Disentangled Multi-Fidelity Deep Bayesian Active Learning

To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. However, existing approaches based on Gaussian processes are hardly scalable to high-dimensional data. Deep learning-based methods often impose a hierarchical structure in hidden representations, which only supports passing information from low-fidelity to high-fidelity. These approaches can lead to the undesirable propagation of errors from low-fidelity representations to high-fidelity ones. We propose a novel framework called Disentangled Multi-fidelity Deep Bayesian Active Learning (D-MFDAL), that learns the surrogate models conditioned on the distribution of functions at multiple fidelities. On benchmark tasks of learning deep surrogates of partial differential equations including heat equation, Poisson's equation and fluid simulations, our approach significantly outperforms state-of-the-art in prediction accuracy and sample efficiency. Our code is available at https://github.com/Rose-STL-Lab/Multi-Fidelity-Deep-Active-Learning

Accelerating Stochastic Simulation with Interactive Neural Processes

Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. We propose Interactive Neural Process (INP), a Bayesian active learning framework to proactively learn a deep learning surrogate model and accelerate simulation. Our framework is based on the novel integration of neural process, deep sequence model and active learning. In particular, we develop a novel spatiotemporal neural process model to mimic the simulator dynamics. Our model automatically infers the latent process which describes the intrinsic uncertainty of the simulator. This also gives rise to a new acquisition function based on the latent information gain. We design Bayesian active learning algorithms to iteratively query the simulator, gather more data, and continuously improve the model. We perform theoretical analysis and demonstrate that our approach reduces sample complexity compared with random sampling in high dimension. Empirically, we demonstrate our framework can faithfully imitate the behavior of a complex infectious disease simulator with a small number of examples, enabling rapid simulation and scenario exploration

Atorvastatin Combining with Probucol: A New Way to Reduce Serum Uric Acid Level during Perioperative Period of Interventional Procedure

Uric acid has ever been considered as one of contrast induced acute kidney injury’s risk factors. Atorvastatin and probucol can both improve contrast induced acute kidney injury separately. This prospective study is to assess their effect on reducing serum uric acid level and contrast induced acute kidney injury during perioperative period of interventional procedure. On the basis of different doses of atorvastatin and probucol, 208 cases admitted for coronary angiography or percutaneous coronary intervention were randomly classified into standard combined group (S-C group), intensive combined group (I-C group), and intensive atorvastatin group (I-A group). Patients’ blood urea nitrogen, serum creatinine, and serum uric acid were measured and estimated glomerular filtration rate was evaluated 24 hours before and after the procedure. After procedure, blood urea nitrogen in all the three groups decreased; Scr of S-C group and I-A group increased significantly, while estimated glomerular filtration decreased in the S-C group (P<0.05); serum uric acid in S-C group and I-C group decreased significantly (P<0.05). Combination treatment of atorvastatin and probucol before intervention could reduce perioperative serum uric acid level; meanwhile, the intensive combined treatment can improve the contrast induced acute kidney injury. The result was the same for hypertensive patients

Csk, a Critical Link of G Protein Signals to Actin Cytoskeletal Reorganization

AbstractHeterotrimeric G proteins can signal to reorganize the actin cytoskeleton, but the mechanism is unclear. Here we report that, in tyrosine kinase Csk-deficient mouse embryonic fibroblast cells, G protein (Gβγ, Gα12, Gα13, and Gαq)-induced, and G protein-coupled receptor-induced, actin stress fiber formation was completely blocked. Reintroduction of Csk into Csk-deficent cells restored the G protein-induced actin stress fiber formation. Chemical rescue experiments with catalytic mutants of Csk demonstrated that the catalytic activity of Csk was required for this process. Furthermore, we uncovered that Gβγ can both translocate Csk to the plasma membrane and directly increase Csk kinase activity. Our genetic and biochemical studies demonstrate that Csk plays a critical role in mediating G protein signals to actin cytoskeletal reorganization

High-temperature modification of steel slag using composite modifier containing silicon calcium slag, fly ash, and reservoir sediment

Steel slag (SS) is a kind of industrial solid waste, and its accumulation brings certain harm to the ecological environment. In order to promote the building material utilization of SS, high-temperature modification (HTM) of SS is performed using a composite modifier (CMSFR) containing silicon calcium slag (SCS), fly ash (FA), and reservoir sediment (RS). Then, the authors investigated the effect of CMSFR on the cementitious properties and volume soundness of SS mixture after HTM (SMHTM). After that, the mineral composition and microstructure of SMHTM were investigated through X-ray fluorescence analysis (XRF), X-ray diffraction (XRD), scanning electronic microscopy (SEM), energy dispersive spectrometry (EDS), and particle size analysis. It was found that the free CaO (f-CaO) content obviously decreased, and the cementitious properties improved in SMHTM. When the CMSFR content was 20% (SCS: FA: RS = 9:7:4), and the modification temperature (MT) was 1,250°C, the mass fraction of f-CaO in SMHTM dropped from 4.81% to 1.90%, down by 60.5%; the 28-day activity index of SMHTM increased to 85.4%, 14.3% higher than that of raw SS, which meets the technical requirement of Steel slag powder used for cement and concrete (GB/T 20491-2017): the activity index of grade I SS powder must be greater than or equal to 80%. As the mass fraction of CMSFR grew from 10% to 30%, new mineral phases formed in SMHTM, including diopside (CMS2), ceylonite (MgFe2O4), gehlenite (C2AS), tricalcium aluminate (C3A), and magnetite (Fe3O4). The HTM with CMSFR promotes the decomposition of RO phase (a continuous solid solution composed of divalent metal oxides like FeO, MgO, MnO, and CaO) in raw SS, turning the FeO in that phase into Fe3O4. The above results indicate that the SMHTM mixed with CMSFR can be applied harmless in cement and concrete, making low-energy fine grinding of SS a possibility

Generation of Functional CLL-Specific Cord Blood CTL Using CD40-Ligated CLL APC

PMCID: PMC3526610This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Transcriptome Analysis of the Model Protozoan, Tetrahymena thermophila, Using Deep RNA Sequencing

Background: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST) database limited the quality of the original genome annotation. Methodology/Principal Findings: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96 % of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs) and an updated (larger) size estimate of the T. thermophila transcriptome: 57 Mb, or about 55 % of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS) events distributed over 5.2 % of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8 % of the genes originally predicted by the gene finder, to correct coding sequence boundaries an

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations