Optimal control of stochastic cylinder flow using data-driven compressive sensing method

A stochastic optimal control problem for incompressible Newtonian channel flow past a circular cylinder is used as a prototype optimal control problem for the stochastic Navier-Stokes equations. The inlet flow and the rotation speed of the cylinder are allowed to have stochastic perturbations. The control acts on the cylinder via adjustment of the rotation speed. Possible objectives of the control include, among others, tracking a desired (given) velocity field or minimizing the kinetic energy, enstrophy, or the drag of the flow over a given body. Owing to the high computational requirements, the direct application of the classical Monte Carlo methods for our problem is limited. To overcome the difficulty, we use a multi-fidelity data-driven compressive sensing based polynomial chaos expansions (MDCS-PCE). An effective gradient-based optimization for the discrete optimality systems resulted from the MDCS-PCE discretization is developed. The strategy can be applied broadly to many stochastic flow control problems. Numerical tests are performed to validate our methodology

Clustering based Multiple Anchors High-Dimensional Model Representation

In this work, a cut high-dimensional model representation (cut-HDMR) expansion based on multiple anchors is constructed via the clustering method. Specifically, a set of random input realizations is drawn from the parameter space and grouped by the centroidal Voronoi tessellation (CVT) method. Then for each cluster, the centroid is set as the reference, thereby the corresponding zeroth-order term can be determined directly. While for non-zero order terms of each cut-HDMR, a set of discrete points is selected for each input component, and the Lagrange interpolation method is applied. For a new input, the cut-HDMR corresponding to the nearest centroid is used to compute its response. Numerical experiments with high-dimensional integral and elliptic stochastic partial differential equation as backgrounds show that the CVT based multiple anchors cut-HDMR can alleviate the negative impact of a single inappropriate anchor point, and has higher accuracy than the average of several expansions

Y27, a novel derivative of 4-hydroxyquinoline-3-formamide, prevents the development of murine systemic lupus erythematosus-like diseases in MRL/lpr autoimmune mice and BDF1 hybrid mice

INTRODUCTION: Naturally occurring CD4(+)CD25(+ )regulatory T (Treg) cells are central to the maintenance of peripheral tolerance. Impaired activity and/or a lower frequency of these cells lead to systemic lupus erythematosus (SLE). Manipulating the number or activity of Treg cells is to be a promising strategy in treating it and other autoimmune diseases. We have examined the effects of Y27, a novel derivative of 4-hydroxyquinoline-3-formamide, on SLE-like symptoms in MRL/lpr autoimmune mice and BDF1 hybrid mice. Whether the beneficial effect of Y27 involves modulation of CD4(+)CD25(+ )Treg cells has also been investigated. METHODS: Female MRL/lpr mice that spontaneously develop lupus were treated orally by gavage with Y27 for 10 weeks, starting at 10 weeks of age. BDF1 mice developed a chronic graft-versus-host disease (GVHD) by two weekly intravenous injections of parental female DBA/2 splenic lymphocytes, characterized by immunocomplex-mediated glomerulonephritis resembling SLE. Y27 was administered to chronic GVHD mice for 12 weeks. Nephritic symptoms were monitored and the percentage of CD4(+)CD25(+)FoxP3(+ )Treg peripheral blood leukocyte was detected with mouse regulatory T cell staining kit by flowcytometry. Purified CD4(+)CD25(+ )Tregs were assessed for immune suppressive activity using the mixed lymphocyte reaction. RESULTS: The life-span of MRL/lpr mice treated with Y27 for 10 weeks was significantly prolonged, proteinuria and renal lesion severity were ameliorated, and blood urea nitrogen, triglyceride and serum anti-double-stranded DNA antibodies were decreased. Similar results were found in chronic GVHD mice. Administration of Y27 had little impact on percentage of the peripheral blood lymphocyte CD4(+)CD25(+)Foxp3(+ )Treg cells in both groups of mice. In contrast, the suppressive capacity of CD4(+)CD25(+ )Treg cells in splenocytes was markedly augmented in Y27-treated mice ex vivo. CONCLUSIONS: Experimental evidence of the protect effects of Y27 against autoimmune nephritis has been shown. The mechanism may involve enhancement of the suppressive capacity of CD4(+)CD25(+ )Treg cells

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

CD64 plays a key role in diabetic wound healing

IntroductionWound healing poses a clinical challenge in diabetes mellitus (DM) due to compromised host immunity. CD64, an IgG-binding Fcgr1 receptor, acts as a pro-inflammatory mediator. While its presence has been identified in various inflammatory diseases, its specific role in wound healing, especially in DM, remains unclear.ObjectivesWe aimed to investigate the involvement of CD64 in diabetic wound healing using a DM animal model with CD64 KO mice.MethodsFirst, we compared CD64 expression in chronic skin ulcers from human DM and non-DM skin. Then, we monitored wound healing in a DM mouse model over 10 days, with or without CD64 KO, using macroscopic and microscopic observations, as well as immunohistochemistry.ResultsCD64 expression was significantly upregulated (1.25-fold) in chronic ulcerative skin from DM patients compared to non-DM individuals. Clinical observations were consistent with animal model findings, showing a significant delay in wound healing, particularly by day 7, in CD64 KO mice compared to WT mice. Additionally, infiltrating CD163+ M2 macrophages in the wounds of DM mice decreased significantly compared to non-DM mice over time. Delayed wound healing in DM CD64 KO mice correlated with the presence of inflammatory mediators.ConclusionCD64 seems to play a crucial role in wound healing, especially in DM conditions, where it is associated with CD163+ M2 macrophage infiltration. These data suggest that CD64 relies on host immunity during the wound healing process. Such data may provide useful information for both basic scientists and clinicians to deal with diabetic chronic wound healing

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability; (b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p<0.001). In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.Comment: 23 pages, 16 figure

Multivalent bicyclic peptides are an effective antiviral modality that can potently inhibit SARS-CoV-2.

COVID-19 has stimulated the rapid development of new antibody and small molecule therapeutics to inhibit SARS-CoV-2 infection. Here we describe a third antiviral modality that combines the drug-like advantages of both. Bicycles are entropically constrained peptides stabilized by a central chemical scaffold into a bi-cyclic structure. Rapid screening of diverse bacteriophage libraries against SARS-CoV-2 Spike yielded unique Bicycle binders across the entire protein. Exploiting Bicycles' inherent chemical combinability, we converted early micromolar hits into nanomolar viral inhibitors through simple multimerization. We also show how combining Bicycles against different epitopes into a single biparatopic agent allows Spike from diverse variants of concern (VoC) to be targeted (Alpha, Beta, Delta and Omicron). Finally, we demonstrate in both male hACE2-transgenic mice and Syrian golden hamsters that both multimerized and biparatopic Bicycles reduce viraemia and prevent host inflammation. These results introduce Bicycles as a potential antiviral modality to tackle new and rapidly evolving viruses