Plasma-induced unconventional shock waves on oil surfaces

Electric corona discharge in a multi-phase system results in complex electro-hydrodynamic phenomena. We observed unconventional shock wave propagation on an oil thin film sprayed over a polymer-coated conductor. A hair-thin single shock wave arose when the high voltage bias of an overhung steel needle was abruptly removed. However, such solitary waves possess neither interference nor reflection properties commonly known for ordinary waves, and also differ from the solitons in a canal or an optical fiber. We also observed time-retarded movement for dispersed oil droplets at various distances from the epicenter which have no physical contact, as if a wave propagating on a continuous medium. Such a causality phenomenon for noncontact droplets to move resembling wave propagation could not be possibly described by the conventional surface wave equation. Our systematic studies reveal a mechanism involving oil surface charges driven by reminiscent electric fields in the air when the needle bias is suddenly removed

DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI

Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page

Plasma-induced unconventional shock waves on oil surfaces

Electric corona discharge in a multi-phase system results in complex electro-hydrodynamic phenomena. We observed unconventional shock wave propagation on an oil thin film sprayed over a polymer-coated conductor. A hair-thin single shock wave arose when the high voltage bias of an overhung steel needle was abruptly removed. However, such solitary waves possess neither interference nor reflection properties commonly known for ordinary waves, and also differ from the solitons in a canal or an optical fiber. We also observed time-retarded movement for dispersed oil droplets at various distances from the epicenter which have no physical contact, as if a wave propagating on a continuous medium. Such a causality phenomenon for noncontact droplets to move resembling wave propagation could not be possibly described by the conventional surface wave equation. Our systematic studies reveal a mechanism involving oil surface charges driven by reminiscent electric fields in the air when the needle bias is suddenly removed

Environmental drivers of the leaf nitrogen and phosphorus stoichiometry characteristics of critically endangered Acer catalpifolium

Acer catalpifolium is a perennial deciduous broad-leaved woody plant, listed in the second-class protection program in China mainly distributed on the northwest edge of Chengdu plain. However, extensive anthropogenic disturbances and pollutants emissions (such as SO2, NH3 and NOX) in this area have created a heterogeneous habitat for this species and its impacts have not been systematically studied. In this study, we investigated the leaf nitrogen (N) and phosphorus (P) content of A. catalpifolium in the natural distribution areas, and a series of simulation experiments (e.g., various water and light supply regimes, different acid and N deposition levels, reintroduction management) were conducted to analyze responses of N and P stoichiometric characteristics to environmental changes. The results showed that leaf nitrogen content (LNC) was 14.49 ~ 25.44 mg g-1, leaf phosphorus content (LPC) was 1.29~3.81 mg g-1 and the N/P ratio of the leaf (L-N/P) was 4.87~13.93. As per the simulation experiments, LNC of A. catalpifolium is found to be relatively high at strong light conditions (80% of full light), high N deposition (100 and 150 kg N ha-1), low acidity rainwater, reintroduction to understory area or N fertilizer applications. A high level of LPC was found when applied with 80% of full light and moderate N deposition (100 kg N ha-1). L-N/P was high under severe shade (8% of full light), severe N deposition (200 kg N ha-1), and reintroduction to gap and undergrowth habitat; however, low L-N/P was observed at low acidity rainwater or P fertilizer application. The nutrient supply facilitates corresponding elements uptake, shade tends to induce P limitation and soil acidification shows N limitation. Our results provide theoretical guidance for field management and nutrient supply regimes for future protection, population rejuvenation of this species and provide guidelines for conservation and nutrient management strategies for the endangered species

Pulmonary lymphangioleimyomatosis and systemic lupus erythematosus in a menopausal woman

Abstract Background Pulmonary lymphangioleimyomatosis (PLAM) is a rare disease involving lung. PLAM primarily affects young women, a characteristic it shares with systemic lupus erythematosus (SLE). Estrogen has long been assumed to play an important role both in PLAM and SLE. We report a menopausal woman, who was found to have PLAM 1 year after she was diagnosed with SLE. Her chest radiograph was normal in the early phase of SLE. Case presentation A 52-year-old Chinese woman was referred to our hospital in August 2014 because of swelling in both legs. She also reported a malar rash and intermittent generalized arthralgia. Laboratory examination showed leukopenia. Her serum albumin level was 23 g/L; 24-h urinary protein excretion was 5.3 g. She tested positive for anti-Smith (Sm) antibody and anti-SS-A antibody. Renal biopsy indicated Class V + IV(G)-A lupus nephritis (LN). The condition of SLE and LN improved on a regime of tapering prednisolone and intermittent intravenous cyclophosphamide therapy until 1 year later when she developed exertional dyspnea accompanied with frequent cough. Thoracic computed tomography revealed numerous well-defined cysts and the diagnosis of PLAM was confirmed by lung biopsy. In the follow-up period, the patient continued to be on prednisolone and mycophenolate mofetil for the treatment of SLE, but only agreed to receive symptomatic treatment for PLAM. One year after the diagnosis of PLAM, during which time the SLE was stable, she died of respiratory failure and cor pulmonale. Conclusion We report a patient with coexisting SLE and PLAM, who was treated with immunosuppressive therapy. SLE was stable but PLAM was not improved. Although the coexistence of SLE and PLAM might be a coincidence, the occurrence of these two diseases in a menopausal woman may warrant further mechanistic exploration

Hybrid mesh for magnetotelluric forward modeling based on the finite element method

Abstract Unstructured tetrahedral grids have been applied in magnetotelluric (MT) forward modeling using the finite element (FE) method because of their adaptability to complex anomalies. However, high-quality results require an extreme refinement of the near-surface area, which leads to excessive meshes and an increased degree of freedom (DoF) of the governing equation of the finite element system. To reduce the computational cost, we have developed a hybrid mesh based on triangular prisms and tetrahedrons. The required elements in the near-surface area are reduced because the quality of the triangular prism is not limited by the element aspect ratio. The deep area is discretized by tetrahedral elements to ensure the flexibility of the unstructured grids. The superiority of this hybrid mesh has been tested on a layered model, the DTM1 model and terrain relief models. The results show that the modeling efficiency has been improved, especially for high-frequency data. The accuracy of the modeling using the hybrid mesh is significantly higher than that of the tetrahedral mesh with a similar DoF. Usage of the hybrid mesh can be easily adapted to complex geoelectric models with strong terrain fluctuations, which requires less computational cost than using conventional unstructured elements