Forward and inverse problems for Eikonal equation based on DeepONet

Seismic forward and inverse problems are significant research areas in geophysics. However, the time burden of traditional numerical methods hinders their applications in scenarios that require fast predictions. Machine learning-based methods also have limitations as retraining is required for every change in initial conditions. In this letter, we adopt deep operator network (DeepONet) to solve forward and inverse problems based on the Eikonal equation, respectively. DeepONet approximates the operator through two sub-networks, branch net and trunk net, which offers good generalization and flexibility. Different structures of DeepONets are proposed to respectively learn the operators in forward and inverse problems. We train the networks on different categories of datasets separately, so that they can deliver accurate predictions with different initial conditions for the specific velocity model. The numerical results demonstrate that DeepONet can not only predict the travel time fields with different sources for different velocity models, but also provide velocity models based on the observed travel time data.Comment: 5 pages, 4 figure

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version

Diaphanous-related formin mDia2 regulates beta2 integrins to control hematopoietic stem and progenitor cell engraftment

Bone marrow engraftment of haematopoietic stem and progenitor cells (HSPCs) requires homing and lodgement to the niche. Here, the authors show that mDia2 is required for HSPC polarization, nuclear MAL, and SRF-induced beta2 integrin expression during transendothelial migration of HSPCs required for engraftment

The Alterations of Gut Microbiome and Lipid Metabolism in Patients with Spinal Muscular Atrophy

Abstract Introduction Spinal muscular atrophy (SMA) can cause multiple system dysfunction, especially lipid metabolic disorders, for which management strategies are currently lacking. Microbes are related to metabolism and the pathogenesis of neurological diseases. This study aimed to preliminarily explore the alterations in the gut microbiota in SMA and the potential relationship between altered microbiota and lipid metabolic disorders. Methods Fifteen patients with SMA and 17 gender- and age-matched healthy controls were enrolled in the study. Feces and fasting plasma samples were collected. 16S ribosomal RNA sequencing and nontargeted metabolomics analysis were performed to explore the correlation between microbiota and differential lipid metabolites. Results No significant difference was found in microbial diversity (α- and β-diversity) between the SMA and control groups, with both groups having a relatively similar community structure. However, compared to the control group, the SMA group showed an increased relative abundance of the genera Ruminiclostridium, Gordonibacter, Enorma, Lawsonella, Frisingicoccus, and Anaerofilum and a decreased abundance of the genera Catabacter, Howardella, Marine_Methylotrophic_Group_3, and Lachnospiraceae_AC2044_group. The concurrent metabolomic analysis showed that the SMA group had 56 different kinds of lipid metabolite levels than did the control group. Additionally, the Spearman correlation suggested a correlation between the altered differential lipid metabolites and the above-mentioned altered microbiota. Conclusions The gut microbiome and lipid metabolites differed between the patients with SMA and the control subjects. The altered microbiota may be related with the lipid metabolic disorders in SMA. However, further study is necessary to clarify the mechanism of lipid metabolic disorders and develop management strategies to improve the related complications in SMA

Designable Immune Therapeutical Vaccine System Based on DNA Supramolecular Hydrogels

Immunotherapy is believed to be an ideal method to treat cancer because it can break the immunotolerance of tumor and induce robust immunoresponse. However, constructing a wide antigen-adaptive, easy-handling, and biodegradable system that can recruit and activate antigen-presenting cells (APCs) much effectively is still a challenge. Herein, we show an injectable DNA supramolecular hydrogel vaccine (DSHV) system which could efficiently recruit and activate APCs in vitro and in vivo. The in vitro processes have been visualized by fluorescence microscopy. Through intraperitoneal or subcutaneous injection, the DSHV system can mimic the function of a lymph node where the APCs are recruited and activated by the high local concentration of cytosine-phosphate-guanine. Subsequently, strong immune response and obvious antitumor effects have been obtained. Our findings demonstrated that the DSHV system could serve as a general platform for tumor vaccination and benefit the personalized cancer therapy in the near future

Designable Immune Therapeutical Vaccine System Based on DNA Supramolecular Hydrogels

Immunotherapy is believed to be an ideal method to treat cancer because it can break the immunotolerance of tumor and induce robust immunoresponse. However, constructing a wide antigen-adaptive, easy-handling, and biodegradable system that can recruit and activate antigen-presenting cells (APCs) much effectively is still a challenge. Herein, we show an injectable DNA supramolecular hydrogel vaccine (DSHV) system which could efficiently recruit and activate APCs in vitro and in vivo. The in vitro processes have been visualized by fluorescence microscopy. Through intraperitoneal or subcutaneous injection, the DSHV system can mimic the function of a lymph node where the APCs are recruited and activated by the high local concentration of cytosine-phosphate-guanine. Subsequently, strong immune response and obvious antitumor effects have been obtained. Our findings demonstrated that the DSHV system could serve as a general platform for tumor vaccination and benefit the personalized cancer therapy in the near future