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  1. Article ; Online: Tap water microbiome shifts in secondary water supply for high-rise buildings.

    Li, Manjie / Liu, Zhaowei / Chen, Yongcan

    Environmental science and ecotechnology

    2024  Volume 20, Page(s) 100413

    Abstract: In high-rise buildings, secondary water supply systems (SWSSs) are pivotal yet provide a conducive milieu for microbial proliferation due to intermittent flow, low disinfectant residual, and high specific pipe-surface area, raising concerns about tap ... ...

    Abstract In high-rise buildings, secondary water supply systems (SWSSs) are pivotal yet provide a conducive milieu for microbial proliferation due to intermittent flow, low disinfectant residual, and high specific pipe-surface area, raising concerns about tap water quality deterioration. Despite their ubiquity, a comprehensive understanding of bacterial community dynamics within SWSSs remains elusive. Here we show how intrinsic SWSS variables critically shape the tap water microbiome at distal ends. In an office setting, distinct from residential complexes, the diversity in piping materials instigates a noticeable bacterial community shift, exemplified by a transition from α-Proteobacteria to γ-Proteobacteria dominance, alongside an upsurge in bacterial diversity and microbial propagation potential. Extended water retention within SWSSs invariably escalates microbial regrowth propensities and modulates bacterial consortia, yet secondary disinfection emerges as a robust strategy for preserving water quality integrity. Additionally, the regularity of water usage modulates proximal flow dynamics, thereby influencing tap water's microbial landscape. Insights garnered from this investigation lay the groundwork for devising effective interventions aimed at safeguarding microbiological standards at the consumer's endpoint.
    Language English
    Publishing date 2024-03-16
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2666-4984
    ISSN (online) 2666-4984
    DOI 10.1016/j.ese.2024.100413
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Untrained, physics-informed neural networks for structured illumination microscopy.

    Burns, Zachary / Liu, Zhaowei

    Optics express

    2022  Volume 31, Issue 5, Page(s) 8714–8724

    Abstract: Structured illumination microscopy (SIM) is a popular super-resolution imaging technique that can achieve resolution improvements of 2× and greater depending on the illumination patterns used. Traditionally, images are reconstructed using the linear SIM ... ...

    Abstract Structured illumination microscopy (SIM) is a popular super-resolution imaging technique that can achieve resolution improvements of 2× and greater depending on the illumination patterns used. Traditionally, images are reconstructed using the linear SIM reconstruction algorithm. However, this algorithm has hand-tuned parameters which can often lead to artifacts, and it cannot be used with more complex illumination patterns. Recently, deep neural networks have been used for SIM reconstruction, yet they require training sets that are difficult to capture experimentally. We demonstrate that we can combine a deep neural network with the forward model of the structured illumination process to reconstruct sub-diffraction images without training data. The resulting physics-informed neural network (PINN) can be optimized on a single set of diffraction-limited sub-images and thus does not require any training set. We show, with simulated and experimental data, that this PINN can be applied to a wide variety of SIM illumination methods by simply changing the known illumination patterns used in the loss function and can achieve resolution improvements that match theoretical expectations.
    Language English
    Publishing date 2022-11-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1491859-6
    ISSN 1094-4087 ; 1094-4087
    ISSN (online) 1094-4087
    ISSN 1094-4087
    DOI 10.1364/OE.476781
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  3. Article ; Online: RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection.

    Guo, Dezhi / Liu, Zhaowei / Li, Ranran

    Neural networks : the official journal of the International Neural Network Society

    2023  Volume 166, Page(s) 273–285

    Abstract: Due to the wide application of dynamic graph anomaly detection in cybersecurity, social networks, e-commerce, etc., research in this area has received increasing attention. Graph generative adversarial networks can be used in dynamic graph anomaly ... ...

    Abstract Due to the wide application of dynamic graph anomaly detection in cybersecurity, social networks, e-commerce, etc., research in this area has received increasing attention. Graph generative adversarial networks can be used in dynamic graph anomaly detection due to their ability to model complex data, but the original graph generative adversarial networks do not have a method to learn reverse mapping and require an expensive process in recovering the potential representation of a given input. Therefore, this paper proposes a novel graph generative adversarial network by adding encoders to map real data to latent space to improve the training efficiency and stability of graph generative adversarial network models, which is named RegraphGAN in this paper. And this paper proposes a dynamic network anomaly edge detection method by combining RegraphGAN with spatiotemporal coding to solve the complex dynamic graph data and the problem of attribute-free node information coding challenges. Meanwhile, anomaly detection experiments are conducted on six real dynamic network datasets, and the results show that the dynamic network anomaly detection method proposed in this paper outperforms other existing methods.
    MeSH term(s) Computer Security ; Learning ; Social Networking
    Language English
    Publishing date 2023-07-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2023.07.026
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2365: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: De novo fabrication of custom-sequence plasmids for the synthesis of long DNA constructs with extrahelical features.

    Ramírez Montero, Daniel / Liu, Zhaowei / Dekker, Nynke H

    Biophysical journal

    2023  Volume 123, Issue 1, Page(s) 31–41

    Abstract: DNA constructs for single-molecule experiments often require specific sequences and/or extrahelical/noncanonical structures to study DNA-processing mechanisms. The precise introduction of such structures requires extensive control of the sequence of the ... ...

    Abstract DNA constructs for single-molecule experiments often require specific sequences and/or extrahelical/noncanonical structures to study DNA-processing mechanisms. The precise introduction of such structures requires extensive control of the sequence of the initial DNA substrate. A commonly used substrate in the synthesis of DNA constructs is plasmid DNA. Nevertheless, the controlled introduction of specific sequences and extrahelical/noncanonical structures into plasmids often requires several rounds of cloning on pre-existing plasmids whose sequence one cannot fully control. Here, we describe a simple and efficient way to synthesize 10.1-kb plasmids de novo using synthetic gBlocks that provides full control of the sequence. Using these plasmids, we developed a 1.5-day protocol to assemble 10.1-kb linear DNA constructs with end and internal modifications. As a proof of principle, we synthesize two different DNA constructs with biotinylated ends and one or two internal 3' single-stranded DNA flaps, characterize them using single-molecule force and fluorescence spectroscopy, and functionally validate them by showing that the eukaryotic replicative helicase Cdc45/Mcm2-7/GINS (CMG) binds the 3' single-stranded DNA flap and translocates in the expected direction. We anticipate that our approach can be used to synthesize custom-sequence DNA constructs for a variety of force and fluorescence single-molecule spectroscopy experiments to interrogate DNA replication, DNA repair, and transcription.
    MeSH term(s) DNA, Single-Stranded ; Cell Cycle Proteins/metabolism ; DNA/chemistry ; DNA Replication ; Plasmids/genetics
    Chemical Substances DNA, Single-Stranded ; Cell Cycle Proteins ; DNA (9007-49-2)
    Language English
    Publishing date 2023-11-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 218078-9
    ISSN 1542-0086 ; 0006-3495
    ISSN (online) 1542-0086
    ISSN 0006-3495
    DOI 10.1016/j.bpj.2023.11.008
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 235: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 2: Show issues

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  5. Article ; Online: Engineering an artificial catch bond using mechanical anisotropy.

    Liu, Zhaowei / Liu, Haipei / Vera, Andrés M / Yang, Byeongseon / Tinnefeld, Philip / Nash, Michael A

    Nature communications

    2024  Volume 15, Issue 1, Page(s) 3019

    Abstract: Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin ... ...

    Abstract Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.
    MeSH term(s) Humans ; Anisotropy ; Mechanical Phenomena ; Kinetics ; Cohesins ; Bacteria ; Protein Binding
    Chemical Substances Cohesins
    Language English
    Publishing date 2024-04-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-46858-9
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  6. Book ; Online: Untrained, physics-informed neural networks for structured illumination microscopy

    Burns, Zachary / Liu, Zhaowei

    2022  

    Abstract: In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on ... ...

    Abstract In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on data-driven, supervised training strategies that need a large number of ground truth images, which is experimentally difficult to realize. For SIM imaging, there exists a need for a flexible, general, and open-source reconstruction method that can be readily adapted to different forms of structured illumination. We demonstrate that we can combine a deep neural network with the forward model of the structured illumination process to reconstruct sub-diffraction images without training data. The resulting physics-informed neural network (PINN) can be optimized on a single set of diffraction limited sub-images and thus doesn't require any training set. We show with simulated and experimental data that this PINN can be applied to a wide variety of SIM methods by simply changing the known illumination patterns used in the loss function and can achieve resolution improvements that match well with theoretical expectations.

    Comment: Preprint for journal submission. 21 Pages. 5 main text figures. 6 supplementary figures
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Physics - Optics
    Subject code 006
    Publishing date 2022-07-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data.

    Doffini, Vanni / Liu, Haipei / Liu, Zhaowei / Nash, Michael A

    Nano letters

    2023  Volume 23, Issue 22, Page(s) 10406–10413

    Abstract: We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called ... ...

    Abstract We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics.
    MeSH term(s) Microscopy, Atomic Force/methods ; Proteins/chemistry ; Mechanical Phenomena ; Machine Learning ; Spectrum Analysis
    Chemical Substances Proteins
    Language English
    Publishing date 2023-11-07
    Publishing country United States
    Document type Journal Article
    ISSN 1530-6992
    ISSN (online) 1530-6992
    DOI 10.1021/acs.nanolett.3c03026
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  8. Article ; Online: Low-dimensional heat conduction in surface phonon polariton waveguide.

    Pei, Yu / Chen, Li / Jeon, Wonjae / Liu, Zhaowei / Chen, Renkun

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 8242

    Abstract: Heat conduction in solids is typically governed by the Fourier's law describing a diffusion process due to the short wavelength and mean free path for phonons and electrons. Surface phonon polaritons couple thermal photons and optical phonons at the ... ...

    Abstract Heat conduction in solids is typically governed by the Fourier's law describing a diffusion process due to the short wavelength and mean free path for phonons and electrons. Surface phonon polaritons couple thermal photons and optical phonons at the surface of polar dielectrics, possessing much longer wavelength and propagation length, representing an excellent candidate to support extraordinary heat transfer. Here, we realize clear observation of thermal conductivity mediated by surface phonon polaritons in SiO
    Language English
    Publishing date 2023-12-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-43736-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Direct Comparison of Lysine versus Site-Specific Protein Surface Immobilization in Single-Molecule Mechanical Assays.

    Liu, Haipei / Liu, Zhaowei / Sá Santos, Mariana / Nash, Michael A

    Angewandte Chemie (International ed. in English)

    2023  Volume 62, Issue 32, Page(s) e202304136

    Abstract: Single-molecule force spectroscopy (SMFS) is powerful for studying folding states and mechanical properties of proteins, however, it requires protein immobilization onto force-transducing probes such as cantilevers or microbeads. A common immobilization ... ...

    Abstract Single-molecule force spectroscopy (SMFS) is powerful for studying folding states and mechanical properties of proteins, however, it requires protein immobilization onto force-transducing probes such as cantilevers or microbeads. A common immobilization method relies on coupling lysine residues to carboxylated surfaces using 1-ethyl-3-(3-dimethyl-aminopropyl) carbodiimide and N-hydroxysuccinimide (EDC/NHS). Because proteins typically contain many lysine groups, this strategy results in a heterogeneous distribution of tether positions. Genetically encoded peptide tags (e.g., ybbR) provide alternative chemistries for achieving site-specific immobilization, but thus far a direct comparison of site-specific vs. lysine-based immobilization strategies to assess effects on the observed mechanical properties was lacking. Here, we compared lysine- vs. ybbR-based protein immobilization in SMFS assays using several model polyprotein systems. Our results show that lysine-based immobilization results in significant signal deterioration for monomeric streptavidin-biotin interactions, and loss of the ability to correctly classify unfolding pathways in a multipathway Cohesin-Dockerin system. We developed a mixed immobilization approach where a site-specifically tethered ligand was used to probe surface-bound proteins immobilized through lysine groups, and found partial recovery of specific signals. The mixed immobilization approach represents a viable alternative for mechanical assays on in vivo-derived samples or other proteins of interest where genetically encoded tags are not feasible.
    MeSH term(s) Lysine ; Peptides ; Membrane Proteins ; Mechanical Phenomena ; Streptavidin ; Microscopy, Atomic Force/methods
    Chemical Substances Lysine (K3Z4F929H6) ; Peptides ; Membrane Proteins ; Streptavidin (9013-20-1)
    Language English
    Publishing date 2023-07-03
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2011836-3
    ISSN 1521-3773 ; 1433-7851
    ISSN (online) 1521-3773
    ISSN 1433-7851
    DOI 10.1002/anie.202304136
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  10. Article ; Online: Macroscopic hydro-thermal processes in a large channel-type reservoir

    Shi, Lidi / Sun, Jian / Lin, Binliang / Morovati, Khosro / Liu, Zhaowei / Zuo, Xinyu

    Journal of Hydrology: Regional Studies. 2023 June, v. 47 p.101367-

    2023  

    Abstract: This study was conducted in Xiangjiaba Reservoir, located in the Jinsha River, China's upper reach of the Yangtze River. Large channel-type reservoirs are characterized by significant runoff, deep water, and narrow-and-long channel shape, creating unique ...

    Abstract This study was conducted in Xiangjiaba Reservoir, located in the Jinsha River, China's upper reach of the Yangtze River. Large channel-type reservoirs are characterized by significant runoff, deep water, and narrow-and-long channel shape, creating unique hydrodynamic and thermodynamic conditions for their eco-environmental systems. However, the hydro-thermal processes of this type reservoir are not fully known. A large channel-type reservoir was selected to investigate the macroscopic processes through a field survey and employing a three-dimensional (3D) hydro-thermodynamic model. The reservoir experienced a seasonal varying thermal stratification, with a vertical temperature difference of ∼9 °C. Meanwhile, a conjoint flow pattern was detected, consisting of an upper-layer warm water flow and a lower-layer circulation, which was very different from the lakes' wind-induced pattern. When the reservoir was stratified, the inflow warm water on the thermocline could produce a shear effect, promoting vertical mixing and further contributing to hypolimnion shrinking. The reservoir experienced a water age increase during the non-stratification period, with the near-dam age being ∼20 days. The trap effect of the lower-layer circulation retained the cold water in the hypolimnion during the stratification period, and the water age reached 115 days. The presented varying flow pattern and transport timescale have essential effects on the eco-environmental systems, providing practical insights into managing water quality and aquatic environment in reservoirs.
    Keywords aquatic environment ; hydrodynamics ; models ; rivers ; runoff ; surveys ; temperature profiles ; thermodynamics ; water flow ; water quality ; China ; Yangtze River ; Macroscopic flow patterns ; Thermal stratification ; Water age ; Large channel-type reservoir
    Language English
    Dates of publication 2023-06
    Publishing place Elsevier B.V.
    Document type Article ; Online
    ZDB-ID 2814784-4
    ISSN 2214-5818
    ISSN 2214-5818
    DOI 10.1016/j.ejrh.2023.101367
    Database NAL-Catalogue (AGRICOLA)

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