Synergy between type 1 fimbriae expression and C3 opsonisation increases internalisation of E. coli by human tubular epithelial cells

<p>Abstract</p> <p>Background</p> <p>Bacterial infection of the urinary tract is a common clinical problem with <it>E. coli </it>being the most common urinary pathogen. Bacterial uptake into epithelial cells is increasingly recognised as an important feature of infection. Bacterial virulence factors, especially fimbrial adhesins, have been conclusively shown to promote host cell invasion. Our recent study reported that C3 opsonisation markedly increases the ability of <it>E. coli </it>strain J96 to internalise into human proximal tubular epithelial cells via CD46, a complement regulatory protein expressed on host cell membrane. In this study, we further assessed whether C3-dependent internalisation by human tubular epithelial cells is a general feature of uropathogenic <it>E. coli </it>and investigated features of the bacterial phenotype that may account for any heterogeneity.</p> <p>Results</p> <p>In 31 clinical isolates of <it>E. coli </it>tested, C3-dependent internalisation was evident in 10 isolates. Type 1 fimbriae mediated-binding is essential for C3-dependent internalisation as shown by phenotypic association, type 1 fimbrial blockade with soluble ligand (mannose) and by assessment of a type 1 fimbrial mutant.</p> <p>Conclusion</p> <p>we propose that efficient internalisation of uropathogenic <it>E. coli </it>by the human urinary tract depends on co-operation between type 1 fimbriae-mediated adhesion and C3 receptor -ligand interaction.</p

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of the optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take plate microreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The Emax of 5% for plate microreactor and Emax of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a reference method for methanol steam reforming performance optimisation for hydrogen production

Development of cylindrical laminated methanol steam reforming microreactor with cascading metal foams as catalyst support

In this study, the cascading metal foams were used as catalyst supports for constructing a new type of cylindrical laminated methanol steam reforming microreactor for hydrogen production. The two-layer impregnation method was used to load the Cu/Zn/Al/Zr catalysts, and the ultrasonic vibration method was then employed to investigate the loading performance of metal foams with different types and thicknesses. Furthermore, the effect of the type of catalyst placement, pores per inch (PPI) and foam type on the performance of methanol steam reforming microreactor was studied by varying the gas hourly space velocity (GHSV) and reaction temperature. Compared with two other types of catalyst placement studied, the microreactor containing catalyst-loaded metal foams without clearance cascading (3 × 2) showed the highest hydrogen production performance. When the PPI of the metal foam was increased from 50 to 100, both the methanol conversion and the H2 flow rate gradually increased. Our results also showed that a microreactor with Cu foam as a catalyst support exhibits increased hydrogen production and higher stability than those of a microreactor with Ni foam

A Benchmark for Accurate GPCR Ligand Binding Affinity Prediction with Free Energy Perturbation

G protein-coupled receptors (GPCRs) are among the most important drug targets in the pharmaceutical industry. Free energy perturbation (FEP), which can accurately predict the relative binding free energies of drug molecules, is now widely used in drug discovery. With the development of structural biology tools such as cryoelectron-microscopy (cryo-EM), the structures of a large number of GPCRs have been resolved, which provides the basis for FEP calculations. In this study, we developed an FEP protocol for GPCR FEP calculation. We performed calculations on 226 perturbation pairs of 139 ligands against 8 GPCRs, spanning 12 datasets (A2A , mGlu5 , D3, OX2 , CXCR4, β1, δ and TA1 receptors) and obtained promising results, particularly for agonist ligands in the TA1 datasets (R2, 0.58, RMSE, 1.07 kcal · mol−1 ). The average R2 is 0.61 and the average RMSE is 0.94 kcal · mol−1 , which is comparable to experimental accuracy(<1 kcal · mol−1 ). We also investigated factors that impact the accuracy of FEP results, including ligand binding pose, water placement, and protein structure. Our input structures for FEP calculation are publicly available as a benchmark dataset for future GPCR-FEP studies (https://doi.org/10.5281/zenodo.7988248). This represents the largest collection of GPCR FEP calculations known to us thus far. This work is expected to significantly contribute to the advancement of GPCR-targeted drug discovery

Microbiologically Influenced Corrosion Mechanism of Ferrous Alloys in Marine Environment

In marine environments, microbial attacks on metallic materials result in microbiologically influenced corrosion (MIC), which could cause severe safety accidents and high economic losses. To date, MIC of a number of metallic materials ranging from common steels to corrosion-resistant ferrous alloys has been reported. The MIC process has been explained based on (1) bio-catalyzed oxygen reduction; (2) kinetics alternation of the corrosion process by increasing the mass transport of the reactants and products; (3) production of corrosive substances; and (4) generation of auxiliary cathodic reactants. However, it is difficult to have a clear understanding of the MIC mechanism of ferrous alloys due to the interdisciplinary nature of MIC and lack of deep knowledge about the interfacial reaction between the biofilm and ferrous alloys. In order to better understand the effect of the MIC process on ferrous alloys, here we comprehensively summarized the process of biofilm formation and MIC mechanisms of ferrous alloys

An overview of noncarbon support materials for membrane electrode assemblies in direct methanol fuel cells: Fundamental and applications

Noncarbon support materials (NCSMs) used in the membrane electrode assemblies (MEAs) have received extensive attentions due to their better chemical stability, excellent corrosion resistance and high electrical conductivity. To develop high-quality MEA, great efforts have been devoted to improving the electrochemical performance and durability of MEAs through the surface engineering of NCSMs. With this context, the recent progress of the NCSMs as catalyst support and diffusion layer is summarized. The functional mechanisms and critical roles of NCSMs in MEAs are firstly discussed in terms of the structure-functions relationship. The design strategies and fabrication process based on different noncarbon materials (e.g., metal-based materials and carbides) as catalyst supports are summarized from the perspective of nanoscale structures like nanoparticles, nanofibers, and nanotubes. The structural design and surface modification of the noncarbon materials applied to the diffusion layer and stack-level MEA are also analyzed. Finally, the current limitations, prospective and future development tendency of NCSMs in MEA are proposed. This review is believed to provide an in-depth overview and promising research directions for the design of catalyst supports in fuel cells

Multifunctional virus manipulation with large-scale arrays of all-dielectric resonant nanocavities

Spatial manipulation of a precise number of viruses for host cell infection is essential for the extensive studies of virus pathogenesis and evolution. Albeit optical tweezers have been advanced to the atomic level via optical cooling, it is still challenging to efficiently trap and manipulate arbitrary number of viruses in an aqueous environment, being restricted by insufficient strength of optical forces and a lack of multifunctional spatial manipulation techniques. Here, by employing the virus hopping and flexibility of moving the laser position, multifunctional virus manipulation with a large trapping area is demonstrated, enabling single or massive (a large quantity of) virus transporting, positioning, patterning, sorting, and concentrating. The enhanced optical forces are produced by the confinement of light in engineered arrays of nanocavities by fine tuning of the interference resonances, and this approach allows trapping and moving viruses down to 40 nm in size. The work paves the way to efficient and precise manipulation of either single or massive groups of viruses, opening a wide range of novel opportunities for virus pathogenesis and inhibitor development at the single-virus level.Ministry of Education (MOE)National Research Foundation (NRF)Y.S. acknowledges the support from the startup funding in Shanghai Jiao Tong University, No. WH220403019. Y.S. and A.Q.L. acknowledge the Singapore National Research Foundation under the Competitive Research Program (NRFCRP13-2014-01), the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001). D.P.T. acknowledges the support from the UGC/RGC of HKSAR, China (Project No. AoE/P-502/20) and Shenzhen Science and Technology Innovation Commission Grant (No. SGDX2019081623281169). P.C.W. acknowledges the support from the Ministry of Science and Technology (MOST), Taiwan (Grant number: 107-2923-M-006-004-MY3; 108-2112-M-006-021-MY3; 110-2124-M-006-004), and in part from the Higher Education Sprout Project of the Ministry of Education (MOE) to the Headquarters of University Advancement at National Cheng Kung University (NCKU). P.C.W. also acknowledges the support from the Ministry of Education (Yushan Young Scholar Program), Taiwan. Y.K. acknowledges a support from the Australian Research Council (grant DP210101292)

DPA-2: Towards a universal large atomic model for molecular and material simulation

&lt;h2&gt;&lt;strong&gt;Data:&lt;/strong&gt;&lt;/h2&gt; &lt;div&gt; &lt;ul&gt; &lt;li&gt;The complete collection of datasets employed in this research is encapsulated within the archive file &lt;em&gt;data-v1.3.tgz.&nbsp;&lt;/em&gt;This encompasses both the upstream datasets for pre-training and downstream datasets for fine-tuning, all in &lt;a href="https://github.com/deepmodeling/deepmd-kit/blob/master/doc/data/system.md" target="_blank" rel="noopener"&gt;DeePMD format&lt;/a&gt;. We recommend creating a new directory and employing the command 'tar -xzvf data-v1.3.tgz' to extract the data files.&lt;/li&gt; &lt;li&gt;Inside each dataset contained in subdirectories (e.g., Domains, Metals, H2O, and Others), one will find: &lt;ul&gt; &lt;li&gt;A README file&lt;/li&gt; &lt;li&gt;A 'train' directory (included if utilized in upstream pre-training) &lt;ul&gt; &lt;li&gt;train.json -- A list of file paths&nbsp;for training systems&lt;/li&gt; &lt;li&gt;test.json -- A list of file paths&nbsp;for testing systems&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt;A 'downstream' directory (included if utilized in downstream fine-tuning) &lt;ul&gt; &lt;li&gt;train.json -- A list of file paths&nbsp;for training systems&lt;/li&gt; &lt;li&gt;test.json -- A list of file paths&nbsp;for testing systems&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt;*Main data files comprising various structures&lt;/li&gt; &lt;li&gt;*Additional processing scripts&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt; &lt;div&gt;The root directory contains train.json and downstream.json files that amalgamate the respective upstream and downstream splits mentioned above.&lt;/div&gt; &lt;/li&gt; &lt;li&gt; &lt;div&gt;The datasets used in this study are described in Section S1 of the Supplementary Materials and are readily accessible on&nbsp;&lt;a href="https://www.aissquare.com/" target="_blank" rel="noopener"&gt;AIS Square&lt;/a&gt;, which provides extensive details.&lt;/div&gt; &lt;/li&gt; &lt;/ul&gt; &lt;/div&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;h2&gt;&lt;strong&gt;Code:&lt;/strong&gt;&lt;/h2&gt; &lt;ul&gt; &lt;li&gt; &lt;div&gt;The 'code' directory, extractable from the archive &lt;em&gt;Code_model_script.tgz&lt;/em&gt;, includes the DeePMD-kit's source code, which is based on PyTorch (2.0) Version. Installation and usage instructions can be found within the README file located in deepmd-pytorch-devel.zip.&lt;/div&gt; &lt;/li&gt; &lt;li&gt;UPDATE: deepmd-pytorch-devel-0110.zip supports unsupervised learning through denoising, see its README for more details.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;h2&gt;&lt;strong&gt;Model:&lt;/strong&gt;&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;Within the 'model' directory, also found in the extracted &lt;em&gt;Code_model_script.tgz&lt;/em&gt;, resides the multi-task pre-trained DPA-2 model utilized in this research. Accompanying the model is its configuration file, input.json, which details the simultaneous pre-training of this model across 18 upstream datasets with shared descriptor parameters for 1 million steps.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&nbsp;&lt;/p&gt; &lt;h2&gt;&lt;strong&gt;Scripts:&lt;/strong&gt;&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;The 'scripts' directory, part of the uncompressed &lt;em&gt;Code_model_script.tgz&lt;/em&gt;, comprises all the scripts used for training, fine-tuning (learning curve analysis), and distillation in this work: &lt;ul&gt; &lt;li&gt;1. Upstream_single_task_training: Contains individual training scripts for DPA-2, Gemnet-OC, Equiformer-V2, Nequip, and Allegro, corresponding to the 18 upstream datasets.&lt;/li&gt; &lt;li&gt;2. Downstream_lcurve_workflow: Includes code and input files to evaluate the learning curves, including tests for DPA-2 fine-tuning transferability across 15 downstream datasets, as depicted in Figure 3 of the manuscript.&nbsp;&lt;/li&gt; &lt;li&gt;3. Distillation_workflow: Provides input files for distilling the fine-tuned DPA-2 models in datasets such as H2O-PBE0TS-MD, SSE-PBE-D, and FerroEle-D, as illustrated in Figure 4 of the manuscript.&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt; &lt;li&gt;It is important to note that the scripts in 'Upstream_single_task_training' require the installation of deepmd-pytorch and other related models from their respective repositories (Gemnet-OC and Equiformer-V2:&nbsp;&lt;a href="https://github.com/Open-Catalyst-Project/ocp" target="_blank" rel="noopener"&gt;here&lt;/a&gt; [commit hash: 9bc9373], Nequip: &lt;a href="https://github.com/mir-group/nequip" target="_blank" rel="noopener"&gt;here&lt;/a&gt; [commit hash: dceaf49, tag: v0.5.6], Allegro:&nbsp;&lt;a href="https://github.com/mir-group/allegro" target="_blank" rel="noopener"&gt;here&lt;/a&gt; [commit hash: 22f673c]).&lt;/li&gt; &lt;li&gt;The scripts in 'Downstream_lcurve_workflow' and 'Distillation_workflow' leverage&nbsp;&lt;a href="https://github.com/deepmodeling/dflow" target="_blank" rel="noopener"&gt;Dflow&lt;/a&gt;&mdash;a Python framework for constructing scientific computing workflow&mdash;and&nbsp;&lt;a href="https://github.com/deepmodeling/dpgen2" target="_blank" rel="noopener"&gt;dpgen2&lt;/a&gt;, the 2nd generation of the Deep Potential GENerator, both of which are repositories in the &lt;a href="https://deepmodeling.com/" target="_blank" rel="noopener"&gt;Deep Modeling Community&lt;/a&gt;.&lt;/li&gt; &lt;/ul&gt