The kinetics of Criegee intermediates are important for atmospheric modeling. However, the quantitative kinetics of Criegee intermediates are still very limited, especially for those with hydroxy groups. Here, we calculate rate constants for the unimolecular reaction of E-glycolaldehyde oxide [E-hydroxyethanal oxide, E-(CH₂OH)CHOO], for its reactions with H₂O and (H₂O)₂, and for the reaction of the E-(CH₂OH)CHOO…H₂O complex with H₂O. For the highest level of electronic structure, we use W3X-L//CCSD(T)-F12a/cc-pVDZ-F12 for the unimolecular reaction and the reaction with water and W3X-L//DF-CCSD(T)-F12b/jun-cc-pVDZ for the reaction with 2 water molecules. For the dynamics, we use a dual-level strategy that combines conventional transition state theory with the highest level of electronic structure and multistructural canonical variational transition state theory with small-curvature tunneling with a validated density functional for the electronic structure. This dynamical treatment includes high-frequency anharmonicity, torsional anharmonicity, recrossing effects, and tunneling. We find that the unimolecular reaction of E-(CH₂OH)CHOO depends on both temperature and pressure. The calculated results show that E-(CH₂OH)CHOO…H₂O + H₂O is the dominant entrance channel, while previous investigations only considered Criegee intermediates + (H₂O)₂. In addition, we find that the atmospheric lifetime of E-(CH₂OH)CHOO with respect to 2 water molecules is particularly short with a value of 1.71 × 10⁻⁶ s at 0 km, which is about 2 orders of magnitude shorter than those usually assumed for Criegee intermediate reactions with water dimer. We also find that the OH group in E-(CH₂OH)CHOO enhances its reactivity.

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system’s changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems.

Effective condition monitoring and fault diagnosis of bearings can not only maximize the life of rolling bearings and prevent unexpected shutdowns caused by equipment failures but also eliminate unnecessary costs and waste caused by excessive maintenance. However, the existing deep-learning-based bearing fault diagnosis models have the following defects. First of all, these models have a large demand for fault data. Second, the previous models only consider that single-scale features are generally less effective in diagnosing bearing faults. Therefore, we designed a bearing fault data collection platform based on the Industrial Internet of Things, which is used to collect bearing status data from sensors in real time and feed it back into the diagnostic model. On the basis of this platform, we propose a bearing fault diagnosis model based on deep generative models with multiscale features (DGMMFs) to solve the above problems. The DGMMF model is a multiclassification model, which can directly output the abnormal type of the bearing. Specifically, the DGMMF model uses 4 different variational autoencoder models to augment the bearing data and integrates features of different scales. Compared with single-scale features, these multiscale features contain more information and can perform better. Finally, we conducted a large number of related experiments on the real bearing fault datasets and verified the effectiveness of the DGMMF model using multiple evaluation metrics. The DGMMF model has achieved the highest value under all metrics, among which the value of precision is 0.926, the value of recall is 0.924, the value of accuracy is 0.926, and the value of F1 score is 0.925.

The majority of atherothrombotic events (e.g., cerebral or myocardial infarction) often occur as a result of plaque rupture or erosion in the carotid, and thereby it is urgent to assess plaque vulnerability and predict adverse cerebrovascular events. However, the monitoring evolution from stable plaque into life-threatening high-risk plaque in the slender carotid artery is a great challenge, due to not enough spatial resolution for imaging the carotid artery based on most of reported fluorescent probes. Herein, copolymerizing with the small molecules of acceptor-donor-acceptor-donor-acceptor (A-D-A′-D-A) and the electron-donating units (D′), the screened second near-infrared (NIR-II) nanoprobe presents high quantum yield and good stability, so that it enables to image slender carotid vessel with enough spatial resolution. Encouragingly, NIR-II nanoprobe can effectively target to intraplaque macrophage, meanwhile distinguishing vulnerable plaque in carotid atherosclerosis in living mice. Moreover, the NIR-II nanoprobe can dynamically monitor the fresh bleeding spots in carotid plaque, indicating the increased risk of plaque instability. Besides, magnetic resonance imaging is integrated with NIR-II fluorescence imaging to provide contrast for subtle structure (e.g., narrow lumen and lipid pool), via incorporating ultrasmall superparamagnetic iron oxide into the NIR-II nanoprobe. Thus, such hybrid NIR-II/magnetic resonance imaging multimodal nanoprobe provides an effective tool for assessing carotid plaque burden, selecting high-risk plaque, and imaging intraplaque hemorrhage, which is promising for reducing cerebral/ myocardial infarction-associated morbidity and mortality.

Monocyte-to-M0/M1 macrophage differentiation with unclear molecular mechanisms is a pivotal cellular event in many cardiovascular diseases including atherosclerosis. Long non-coding RNAs (lncRNAs) are a group of protein expression regulators; however, the roles of monocyte-lncRNAs in macrophage differentiation and its related vascular diseases are still unclear. The study aims to investigate whether the novel leukocyte-specific lncRNA Morrbid could regulate macrophage differentiation and atherogenesis. We identified that Morrbid was increased in monocytes and arterial walls from atherosclerotic mouse and from patients with atherosclerosis. In cultured monocytes, Morrbid expression was markedly increased during monocyte to M0 macrophage differentiation with an additional increase during M0 macrophage-to-M1 macrophage differentiation. The differentiation stimuli-induced monocyte–macrophage differentiation and the macrophage activity were inhibited by Morrbid knockdown. Moreover, overexpression of Morrbid alone was sufficient to elicit the monocyte–macrophage differentiation. The role of Morrbid in monocyte–macrophage differentiation was also identified in vivo in atherosclerotic mice and was verified in Morrbid knockout mice. We identified that PI3-kinase/Akt was involved in the up-regulation of Morrbid expression, whereas s100a10 was involved in Morrbid-mediated effect on macrophage differentiation. To provide a proof of concept of Morrbid in pathogenesis of monocyte/macrophage-related vascular disease, we applied an acute atherosclerosis model in mice. The results revealed that overexpression of Morrbid enhanced but monocyte/macrophage-specific Morrbid knockout inhibited the monocytes/macrophages recruitment and atherosclerotic lesion formation in mice. The results suggest that Morrbid is a novel biomarker and a modulator of monocyte–macrophage phenotypes, which is involved in atherogenesis.

The therapeutic outcomes of conventional oral medications against ulcerative colitis (UC) are restricted by inefficient drug delivery to the colitis mucosa and weak capacity to modulate the inflammatory microenvironment. Herein, a fluorinated pluronic (FP127) was synthesized and employed to functionalize the surface of mulberry leaf-derived nanoparticles (MLNs) loading with resveratrol nanocrystals (RNs). The obtained FP127@RN-MLNs possessed exosome-like morphologies, desirable particle sizes (around 171.4 nm), and negatively charged surfaces (−14.8 mV). The introduction of FP127 to RN-MLNs greatly improved their stability in the colon and promoted their mucus infiltration and mucosal penetration capacities due to the unique fluorine effect. These MLNs could efficiently be internalized by colon epithelial cells and macrophages, reconstruct disrupted epithelial barriers, alleviate oxidative stress, provoke macrophage polarization to M2 phenotype, and down-regulate inflammatory responses. Importantly, in vivo studies based on chronic and acute UC mouse models demonstrated that oral administration of chitosan/alginate hydrogel-embedding FP127@RN-MLNs achieved substantially improved therapeutic efficacies compared with nonfluorinated MLNs and a first-line UC drug (dexamethasone), as evidenced by decreased colonic and systemic inflammation, integrated colonic tight junctions, and intestinal microbiota balance. This study brings new insights into the facile construction of a natural, versatile nanoplatform for oral treatment of UC without adverse effects.

Heterogeneous nucleation plays a critical role in the phase transition of water, which can cause damage in various systems. Here, we report that heterogeneous nucleation can be inhibited by utilizing hydrogel coatings to isolate solid surfaces and water. Hydrogels, which contain over 90% water when fully swelled, exhibit a high degree of similarity to water. Due to this similarity, there is a great energy barrier for heterogeneous nucleation along the water–hydrogel interface. Additionally, hydrogel coatings, which possess polymer networks, exhibit higher fracture energy and more robust adhesion to solid surfaces compared to water. This high fracture and adhesion energy acts as a deterrent for fracture nucleation within the hydrogel or along the hydrogel–solid interface. With a hydrogel layer approximately 100 μm thick, the boiling temperature of water under atmospheric pressure can be raised from 100 to 108 °C. Notably, hydrogel coatings also result in remarkable reductions in cavitation pressure on multiple solid surfaces. We have demonstrated the efficacy of hydrogel coatings in preventing damages resulting from acceleration-induced cavitation. Hydrogel coatings have the potential to alter the energy landscape of heterogeneous nucleation on the water–solid interface, making them an exciting avenue for innovation in heat transfer and fluidic systems.

The oxygen octahedral rotation (OOR) forms fundamental atomic distortions and symmetries in perovskite oxides and definitely determines their properties and functionalities. Therefore, epitaxial strain and interfacial structural coupling engineering have been developed to modulate the OOR patterns and explore novel properties, but it is difficult to distinguish the 2 mechanisms. Here, different symmetries are induced in Na_0.5Bi_0.5TiO₃ (NBT) epitaxial films by interfacial oxygen octahedral coupling rather than epitaxial strain. The NBT film grown on the Nb:SrTiO₃ substrate exhibits a paraelectric tetragonal phase, while with La_0.5Sr_0.5MnO₃ as a buffer layer, a monoclinic phase and robust ferroelectricity are obtained, with a remanent polarization of 42 μC cm⁻² and a breakdown strength of 7.89 MV cm⁻¹, which are the highest record among NBT-based films. Moreover, the interfacial oxygen octahedral coupling effect is demonstrated to propagate to the entire thickness of the film, suggesting an intriguing long-range effect. This work provides a deep insight into understanding the structure modulation in perovskite heterostructures and an important avenue for achieving unique functionalities.

Mitochondrial dysfunction is a hallmark feature of cellular senescence and organ aging. Here, we asked whether the mitochondrial antiviral signaling protein (MAVS), which is essential for driving antiviral response, also regulates human stem cell senescence. To answer this question, we used CRISPR/Cas9-mediated gene editing and directed differentiation techniques to generate various MAVS-knockout human stem cell models. We found that human mesenchymal stem cells (hMSCs) were sensitive to MAVS deficiency, as manifested by accelerated senescence phenotypes. We uncovered that the role of MAVS in maintaining mitochondrial structural integrity and functional homeostasis depends on its interaction with the guanosine triphosphatase optic atrophy type 1 (OPA1). Depletion of MAVS or OPA1 led to the dysfunction of mitochondria and cellular senescence, whereas replenishment of MAVS or OPA1 in MAVS-knockout hMSCs alleviated mitochondrial defects and premature senescence phenotypes. Taken together, our data underscore an uncanonical role of MAVS in safeguarding mitochondrial homeostasis and antagonizing human stem cell senescence.

With the progress of theoretical and applied technologies, the communication system based on the classical encryption is seriously threatened by quantum computing and distributed computing. A communication method that directly loads confidential information on the quantum state, quantum secure direct communication (QSDC), came into being for resisting security threats. Here, we report the first continuous-variable QSDC (CV-QSDC) experimental demonstration for verifying the feasibility and effectiveness of the CV-QSDC protocol based on Gaussian mapping and propose a parameter estimation for signal classification under the actual channels. In our experiment, we provided 4 × 10² blocks, where each block contains 10⁵ data for direct information transmission. For the transmission distance of 5 km in our experiment, the excess noise is 0.0035 SNU, where SNU represents the unit of shot-noise units. The 4.08 × 10⁵ bit per second experimental results firmly demonstrated the feasibility of CV-QSDC under the fiber channel. The proposed grading judgment method based on parameter estimation provides a practical and available message processing scheme for CV-QSDC in a practical fiber channel and lays the groundwork for the grading reconciliation.