constraints for assessing oceanic H2 emission and environmental impact.

With the advent of large-scale datasets, significant advancements have been made in image semantic segmentation. However, the annotation of these datasets necessitates substantial human and financial resources. Therefore, the focus of research has shifted towards few-shot semantic segmentation, which leverages a small number of labeled samples to effectively segment unknown categories. The current mainstream methods are to use the meta-learning framework to achieve model generalization, and the main challenges are as follows. (1) The trained model will be biased towards the seen class, so the model will misactivate the seen class when segmenting the unseen class, which makes it difficult to achieve the idealized class agnostic effect. (2) When the sample size is limited, there exists an intra-class gap between the provided support images and the query images, significantly impacting the model’s generalization capability. To solve the above two problems, we propose a network with prototype complementarity characteristics (PCNet). Specifically, we first generate a self-support query prototype based on the query image. Through the self-distillation, the query prototype and the support prototype perform feature complementary learning, which effectively reduces the influence of the intra-class gap on the model generalization. A standard semantic segmentation model is introduced to segment the seen classes during the training process to achieve accurate irrelevant class shielding. After that, we use the rough prediction map to extract its background prototype and shield the background in the query image by the background prototype. In this way, we obtain more accurate fine-grained segmentation results. The proposed method exhibits superiority in extensive experiments conducted on the PASCAL-5i and COCO-20i datasets. We achieve new state-of-the-art results in the few-shot semantic segmentation task, with an mIoU of 71.27% and 51.71% in the 5-shot setting, respectively. Comprehensive ablation experiments and visualization studies show that the proposed method has a significant effect on small-sample semantic segmentation.

The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation (μSR) results of H3LiIr2O6, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field (LF)-μSR polarization is observed down to 0.1 K. This indicates a dynamical scaling, whose critical exponent of 0.46 is excellently consistent with the scaling behavior of specific heat and magnetization data. All the observations point to the finite DOS with the form N(E)∼E−ν, which is expected for the Kitaev QSL in the presence of vacancies. Our μSR study provides a dynamical fingerprint of the power-law low-energy DOS and introduces a crucial new insight into the vacancy effect in QSL.

Hypertension is a prominent contributor to vascular injury. Deubiquinatase has been implicated in the regulation of hypertension-induced vascular injury. In the present study we investigated the specific role of deubiquinatase YOD1 in hypertension-induced vascular injury. Vascular endothelial endothelial-mesenchymal transition (EndMT) was induced in male WT and YOD1 −/− mice by administration of Ang II (1 μg/kg per minute) via osmotic pump for four weeks. We showed a significantly increased expression of YOD1 in mouse vascular endothelial cells upon Ang II stimulation. Knockout of YOD1 resulted in a notable reduction in EndMT in vascular endothelial cells of Ang II-treated mouse; a similar result was observed in Ang II-treated human umbilical vein endothelial cells (HUVECs). We then conducted LC-MS/MS and co-immunoprecipitation (Co-IP) analyses to verify the binding between YOD1 and EndMT-related proteins, and found that YOD1 directly bound to β-catenin in HUVECs via its ovarian tumor-associated protease (OTU) domain, and histidine at 262 performing deubiquitination to maintain β-catenin protein stability by removing the K48 ubiquitin chain from β-catenin and preventing its proteasome degradation, thereby promoting EndMT of vascular endothelial cells. Oral administration of β-catenin inhibitor MSAB (20 mg/kg, every other day for four weeks) eliminated the protective effect of YOD1 deletion on vascular endothelial injury. In conclusion, we demonstrate a new YOD1-β-catenin axis in regulating Ang II-induced vascular endothelial injury and reveal YOD1 as a deubiquitinating enzyme for β-catenin, suggesting that targeting YOD1 holds promise as a potential therapeutic strategy for treating β-catenin-mediated vascular diseases.

The exploration of robust titanium-based metal-organic framework (MOF) photocatalysts for efficient CO 2 reduction is of critical significance but remains challenging. Herein, a hierarchically porous titanium-MOF (hMUV-10) anchored with ultrafine Pd nanoparticles was rationally designed via a convenient one-step in-situ water-etching strategy. The hierarchical MUV-10 structure provided abundant sites for the anchoring of Pd nanoparticles on the outside and inside of MOFs. The optimized Pd/hMUV-10 demonstrated an ultrahigh CO production rate of 65.9 mmol g −1 h −1 under light irradiation at 350°C, approximately two orders of magnitude higher than the state-of-the-art MOF-based catalysts and surpassed most reported inorganic semiconductor-based catalysts. The CO production rate under a relatively mild temperature of 200°C also reached as high as 3.36 mmol g −1 h −1 , and negligible activity decay was observed during continuous cycling measurement under 350°C. Theoretical calculations suggested that Pd enhanced CO 2 adsorption ability and reduced the energy barrier for CO 2 reduction, thereby leading to a highly improved CO yield from photothermal CO 2 reduction.

In the present work, the density, ρ, refractive index, n D , and dynamic viscosity, η, for four binary solutions containing ionic liquids [C n mim]X (C n mim = 1-alkyl-3-methylimidazolium; n = 6, 8; X = Cl, Br) and ethylene glycol (EG) were investigated at temperatures of 288.15–333.15 K and at ambient pressure. In addition, the excess molar volume, V m E , was calculated and correlated using the Redlich-Kister polynomial equation. The temperature effect on the density of the binary systems studied was expressed by a linear two-parameter equation. The variation in density, refractive index, and viscosity with the composition was described by polynomial equations. The influence of carbon chain-length and the anion of the ionic liquids, and the influence of the temperature on the physicochemical properties of the binary systems can be explained by comparison of the experimental results.