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We propose a new advection scheme for fluid simulation that improves both conservation and numerical diffusion. Our work differs from previous works in that we re-formulate interpolation as cell-face flux of a vector field instantly constructed from a scalar field, rather than a per-point evaluation at back-traced positions. Our novel interpolation method enables excellent preservation of conservative quantities since the sum of flux exactly counteracts on cell faces, which eventually evaluates the boundary-flux of the whole domain. Our method can be implemented as a plug-and-play extension (or a temporary scratchpad) to the conventional semi-Lagrangian scheme; hence, our method naturally inherits all the benefits of semi-Lagrangian schemes and can be seamlessly integrated with existing fluid simulation pipelines together with other (black-boxed) solver components. We conducted numerical experiments to verify the accuracy of our scheme (conservation and the improved numerical diffusion) and compared its qualitative results with state-of-the-art advection schemes that are in heavy use in the production environment such as MacCormack and the WENO6 interpolation.

We introduce an unsupervised GAN-based model for shading photorealistic hair animations. Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation methods. The main idea is to extend the Cycle-GAN structure to avoid semitransparent hair appearance and to exactly reproduce the interaction of the lights with the scene. We use two constraints to ensure temporal coherence and highlight stability. Our approach outperforms and is computationally more efficient than previous methods.

The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been reported to be a predictor for intravenous immunoglobulin (IVIG) resistance in patients with Kawasaki disease (KD) recently. The objective of the present study was to elucidate the predictive validity of this new marker in a multicenter study. We retrospectively reviewed the clinical records of 520 consecutive KD patients (development data set) and 332 subsequent patients (validation data set) at 7 hospitals in Japan. Both NLR and PLR were significantly higher in the IVIG-resistant group than in the IVIG-responsive group. When we set the cut-off point as NLR ≥ 4.11 and PLR ≥ 119, multiple logistic regression analyses showed that a high NLR and PLR before initial IVIG were independent predictors of IVIG resistance, and their combination was a stronger predictor than either alone. The sensitivity and specificity of the combination of NLR ≥ 4.11 and PLR ≥ 119 were 0.58 and 0.73 in the development data set. Validated using an independent data set, they were 0.54 and 0.72 in the validation data set. On comparing the AUC of this predictor with those of the Gunma and Kurume scores, the AUC was highest for this predictor, followed by the Gunma score and Kurume score (0.70, 0.68, and 0.64, respectively). The predictive validity of the combination of a high NLR and PLR, which is a simple and convenient indicator, was equal to or better than that of the existing scoring systems. The new predictive marker may be a suitable indicator for predicting IVIG resistance in KD patients.

Few previous works have studied the modeling of forest ground surfaces from LiDAR point clouds using implicit functions. [10] is a pioneer in this area. However, by design this approach proposes over-smoothed surfaces, in particular in highly occluded areas, limiting its ability to reconstruct fine-grained terrain surfaces. This paper presents a method designed to finely approximate ground surfaces by relying on deep learning to separate vegetation from potential ground points, filling holes by blending multiple local approximations through the partition of unity principle, then improving the accuracy of the reconstructed surfaces by pushing the surface towards the data points through an iterative convection model.

Neutrophils contribute to the clearance of pathogens through the formation of neutrophil extracellular traps (NETs) in a process known as NETosis, but the excessive release of NETs has been reported to be involved in the pathogenesis of various diseases, including vasculitis, by inducing tissue injury. The aim of the present study was to investigate whether or not NETosis is enhanced in the acute phase of Kawasaki disease (KD). After neutrophils isolated from the peripheral blood of patients with KD and healthy control (HC) were cultured in vitro, the degree of spontaneous NETosis was evaluated by measuring the number of NETs formed and the titers of cell-free DNA (cfDNA) and neutrophil elastase (NE)-DNA complex. Spontaneous NET formation in vitro was observed in neutrophils isolated from KD patients, and the number of NET formations was significantly higher in acute KD than in convalescent KD and HC. The increased levels of cfDNA and NE-DNA complexes in the acute phase of KD tended to decrease in the convalescent phase. Spontaneous NET formation was enhanced in neutrophils from patients with acute KD, suggesting that circulating neutrophils may be primed to undergo NETosis in KD vasculitis.