DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware

Experimental Study of Diffusion and Formation Mineral Change in Supercritical CO2 Huff and Puff Process of Shale Reservoir

AbstractIn order to understand the diffusion during CO2 huff and puff in the development of shale oil and its influence on the formation, expansion and viscosity reduction experiments of shale oil-CO2 system, CO2 extraction experiments, and CO2 huff and puff physical simulation experiments were conducted. The diffusion characteristics of CO2 during huff and puff and their effects on formation minerals were studied by chromatographic analysis and X-ray diffraction analysis of artificially fractured natural cores. Research indicates that CO2 huff and puff technology is an effective method to enhance the recovery of shale reservoirs after fracturing. By injecting CO2, the light components of shale oil can be effectively extracted; when the amount of injected CO2 is 50%, the saturation pressure of shale oil increases to 27.72 MPa, and the expansion coefficient increases by 27.9%, the viscosity reduction rate of shale oil can reach 58.97%, and the density reduction rate is 10.02%; under the soaking well pressure of 50 MPa, when 0.5PVCO2 was injected and the well stuffed for 8 hours, the CO2 was fully dissolved in the shale oil, and the continuous increase of the injection slug had a little effect on the CO2 diffusion. During the CO2 huff and puff process, CO2 would dissolve in the formation water and fracturing fluid and reacts with dolomite in the reservoir rock, consuming a large amount of dolomite in the reservoir, and the dolomite mineral content of core sample decreased from 30.1% to 2.6%

Biomimetic nanotherapies: red blood cell based core-shell structured nanocomplexes for atherosclerosis management

Cardiovascular disease is the leading cause of mortality worldwide. Atherosclerosis, one of the most common forms of the disease, is characterized by a gradual formation of atherosclerotic plaque, hardening, and narrowing of the arteries. Nanomaterials can serve as powerful delivery platforms for atherosclerosis treatment. However, their therapeutic efficacy is substantially limited in vivo due to nonspecific clearance by the mononuclear phagocytic system. In order to address this limitation, rapamycin (RAP)‐loaded poly(lactic‐co‐glycolic acid) (PLGA) nanoparticles are cloaked with the cell membrane of red blood cells (RBCs), creating superior nanocomplexes with a highly complex functionalized bio‐interface. The resulting biomimetic nanocomplexes exhibit a well‐defined “core–shell” structure with favorable hydrodynamic size and negative surface charge. More importantly, the biomimetic nature of the RBC interface results in less macrophage‐mediated phagocytosis in the blood and enhanced accumulation of nanoparticles in the established atherosclerotic plaques, thereby achieving targeted drug release. The biomimetic nanocomplexes significantly attenuate the progression of atherosclerosis. Additionally, the biomimetic nanotherapy approach also displays favorable safety properties. Overall, this study demonstrates the therapeutic advantages of biomimetic nanotherapy for atherosclerosis treatment, which holds considerable promise as a new generation of drug delivery system for safe and efficient management of atherosclerosis

Stackelberg game-based three-stage optimal pricing and planning strategy for hybrid shared energy storage

Inspired from sharing economy and advanced energy storage technologies, hybrid shared energy storage (HSES), as an innovative business model, can provide flexible storage leasing services to new energy stations (NESs) and bring additional profits to the energy storage owner. Under this business model, pricing and planning issues are the main focus of the HSES operator to increase revenues but are rarely considered in current studies. Therefore, a Stackelberg game-based three-stage optimal pricing and planning strategy of HSES is formulated for the operator. First, an HSES model considering two leasing options is developed to provide two kinds of short-term use rights of energy storage resources for NESs. Then, the interactions between selfish NESs and the HSES operator are characterized as a Stackelberg game, and a bi-level pricing and planning strategy optimization model is developed to help the HSES operator make optimal decisions. Finally, considering different characteristics in each stage of the Stackelberg game, a three-stage solution method based on the genetic algorithm (GA) and mixed-integer linear programming (MILP) models is proposed to solve the optimization problem. Case studies on six NESs in a certain region are taken to verify the effectiveness of the proposed strategy. Simulation results show that the HSES operator can obtain maximum profit under the proposed pricing and planning strategy. In addition, the proposed HSES leasing model can provide additional benefits to both the operator and NESs

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease

Alzheimer’s disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD

Corrigendum: eIF3a Regulation of NHEJ Repair Protein Synthesis and Cellular Response to Ionizing Radiation

[This corrects the article DOI: 10.3389/fcell.2020.00753.]

eIF3a Regulation of NHEJ Repair Protein Synthesis and Cellular Response to Ionizing Radiation

Translation initiation in protein synthesis regulated by eukaryotic initiation factors (eIFs) is a crucial step in controlling gene expression. eIF3a has been shown to regulate protein synthesis and cellular response to treatments by anticancer agents including cisplatin by regulating nucleotide excision repair. In this study, we tested the hypothesis that eIF3a regulates the synthesis of proteins important for the repair of double-strand DNA breaks induced by ionizing radiation (IR). We found that eIF3a upregulation sensitized cellular response to IR while its downregulation caused resistance to IR. eIF3a increases IR-induced DNA damages and decreases non-homologous end joining (NHEJ) activity by suppressing the synthesis of NHEJ repair proteins. Furthermore, analysis of existing patient database shows that eIF3a expression associates with better overall survival of breast, gastric, lung, and ovarian cancer patients. These findings together suggest that eIF3a plays an important role in cellular response to DNA-damaging treatments by regulating the synthesis of DNA repair proteins and, thus, eIIF3a likely contributes to the outcome of cancer patients treated with DNA-damaging strategies including IR

Macrophage membrane functionalized biomimetic nanoparticles for targeted anti-atherosclerosis applications

Atherosclerosis (AS), the underlying cause of most cardiovascular events, is one of the most common causes of human morbidity and mortality worldwide due to the lack of an efficient strategy for targeted therapy. In this work, we aimed to develop an ideal biomimetic nanoparticle for targeted AS therapy. Methods: Based on macrophage “homing” into atherosclerotic lesions and cell membrane coating nanotechnology, biomimetic nanoparticles (MM/RAPNPs) were fabricated with a macrophage membrane (MM) coating on the surface of rapamycin-loaded poly (lactic-co-glycolic acid) copolymer (PLGA) nanoparticles (RAPNPs). Subsequently, the physical properties of the MM/RAPNPs were characterized. The biocompatibility and biological functions of MM/RAPNPs were determined in vitro. Finally, in AS mouse models, the targeting characteristics, therapeutic efficacy and safety of the MM/RAPNPs were examined. Results: The advanced MM/RAPNPs demonstrated good biocompatibility. Due to the MM coating, the nanoparticles effectively inhibited the phagocytosis by macrophages and targeted activated endothelial cells in vitro. In addition, MM-coated nanoparticles effectively targeted and accumulated in atherosclerotic lesions in vivo. After a 4-week treatment program, MM/RAPNPs were shown to significantly delay the progression of AS. Furthermore, MM/RAPNPs displayed favorable safety performance after long-term administration. Conclusion: These results demonstrate that MM/RAPNPs could efficiently and safely inhibit the progression of AS. These biomimetic nanoparticles may be potential drug delivery systems for safe and effective anti-AS applications

Targeted immunotherapy for HER2-low breast cancer with 17p loss

The clinical challenge for treating HER2 (human epidermal growth factor receptor 2)-low breast cancer is the paucity of actionable drug targets. HER2-targeted therapy often has poor clinical efficacy for this disease due to the low level of HER2 protein on the cancer cell surface. We analyzed breast cancer genomics in the search for potential drug targets. Heterozygous loss of chromosome 17p is one of the most frequent genomic events in breast cancer, and 17p loss involves a massive deletion of genes including the tumor suppressor TP53 Our analyses revealed that 17p loss leads to global gene expression changes and reduced tumor infiltration and cytotoxicity of T cells, resulting in immune evasion during breast tumor progression. The 17p deletion region also includes POLR2A, a gene encoding the catalytic subunit of RNA polymerase II that is essential for cell survival. Therefore, breast cancer cells with heterozygous loss of 17p are extremely sensitive to the inhibition of POLR2A via a specific small-molecule inhibitor, α-amanitin. Here, we demonstrate that α-amanitin-conjugated trastuzumab (T-Ama) potentiated the HER2-targeted therapy and exhibited superior efficacy in treating HER2-low breast cancer with 17p loss. Moreover, treatment with T-Ama induced immunogenic cell death in breast cancer cells and, thereby, delivered greater efficacy in combination with immune checkpoint blockade therapy in preclinical HER2-low breast cancer models. Collectively, 17p loss not only drives breast tumorigenesis but also confers therapeutic vulnerabilities that may be used to develop targeted precision immunotherapy