56 research outputs found
Subgraph Networks Based Contrastive Learning
Graph contrastive learning (GCL), as a self-supervised learning method, can
solve the problem of annotated data scarcity. It mines explicit features in
unannotated graphs to generate favorable graph representations for downstream
tasks. Most existing GCL methods focus on the design of graph augmentation
strategies and mutual information estimation operations. Graph augmentation
produces augmented views by graph perturbations. These views preserve a locally
similar structure and exploit explicit features. However, these methods have
not considered the interaction existing in subgraphs. To explore the impact of
substructure interactions on graph representations, we propose a novel
framework called subgraph network-based contrastive learning (SGNCL). SGNCL
applies a subgraph network generation strategy to produce augmented views. This
strategy converts the original graph into an Edge-to-Node mapping network with
both topological and attribute features. The single-shot augmented view is a
first-order subgraph network that mines the interaction between nodes,
node-edge, and edges. In addition, we also investigate the impact of the
second-order subgraph augmentation on mining graph structure interactions, and
further, propose a contrastive objective that fuses the first-order and
second-order subgraph information. We compare SGNCL with classical and
state-of-the-art graph contrastive learning methods on multiple benchmark
datasets of different domains. Extensive experiments show that SGNCL achieves
competitive or better performance (top three) on all datasets in unsupervised
learning settings. Furthermore, SGNCL achieves the best average gain of 6.9\%
in transfer learning compared to the best method. Finally, experiments also
demonstrate that mining substructure interactions have positive implications
for graph contrastive learning.Comment: 12 pages, 6 figure
High-fidelity quantitative differential phase contrast deconvolution using dark-field sparse prior
Differential phase contrast (DPC) imaging plays an important role in the family of quantitative phase measurement. However, the reconstruction algorithm for quantitative DPC (qDPC) imaging is not yet optimized, as it does not incorporate the inborn properties of qDPC imaging. In this research, we propose a simple but effective image prior, the dark-field sparse prior (DSP), to facilitate the phase reconstruction quality for all DPC-based phase reconstruction algorithms. The DSP is based on the key observation that most pixel values for an idea differential phase contrast image are zeros since the subtraction of two images under anti-symmetric illumination cancels all background components. With this DSP prior, we formed a new cost function in which L0-norm was used to represent the DSP. Further, we developed the algorithm based on the Half Quadratic Splitting to solve this NP-hard L0-norm problem. We tested our new model on both simulated and experimental data and compare it against state-of-The-Art (SOTA) methods including L2-norm and total variation regularizations. Results show that our proposed model is superior in terms of phase reconstruction quality and implementation efficiency, which significantly increases the experimental robustness, while maintaining the data fidelity. In general, the DSP supports high-fidelity qDPC reconstruction without any modification of the optical system, which simplifies the system complexity and benefit all qDPC applications
Pupil-driven quantitative differential phase contrast imaging
In this research, we reveal the inborn but hitherto ignored properties of
quantitative differential phase contrast (qDPC) imaging: the phase transfer
function being an edge detection filter. Inspired by this, we highlighted the
duality of qDPC between optics and pattern recognition, and propose a simple
and effective qDPC reconstruction algorithm, termed Pupil-Driven qDPC
(pd-qDPC), to facilitate the phase reconstruction quality for the family of
qDPC-based phase reconstruction algorithms. We formed a new cost function in
which modified L0-norm was used to represent the pupil-driven edge sparsity,
and the qDPC convolution operator is duplicated in the data fidelity term to
achieve automatic background removal. Further, we developed the iterative
reweighted soft-threshold algorithms based on split Bregman method to solve
this modified L0-norm problem. We tested pd-qDPC on both simulated and
experimental data and compare against state-of-the-art (SOTA) methods including
L2-norm, total variation regularization (TV-qDPC), isotropic-qDPC, and Retinex
qDPC algorithms. Results show that our proposed model is superior in terms of
phase reconstruction quality and implementation efficiency, in which it
significantly increases the experimental robustness while maintaining the data
fidelity. In general, the pd-qDPC enables the high-quality qDPC reconstruction
without any modification of the optical system. It simplifies the system
complexity and benefits the qDPC community and beyond including but not limited
to cell segmentation and PTF learning based on the edge filtering property
1D SbSeI, SbSI, and SbSBr With High Stability and Novel Properties for Microelectronic, Optoelectronic, and Thermoelectric Applications
Mechanical exfoliation of 2D materials has triggered an explosive interest in low dimensional material research. We extend this idea to 1D van der‐Waals materials. Three 1D semiconductors (SbSeI, SbSI, and SbSBr) with high stability and novel electronic properties are discovered using first principles calculations. Both the dynamical and the thermal stability of these 1D materials are examined. We demonstrate that their nanowire thinner than 7 Å can be easily obtained by mechanical exfoliation, hydrothermal method, or sonochemical method. The bulk‐to‐1D transition results in dramatic changes in band gap, effective mass, and static dielectric constant due to quantum confinement, making 1D SbSeI a highly promising channel material for transistors with gate length shorter than 1 nm. Under small uniaxial strain, these materials are transformed from indirect into direct band gap semiconductors, paving the way for optoelectronic devices and mechanical sensors. Moreover, the thermoelectric performance of these materials is significantly improved over their bulk counterparts. These highly desirable properties render SbSeI, SbSI, and SbSBr promising 1D materials for applications in future microelectronics, optoelectronics, mechanical sensors, and thermoelectrics
Recent progress in biobased synthetic textile fibers
The use of synthetic fibers in our daily life is growing continuously; however, the excessive dependence of these chemical fibers on petroleum-based chemicals will lead to large consumption of non-renewable resources. The scarcity of oil resources, economic and environmental problems, reliance on a few oil-rich countries, and predicted depletion of these resources. Therefore, research and development of biobased materials to reduce the use of fossil fuels have become increasingly important. Biobased synthetic fiber has a low carbon footprint in the synthesis process because its raw materials are derived from biomass. In addition, most biobased synthetic fibers have excellent biodegradability, which can be composted and degraded in natural environments or by microorganisms with or without specific conditions. However, all biobased fibers cannot be proven to be biodegradable, so the development of biodegradability is an important driving force for the progress of research on biobased fibers. In the past, biobased fiber was obtained, extracted, or synthesized from food crops, which was soon replaced by non-food crops. With environmental protection, sustainability, and resource conservation, it has become necessary to make non-food crops and food residues biobased raw materials to obtain biobased textile fibers and even to develop ideal biobased raw materials that are carbon negatives, such as moss and CO2. Besides, there is huge potential for these biobased textile fibers to be used for sustainable clothing and medical textiles due to their non-toxicity, skin friendliness, and antibacterial properties. This review paper introduces biobased synthetic textile fibers, summarizes the recent development, and clarifies key concepts in this domain
VP2 residue N142 of coxsackievirus A10 is critical for the interaction with KREMEN1 receptor and neutralizing antibodies and the pathogenicity in mice.
Coxsackievirus A10 (CVA10) has recently emerged as one of the major causative agents of hand, foot, and mouth disease. CVA10 may also cause a variety of complications. No approved vaccine or drug is currently available for CVA10. The residues of CVA10 critical for viral attachment, infectivity and in vivo pathogenicity have not been identified by experiment. Here, we report the identification of CVA10 residues important for binding to cellular receptor KREMEN1. We identified VP2 N142 as a key receptor-binding residue by screening of CVA10 mutants resistant to neutralization by soluble KREMEN1 protein. The receptor-binding residue N142 is exposed on the canyon rim but highly conserved in all naturally occurring CVA10 strains, which provides a counterexample to the canyon hypothesis. Residue N142 when mutated drastically reduced receptor-binding activity, resulting in decreased viral attachment and infection in cell culture. More importantly, residue N142 when mutated reduced viral replication in limb muscle and spinal cord of infected mice, leading to lower mortality and less severe clinical symptoms. Additionally, residue N142 when mutated could decrease viral binding affinity to anti-CVA10 polyclonal antibodies and a neutralizing monoclonal antibody and render CVA10 resistant to neutralization by the anti-CVA10 antibodies. Overall, our study highlights the essential role of VP2 residue N142 of CVA10 in the interactions with KREMEN1 receptor and neutralizing antibodies and viral virulence in mice, facilitating the understanding of the molecular mechanisms of CVA10 infection and immunity. Our study also provides important information for rational development of antibody-based treatment and vaccines against CVA10 infection
A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction
Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augment a mini-Mu transposon-based screening approach and devise the intein-assisted bisection mapping (IBM) method. IBM robustly reveals clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further show that the use of inteins expands functional sequence space for splitting a protein. We also demonstrate the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins, and that basal activities of highly active proteins can be mitigated by splitting them. Our work offers a generalizable and systematic route towards creating split protein-intein fusions for synthetic biology. Split inteins are powerful tools for designing synthetic split proteins. Here the authors use a mini-Mu transposon screen to map split sites, enabling the development of protein-based logic gates and fine control of protein activity
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