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Lifting the valley degeneracy of monolayer transition metal dichalcogenides (TMDs) would allow versatile control of the valley degree of freedom. We report a giant valley exciton splitting of 16 meV/T for monolayer WS 2 , using the proximity effect from an EuS substrate, which is enhanced by nearly two orders of magnitude from that obtained by an external magnetic field. More interestingly, a sign reversal of the valley splitting is observed as compared to that of WSe 2 on EuS. Using first principles calculations, we investigate the complex behavior of exchange interactions between TMDs and EuS. The sign reversal is attributed to competing ferromagnetic (FM) and antiferromagnetic (AFM) exchange interactions for Eu- and S- terminated EuS surface sites. They act differently on the conduction and valence bands of WS 2 compared to WSe 2 . Tuning the sign and magnitude of the valley exciton splitting offers opportunities for control of valley pseudospin for quantum information processing. Valley degree of freedom promises the additional control of electrons in 2D materials but is limited by small valley splitting. Here the authors show heavily enhanced valley splitting in monolayer WS 2 on EuS substrate due to competing ferromagnetic and antiferromagnetic exchange interactions for Eu- and S-terminated EuS surface sites.

ObjectivesTo take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).Materials and methodsThe training set in this retrospective study consisted of 590 patients (460 with APE and 130 without APE) who underwent CTPA. A fully deep learning convolutional neural network (DL-CNN), called U-Net, was trained for the segmentation of clot. Additionally, an in-house validation set consisted of 288 patients (186 with APE and 102 without APE). In this study, we set different probability thresholds to test the performance of U-Net for the clot detection and selected sensitivity, specificity, and area under the curve (AUC) as the metrics of performance evaluation. Furthermore, we investigated the relationship between the clot burden assessed by the Qanadli score, Mastora score, and other imaging parameters on CTPA and the clot burden calculated by the DL-CNN model.ResultsThere was no statistically significant difference in AUCs with the different probability thresholds. When the probability threshold for segmentation was 0.1, the sensitivity and specificity of U-Net in detecting clot respectively were 94.6% and 76.5% while the AUC was 0.926 (95% CI 0.884–0.968). Moreover, this study displayed that the clot burden measured with U-Net was significantly correlated with the Qanadli score (r = 0.819, p < 0.001), Mastora score (r = 0.874, p < 0.001), and right ventricular functional parameters on CTPA.ConclusionsDL-CNN achieved a high AUC for the detection of pulmonary emboli and can be applied to quantitatively calculate the clot burden of APE patients, which may contribute to reducing the workloads of clinicians.Key Points• Deep learning can detect APE with a good performance and efficiently calculate the clot burden to reduce the physicians’ workload.• Clot burden measured with deep learning highly correlates with Qanadli and Mastora scores of CTPA.• Clot burden measured with deep learning correlates with parameters of right ventricular function on CTPA.

Magnetic exchange field from an EuS substrate breaks the valley degeneracy for monolayer WSe 2 , leading to enhanced valley splitting. Exploiting the valley degree of freedom to store and manipulate information provides a novel paradigm for future electronics. A monolayer transition-metal dichalcogenide (TMDC) with a broken inversion symmetry possesses two degenerate yet inequivalent valleys 1 , 2 , which offers unique opportunities for valley control through the helicity of light 3 , 4 , 5 . Lifting the valley degeneracy by Zeeman splitting has been demonstrated recently, which may enable valley control by a magnetic field 6 , 7 , 8 , 9 . However, the realized valley splitting is modest (∼0.2 meV T –1 ). Here we show greatly enhanced valley spitting in monolayer WSe 2 , utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate. A valley splitting of 2.5 meV is demonstrated at 1 T by magnetoreflectance measurements and corresponds to an effective exchange field of ∼12 T. Moreover, the splitting follows the magnetization of EuS, a hallmark of the MEF. Utilizing the MEF of a magnetic insulator can induce magnetic order and valley and spin polarization in TMDCs, which may enable valleytronic and quantum-computing applications 10 , 11 , 12 .

Status monitoring and fault diagnosis of mechanical equipment are vital for ensuring operational safety. However, real-world diagnostic scenarios often suffer from limited and imbalanced fault data, affecting model accuracy and reliability. This study addresses these challenges by focusing on bearings and hydraulic pumps as research objects. A dual attention-deep convolutional generative adversarial network (DA-DCGAN) is proposed to generate fault signals and enhance diagnosis under imbalanced conditions.Initially, fault vibration signals are converted into time-frequency maps using continuous wavelet transform (CWT) to highlight key features. These maps are used to train the DA-DCGAN, which generates additional fault samples to augment the imbalanced dataset. The expanded dataset is then used to train two classifiers, CNN and DA-CNN, to evaluate their ability to capture minority class fault features. Experimental evaluations on bearing and hydraulic pump datasets reveal that the proposed approach significantly improves classification performance across varying imbalance ratios.The results demonstrate that DA-DCGAN effectively enhances diagnostic accuracy and model generalization under imbalanced sample conditions, offering a robust solution for fault diagnosis in mechanical systems.

Research efforts of cavity quantum electrodynamics have focused on the manipulation of matter hybridized with photons under the strong coupling regime 1 – 3 . This has led to striking discoveries including polariton condensation 2 and single-photon nonlinearity 3 , where the phonon scattering plays a critical role 1 – 9 . However, resolving the phonon scattering remains challenging for its non-radiative complexity. Here we demonstrate nonlinear phonon scattering in monolayer MoS 2 that is strongly coupled to a plasmonic cavity mode. By hybridizing excitons and cavity photons, the phonon scattering is equipped with valley degree of freedom and boosted with superlinear enhancement to a stimulated regime, as revealed by Raman spectroscopy and our theoretical model. The valley polarization is drastically enhanced and sustained throughout the stimulated regime, suggesting a coherent scattering process enabled by the strong coupling. Our findings clarify the feasibility of valley–cavity-based systems for lighting, imaging, optical information processing and manipulating quantum correlations in cavity quantum electrodynamics 2 , 3 , 10 – 17 . Strong exciton–polariton coupling is leveraged as a means to open up phonon scattering channels that are otherwise weak.

Accurate solar background noise modeling in island-reef LiDAR surveys is hindered by anisotropic coastal reflectivity and dynamic light paths, which isotropic models fail to address. We propose BNR-B, a bidirectional reflectance distribution function (BRDF)-based noise model that integrates solar-receiver geometry with micro-facet scattering dynamics. Validated via single-photon LiDAR field tests on diverse coastal terrains at Jiajing Island, China, BNR-B reveals the following: (1) Solar zenith/azimuth angles non-uniformly modulate noise fields—higher solar zenith angles reduce noise intensity and homogenize spatial distribution; (2) surface reflectivity linearly correlates with noise rate (R2 > 0.99), while roughness governs scattering directionality through micro-facet redistribution. BNR-B achieves 28.6% higher noise calculation accuracy than Lambertian models, with a relative phase error < 2% against empirical data. As the first BRDF-derived solar noise correction framework for coastal LiDAR, it addresses critical limitations of isotropic assumptions by resolving directional noise modulation. The model’s adaptability to marine–terrestrial interfaces enhances precision in coastal monitoring and submarine mapping, offering transformative potential for geospatial applications requiring photon-counting LiDAR in complex environments. Key innovations include dynamic coupling of geometric optics and surface scattering physics, enabling robust spatiotemporal noise quantification, critical for high-resolution terrain reconstruction.

The recently emerged ferromagnetic two-dimensional (2D) materials provide unique platforms for compact spintronic devices down to the atomic-thin regime; however, the prospect is hindered by the limited number  of ferromagnetic 2D materials discovered with limited choices of magnetic properties. If 2D antiferromagnetism could be converted to 2D ferromagnetism, the range of 2D magnets and their potential applications would be significantly broadened. Here, we discovered emergent ferromagnetism by interfacing non-magnetic WS 2 layers with the antiferromagnetic FePS 3 . The WS 2 exhibits an order of magnitude enhanced Zeeman effect with a saturated interfacial exchange field ~38 Tesla. Given the pristine FePS 3 is an intralayer antiferromagnet, the prominent interfacial exchange field suggests the formation of ferromagnetic FePS 3 at interface. Furthermore, the enhanced Zeeman effect in WS 2 is found to exhibit a strong WS 2 -thickness dependence, highlighting the layer-tailorable interfacial exchange coupling in WS 2 -FePS 3 heterostructures, which is potentially attributed to the thickness-dependent interfacial hybridization. The isolation of graphene leads to a surge of interest in two dimensional materials, and recently, ferromagnetism has been observed in several two-dimensional materials. However, two-dimensional ferromagnetism remains rare. Here, Gong et al present an alternative approach to achieve two-dimensional ferromagnetism; combining antiferromagnetic FePS3 with non-magnetic WS2 they find a ferromagnetic state forms at the interface of these two materials.

Background The ischemia–reperfusion (IR) environment during deep hypothermic circulatory arrest (DHCA) cardiovascular surgery is a major cause of acute kidney injury (AKI), which lacks preventive measure and treatment. It was reported that cold inducible RNA-binding protein (CIRP) can be induced under hypoxic and hypothermic stress and may have a protective effect on multiple organs. The purpose of this study was to investigate whether CIRP could exert renoprotective effect during hypothermic IR and the potential mechanisms. Methods Utilizing RNA-sequencing, we compared the differences in gene expression between Cirp knockout rats and wild-type rats after DHCA and screened the possible mechanisms. Then, we established the hypothermic oxygen–glucose deprivation (OGD) model using HK-2 cells transfected with siRNA to verify the downstream pathways and explore potential pharmacological approach. The effects of CIRP and enarodustat (JTZ-951) on renal IR injury (IRI) were investigated in vivo and in vitro using multiple levels of pathological and molecular biological experiments. Results We discovered that Cirp knockout significantly upregulated rat Phd3 expression, which is the key regulator of HIF-1α, thereby inhibiting HIF-1α after DHCA. In addition, deletion of Cirp in rat model promoted apoptosis and aggravated renal injury by reactive oxygen species (ROS) accumulation and significant activation of the TGF-β1/p38 MAPK inflammatory pathway. Then, based on the HK-2 cell model of hypothermic OGD, we found that CIRP silencing significantly stimulated the expression of the TGF-β1/p38 MAPK inflammatory pathway by activating the PHD3/HIF-1α axis, and induced more severe apoptosis through the mitochondrial cytochrome c-Apaf-1-caspase 9 and FADD-caspase 8 death receptor pathways compared with untransfected cells. However, silencing PHD3 remarkably activated the expression of HIF-1α and alleviated the apoptosis of HK-2 cells in hypothermic OGD. On this basis, by pretreating HK-2 and rats with enarodustat, a novel HIF-1α stabilizer, we found that enarodustat significantly mitigated renal cellular apoptosis under hypothermic IR and reversed the aggravated IRI induced by CIRP defect, both in vitro and in vivo. Conclusion Our findings indicated that CIRP may confer renoprotection against hypothermic IRI by suppressing PHD3/HIF-1α-mediated apoptosis. PHD3 inhibitors and HIF-1α stabilizers may have clinical value in renal IRI.

Objectives This study investigated whether exosomes from LPS pretreated bone marrow mesenchymal stem cells (LPS pre-MSCs) could prolong skin graft survival. Methods The exosomes were isolated from the supernatant of MSCs pretreated with LPS. LPS pre-Exo and rapamycin were injected via the tail vein into C57BL/6 mice allografted with BALB/c skin; graft survival was observed and evaluated. The accumulation and polarization of macrophages were examined by immunohistochemistry. The differentiation of macrophages in the spleen was analyzed by flow cytometry. For in vitro, an inflammatory model was established. Specifically, bone marrow-derived macrophages (BMDMs) were isolated and cultured with LPS (100 ng/ml) for 3 h, and were further treated with LPS pre-Exo for 24 h or 48 h. The molecular signaling pathway responsible for modulating inflammation was examined by Western blotting. The expressions of downstream inflammatory cytokines were determined by Elisa, and the polarization of macrophages was analyzed by flow cytometry. Results LPS pre-Exo could better ablate inflammation compared to untreated MSC-derived exosomes (BM-Exo). These loaded factors inhibited the expressions of inflammatory factors via a negative feedback mechanism. In vivo, LPS pre-Exo significantly attenuated inflammatory infiltration, thus improving the survival of allogeneic skin graft. Flow cytometric analysis of BMDMs showed that LPS pre-Exo were involved in the regulation of macrophage polarization and immune homeostasis during inflammation. Further investigation revealed that the NF-κB/NLRP3/procaspase-1/IL-1β signaling pathway played a key role in LPS pre-Exo-mediated regulation of macrophage polarization. Inhibiting NF-κB in BMDMs could abolish the LPS-induced activation of inflammatory pathways and the polarization of M1 macrophages while increasing the proportion of M2 cells. Conclusion LPS pre-Exo are able to switch the polarization of macrophages and enhance the resolution of inflammation. This type of exosomes provides an improved immunotherapeutic potential in prolonging graft survival.

Geopolitics events have a significant impact on financial markets in the process of global economic integration and financial liberalization. Focusing on the 2022 Russia-Ukraine conflict, this paper utilizes the TVP-VAR network connectedness approach to investigate its specific impacts on the connectedness of major global financial markets. It examines the dynamic evolution of high-frequency and low-frequency shocks on market correlations during the pre-conflict, initial, and stalemate stages, revealing the changes in risk transmission paths. Findings show that the conflict led to increased market interconnectedness. Low-frequency became a major part of the total spillover, signaling long-term structural changes. The stock and foreign exchange market initially responded swiftly, serving as starting points for risk diffusion. The commodity market has been risk receivers throughout the conflict period. Intra-market and cross-market linkages have also changed. The German stock market was the main source of risk spillovers in the conflict, influencing international market linkages. In the global financial market network, several stock markets were the primary risk transmitters in the early stage of the conflict, while foreign exchange markets took on this role during the stalemate period. In addition, gold continues to play a safe-haven role. Furthermore, the TVP-VAR model is used to quantitatively assess the extent of the impact of the conflict on the spillover effects of major global financial markets at different time points and lags. Based on these findings, this paper constructs diversified portfolios and proposes a series of targeted risk management recommendations, which provide a theoretical basis for future policy formulation under similar crises.