A super-resolution strategy for mass spectrometry imaging via transfer learning

Liao, Tiepeng; Ren, Zihao; Chai, Zhaoliang; Yuan, Mei; Miao, Chenjian; Li, Junjie; Chen, Qi; Li, Zhilin; Wang, Ziyi; Lin, Yandan; Ge, Shanhai; Qian, Wenwei; Shen, Longfeng; Wang, Zilei; Xiong, Wei; Zhu, Huilong

doi:10.1038/s42256-023-00677-7

Cited by 17 publications

(9 citation statements)

References 63 publications

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“…Consequently, there is an exigent requirement for a technology capable of augmenting spatial resolution of images within the confines of existing hardware capabilities. Super-resolution restoration entails the generation of high-resolution images from blurred low-resolution counterparts 46 , 47 . Presently, this technology relies on software-based signal processing methods and necessitates high-performance GPUs for super-resolution processing, while being constrained by the throughput and power limitations inherent in traditional architectures.…”

Section: Resultsmentioning

confidence: 99%

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Chen,

Lin,

Yang

et al. 2024

Nat Commun

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Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information’s optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Chen,

Lin,

Yang

et al. 2024

Nat Commun

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“…Advancements in NMR techniques like 2D NMR also presents a chance to uncover signals that are overlapping in 1D NMR spectroscopy due to similar resonant frequencies and hence discover metabolites that were otherwise not resolved by 1D NMR ( Mahrous and Farag, 2015 ). Besides, an emerging technique, Mass Spectrometry Imaging (MSI), is also a highly futuristic tool which enables untargeted investigations of a variety of samples sectioned into different spatial distributions ( Liao et al, 2023 ). It has a capability to image thousands of molecules, such as metabolites, lipids, peptides, proteins, and glycans, in a single experiment without labelling.…”

Section: Evolving Methods and Future Perspectives In Cho Metabolomicsmentioning

confidence: 99%

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Singh,

Fatima,

Thakur

et al. 2024

Front. Bioeng. Biotechnol.

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Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical.Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures.Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

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“…By utilizing a larger data set for initially training on a similar “out of domain” task of classifying photos of animals, the model may perform better once fine-tuned for the new “domain” of classifying photos of plants. Transfer learning has already been utilized for MSI, sample classification, , RT prediction, and spectra refinement . However, the repurposing and retraining of domain/task-specific models for MS data as starting points remain limited, although some groups have developed domain/task specific transfer learning approaches from the ground up .…”

Section: Future Directionsmentioning

confidence: 99%

Recent Developments in Machine Learning for Mass Spectrometry

Beck,

Muhoberac,

Randolph

et al. 2024

ACS Meas. Sci. Au

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Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

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A super-resolution strategy for mass spectrometry imaging via transfer learning

Cited by 17 publications

References 63 publications

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Advancements in CHO metabolomics: techniques, current state and evolving methodologies

Recent Developments in Machine Learning for Mass Spectrometry

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