Modeling of CH4-assisted SOEC for H2O/CO2 co-electrolysis

This research was supported by a grant of SFC/RGC Joint Research Scheme (X-PolyU/501/14) from Research Grant Council, University Grants Committee, Hong Kong SAR.Co-electrolysis of H2O and CO2 in a solid oxide electrolysis cell (SOEC) is promising for simultaneous energy storage and CO2 utilization. Fuel-assisted H2O electrolysis by SOEC (SOFEC) has been demonstrated to be effective in reducing power consumption. In this paper, the effects of fuel (i.e. CH4) assisting on CO2/H2O co-electrolysis are numerically studied using a 2D model. The model is validated with the experimental data for CO2/H2O co-electrolysis. One important finding is that the CH4 assisting is effective in lowering the equilibrium potential of SOEC thus greatly reduces the electrical power consumption for H2O/CO2 co-electrolysis. The performance of CH4-assisted SOFEC increases substantially with increasing temperature, due to increased reaction kinetics of electrochemical reactions and CH4 reforming reaction. The CH4-assisted SOFEC can generate electrical power and syngas simultaneously at a low current density of less than 600 Am−2 and at 1123 K. In addition, different from conventional SOEC whose performance weakly depends on the anode gas flow rate, the CH4-assisted SOFEC performance is sensitive to the anode gas flow rate (i.g. peak current density is achieved at an anode flow rate of 70 SCCM at 1073 K). The model can be used for subsequent design optimization of SOFEC to achieve high performance energy storage.PostprintPeer reviewe

Modelling of a hybrid system for on-site power generation from solar fuels

201906 bcmaVersion of RecordPublishe

Baicalin Normalizes Blood Glucose Level in Streptozotocin -induced Diabetic Rats

This study aimed to determine the effect of baicalin on insulin resistance, glucose absorption, and blood lipids in type 2 diabetic rat model. Diabetic rats were treated with baicalin (40, 80 mg/kg) for 40 days. The results showed that diabetic rats treated with baicalin resulted in a significant decrease in the concentration of plasma triglycerides and high-density lipoprotein cholesterol, improved the body weight. Furthermore, baicalin markedly decreased blood glucose level in the diabetic rats. The levels of plasma insulin and resistin exhibited significantly lower in the diabetic rats treated with baicalin than those of the model group. These findings suggest that baicalin can improve adipose metabolic disturbance in the experimental type 2 diabetic rats, can effectively ameliorate insulin resistance and plasma glucose transport by decreasing the levels of plasma resistin.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Expanding Language-Image Pretrained Models for General Video Recognition

Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable "zero-shot" generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem. In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios. In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using 12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited. Code and models are available at https://aka.ms/X-CLIPComment: Accepted by ECCV2022, Ora

Baicalin Normalizes Blood Glucose Level in Streptozotocin -induced Diabetic Rats

This study aimed to determine the effect of baicalin on insulin resistance, glucose absorption, and blood lipids in type 2 diabetic rat model. Diabetic rats were treated with baicalin (40, 80 mg/kg) for 40 days. The results showed that diabetic rats treated with baicalin resulted in a significant decrease in the concentration of plasma triglycerides and high-density lipoprotein cholesterol, improved the body weight. Furthermore, baicalin markedly decreased blood glucose level in the diabetic rats. The levels of plasma insulin and resistin exhibited significantly lower in the diabetic rats treated with baicalin than those of the model group. These findings suggest that baicalin can improve adipose metabolic disturbance in the experimental type 2 diabetic rats, can effectively ameliorate insulin resistance and plasma glucose transport by decreasing the levels of plasma resistin.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations

Stochastic partial differential equations (SPDEs) are crucial for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly popular. However, SPDEs have two unique properties that require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over both initial conditions and the random sampled forcing term. Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white noise). To deal with the problems, in this work, we design a deep neural network called Deep Latent Regularity Net (DLR-Net). DLR-Net includes a regularity feature block as the main component, which maps the initial condition and the random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to represent the SPDE solution. The regularity features are then fed into a small backbone neural operator to get the output. We conduct experiments on various SPDEs including the dynamic Φ^{4}_{1} model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that the proposed DLR-Net can achieve SOTA accuracy compared with the baselines. Moreover, the inference time is over 20 times faster than the traditional numerical solver and is comparable with the baseline deep learning models

Influence of synthetic superparamagnetic iron oxide on dendritic cells

Yongbin Mou1, Baoan Chen2, Yu Zhang3, Yayi Hou4, Hao Xie4, Guohua Xia2, Meng Tang5, Xiaofeng Huang1, Yanhong Ni1, Qingang Hu1,6 1Central Laboratory of Stomatology, Stomatological Hospital Affiliated Medical School, Nanjing University, 2Department of Hematology, Zhongda Hospital, Medical School, Southeast University, 3State Key Laboratory of Bioelectronics, Jiangsu Key Laboratory for Biomaterials and Devices, Southeast University, 4Immunology and Reproductive Biology Laboratory, Medical School, Nanjing University, 5Laboratory of Environmental Medicine and Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, People's Republic of China; 6Leeds Dental Institute, Faculty of Medicine and Health, University of Leeds, Leeds, UK Background: This study investigated the influence of synthetic superparamagnetic iron oxide (SPIO) on dendritic cells and provides a possible method for labeling these cells. Methods: SPIO nanoparticles were prepared, and their morphology and magnetic properties were characterized. The particles were endocytosed by dendritic cells generated from mouse bone marrow. Labeling efficiency and cellular uptake were analyzed by Prussian blue staining and quantitative spectrophotometric assay. Meanwhile, the surface molecules, cellular apoptosis, and functional properties of the SPIO-labeled dendritic cells were explored by flow cytometry and the mixed lymphocyte reaction assay. Results: The synthetic nanoparticles possessed a spherical shape and good superparamagnetic behavior. The mean concentration of iron in immature and mature dendritic cells was 31.8 ± 0.7 µg and 35.6 ± 1.0 µg per 1 × 106 cells, respectively. After 12 hours of incubation with SPIO at a concentration of 25 µg/mL, nearly all cells were shown to contain iron. Interestingly, cellular apoptosis and surface expression of CD80, CD86, major histocompatibility II, and chemokine receptor 7 in mature dendritic cells were not affected to any significant extent by SPIO labeling. T cell activation was maintained at a low ratio of dendritic cells to T cells. Conclusion: SPIO nanoparticles have good superparamagnetic behavior, highly biocompatible characteristics, and are suitable for use in further study of the migratory behavior and biodistribution of dendritic cells in vivo. Keywords: superparamagnetic iron oxide, dendritic cell, cell labelin

Isolation and in Vitro Probiotic Characteristics of Akkermansia muciniphila from Maternal and Infant Feces in Three Different Regions

In this study, a combination of an improved mucin enriched medium with real-time polymerase chain reaction (real-time PCR) was used to test 48 samples of maternal and infant feces for Akkermansia muciniphila (Akk). Under optimized conditions, 24 Akk strains were isolated from eight positive samples. All these strains were confirmed as Akk by 16S rRNA gene sequencing and PCR with Akk-specific primers. Repetitive extragenic palindrome-polymerase chain reaction (rep-PCR) fingerprinting classified the 24 strains into four genotypic groups. Subsequently, these strains were tested in vitro for simulated gastrointestinal fluid tolerance, hydrophobicity, antibiotic susceptibility, and glycan utilization capacity. The results showed that strains HN18D-1, HN18D-3, and WW48D1-13 had the highest tolerance to simulated gastric and intestinal fluids. All Akk strains were resistant to vancomycin, clindamycin, kanamycin and erythromycin. Xylooligosaccharides and soybean oligosaccharides had prebiotic effects on the Akk strains. Collectively, Akk isolates HN18D-1, HN18D-3 and WW48D1-13 can be used as potential probiotic candidates for subsequent in-depth studies

Coordinated regulation of core and accessory genes in the multipartite genome of Sinorhizobium fredii

Prokaryotes benefit from having accessory genes, but it is unclear how accessory genes can be linked with the core regulatory network when developing adaptations to new niches. Here we determined hierarchical core/accessory subsets in the multipartite pangenome (composed of genes from the chromosome, chromid and plasmids) of the soybean microsymbiont Sinorhizobium fredii by comparing twelve Sinorhizobium genomes. Transcriptomes of two S. fredii strains at mid-log and stationary growth phases and in symbiotic conditions were obtained. The average level of gene expression, variation of expression between different conditions, and gene connectivity within the co-expression network were positively correlated with the gene conservation level from strain-specific accessory genes to genus core. Condition-dependent transcriptomes exhibited adaptive transcriptional changes in pangenome subsets shared by the two strains, while strain-dependent transcriptomes were enriched with accessory genes on the chromid. Proportionally more chromid genes than plasmid genes were co-expressed with chromosomal genes, while plasmid genes had a higher within-replicon connectivity in expression than chromid ones. However, key nitrogen fixation genes on the symbiosis plasmid were characterized by high connectivity in both within- and between-replicon analyses. Among those genes with host-specific upregulation patterns, chromosomal znu and mdt operons, encoding a conserved high-affinity zinc transporter and an accessory multi-drug efflux system, respectively, were experimentally demonstrated to be involved in host-specific symbiotic adaptation. These findings highlight the importance of integrative regulation of hierarchical core/accessory components in the multipartite genome of bacteria during niche adaptation and in shaping the prokaryotic pangenome in the long run