Simulation of centrifugal pumps based on MPS solver

In this article, a three dimensional full-scale centrifugal pump is, for the first time, simulated using the moving particle semi-implicit method(MPS). The genetic smooth wall boundary(GSW) is used and extended to three dimension to deal with the complicated wall shape and thin blades in turbo-machines. The non-surface detection technique(NSD) based on conceptual particles is combined with this wall model to avoid the loss of particle number density near wall boundaries. A local wall particle refinement method is developed. Fine particles and coarse particles are applied to curved surface and flat surface respectively so as to reduce the computational cost while maintain the high discretization precision. The fully developed velocity inflow and pressure outflow boundary conditions are proposed. Three typical cases including hydrostatic cases with complicated geometry, flows over a two-dimensional backward-facing step, and flows in a three-dimensional tube are tested to verify the proposed models. This paper constructs a framework for the simulation of incompressible fluid machines, in which 3D complex revolving bodies can be integrally discretized and interactions between the stator and rotor can be integrally solved within a single coordinate. This paper provides a particle-based solver for incompressible fluid machinery and has the potential to study its inner flow with multiphase or phase change

SaliencyCut: Augmenting plausible anomalies for anomaly detection

Anomaly detection under the open-set scenario is a challenging task that requires learning discriminative features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models. Recent wisdom of augmentation methods focuses on generating random pseudo instances that may lead to a mixture of augmented instances with seen anomalies, or out of the typical range of anomalies. To address this issue, we propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies that tend to stay in the plausible range of anomalies. Furthermore, we deploy a two-head learning strategy consisting of normal and anomaly learning heads to learn the anomaly score of each sample. Theoretical analyses show that this mechanism offers a more tractable and tighter lower bound of the data log-likelihood. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances. Extensive experiments conducted on six real-world anomaly detection datasets demonstrate the superiority of our method to competing methods under various settings. Codes are available at: https://github.com/yjnanan/SaliencyCut

Acoustoelectric brain imaging with different conductivities and acoustic distributions

Objective: Acoustoelectric brain imaging (AEBI) is a promising imaging method for mapping brain biological current densities with high spatiotemporal resolution. Currently, it is still challenging to achieve human AEBI with an unclear acoustoelectric (AE) signal response of medium characteristics, particularly in conductivity and acoustic distribution. This study introduces different conductivities and acoustic distributions into the AEBI experiment, and clarifies the response interaction between medium characteristics and AEBI performance to address these key challenges.Approach: AEBI with different conductivities is explored by the imaging experiment, potential measurement, and simulation on a pig’s fat, muscle, and brain tissue. AEBI with different acoustic distributions is evaluated on the imaging experiment and acoustic field measurement through a deep and surface transmitting model built on a human skullcap and pig brain tissue.Main results: The results show that conductivity is not only inversely proportional to the AE signal amplitude but also leads to a higher AEBI spatial resolution as it increases. In addition, the current source and sulcus can be located simultaneously with a strong AE signal intensity. The transcranial focal zone enlargement, pressure attenuation in the deep-transmitting model, and ultrasound echo enhancement in the surface-transmitting model cause a reduced spatial resolution, FFT-SNR, and timing correlation of AEBI. Under the comprehensive effect of conductivity and acoustics, AEBI with skull finally shows reduced imaging performance for both models compared with no-skull AEBI. On the contrary, the AE signal amplitude decreases in the deep-transmitting model and increases in the surface-transmitting model.Significance: This study reveals the response interaction between medium characteristics and AEBI performance, and makes an essential step toward developing AEBI as a practical neuroimaging technique

Soil chemistry, metabarcoding, and metabolome analyses reveal that a sugarcane—Dictyophora indusiata intercropping system can enhance soil health by reducing soil nitrogen loss

IntroductionGreater amounts of fertilizer are applied every year to meet the growing demand for food. Sugarcane is one of the important food sources for human beings.MethodsHere, we evaluated the effects of a sugarcane—Dictyophora indusiata (DI) intercropping system on soil health by conducting an experiment with three different treatments: (1) bagasse application (BAS process), (2) bagasse + DI (DIS process), and (3) the control (CK). We then analyzed soil chemistry, the diversity of soil bacteria and fungi, and the composition of metabolites to clarify the mechanism underlying the effects of this intercropping system on soil properties.Results and discussionSoil chemistry analyses revealed that the content of several soil nutrients such as nitrogen (N) and phosphorus (P) was higher in the BAS process than in the CK. In the DIS process, a large amount of soil P was consumed by DI. At the same time, the urease activity was inhibited, thus slowing down the loss of soil in the DI process, while the activity of other enzymes such as β-glucosidase and laccase was increased. It was also noticed that the content of lanthanum and calcium was higher in the BAS process than in the other treatments, and DI did not significantly alter the concentrations of these soil metal ions. Bacterial diversity was higher in the BAS process than in the other treatments, and fungal diversity was lower in the DIS process than in the other treatments. The soil metabolome analysis revealed that the abundance of carbohydrate metabolites was significantly lower in the BAS process than in the CK and the DIS process. The abundance of D(+)-talose was correlated with the content of soil nutrients. Path analysis revealed that the content of soil nutrients in the DIS process was mainly affected by fungi, bacteria, the soil metabolome, and soil enzyme activity. Our findings indicate that the sugarcane–DIS intercropping system can enhance soil health

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

Mesendogen, a novel inhibitor of TRPM6, promotes mesoderm and definitive endoderm differentiation of human embryonic stem cells through alteration of magnesium homeostasis

The homo- and hetero-tetrameric channel complexes formed by transient receptor potential cation channel, subfamily M, member 6 (TRPM6) and 7 (TRPM7) (collectively referred to as TRPM6/TRPM7 channels in this study) are the major regulators of cellular magnesium uptake, yet the exact roles of TRPM6/TRPM7 channels and cellular magnesium homeostasis during development are poorly understood. Here, we report a novel small molecule Mesendogen (MEG) which robustly induces nearly homogeneous (≥85%) mesoderm and definitive endoderm (DE) differentiations of human embryonic stem cells (hESCs) in combination with growth factors. A kinome screen followed by loss-of-function experiments identified TRPM6 as the biological target of MEG. We demonstrated that MEG functions by inhibiting TRPM6/TRPM7 magnesium channel activity, as MEG reduced intracellular magnesium level, while TRPM6/TRPM7 channel modulation and magnesium-withdrawal phenocopied MEG at enhancing mesoderm and DE differentiations. This study discovers a robust chemical enhancer of hESC directed differentiation, and uncovers a novel regulatory role of cellular magnesium homeostasis during early embryonic cell fate specification