Spatial distribution of the Shannon entropy for mass spectrometry imaging

Xu, Lili; Kikushima, Kenji; Sato, Shumpei; Islam, Ariful; Sato, Tomohito; Aramaki, Shuhei; Zhang, Chi; Sakamoto, Takumi; Eto, Fumihiro; Takahashi, Yutaka; Yao, Ikuko; Machida, Manabu; Kahyo, Tomoaki; Setou, Mitsutoshi

doi:10.1371/journal.pone.0283966

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…Higher audio entropy indicates greater variability in audio sample values, potentially suggesting more complex or random sounds. Conversely, lower entropy implies that the sound tends to have patterns or repetitions, with less variation in sample values [27].…”

Section: Entropy Voice Encryption Analysismentioning

confidence: 99%

Encryption of digital images and voices using the Arneodo-Coullet-Tresser chaotic system

Sanjaya,

Roziqin,

Temiesela

et al. 2023

Phys. Scr.

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In this study, we investigate the application of the Arneodo-Coullet-Tresser chaotic system in both image and voice encryption, considering the increasing importance of data security in the digital age. The analysis of the chaotic system involves exploring phase diagrams, time series, bifurcation diagrams, Lyapunov exponent analysis, and Poincaré maps to understand its dynamics. For image encryption, we evaluate the effectiveness of the system through various analyses, including histogram analysis, correlation analysis, entropy analysis, sensitivity analysis, and noise attack analysis. Similarly, for voice encryption, we assess it through various analyses, including waveform plots, FFT, spectrograms, correlation coefficients, entropy analysis, and RMSE. The research findings demonstrate the suitability of the Arneodo-Coullet-Tresser chaotic system for both image and voice encryption, suggesting its potential as a data transmission masking technique. The research includes numerical simulations conducted using Python to support the proposed approach.

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Section: Entropy Voice Encryption Analysismentioning

confidence: 99%

Encryption of digital images and voices using the Arneodo-Coullet-Tresser chaotic system

Sanjaya,

Roziqin,

Temiesela

et al. 2023

Phys. Scr.

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“…In [20], a method based on the information entropy (Shannon entropy) for time-of-flight secondary ion mass spectrometry (TOF-SIMS) was proposed and it was shown that without peak identification the spatial distribution (heat maps) of the Shannon entropy of spectra indicates differences in materials and changes in the conditions of a material in a sample. The spatial distribution of the Shannon entropy was also studied for the matrix-assisted laser desorption/ionization (MALDI) MSI and a method to select candidate peaks was proposed [21].…”

Section: Introductionmentioning

confidence: 99%

Perplexity for mass spectrometry imaging

Xu¹,

Zhang²,

Oyama³

et al. 2023

Preprint

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MSI (mass spectrometry imaging) serves as an efficient and reliable tool for imaging certain molecules on biological samples. But the comprehensive understanding of the information from MSI has not been fully studied. In this paper, it is studied by coarse-graining how molecules spatially vary in the sample. We find that the double-exponential of entropy shows the power-law behavior in coarse-graining. The spatial distribution of the exponent provides new information for MSI. Experiments were performed using kidneys of mice. Heat maps of the exponent showed high values in the renal pelvis area and low values in the medulla area. Spots within and around the veins and arteries were distinguished from those composed of kidney cells. The method reveals and visualizes changes in the diversity of full chemical information obtained by MSI.

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Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method

Xu,

Zhai,

Islam

et al. 2024

J. Am. Soc. Mass Spectrom.

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Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics research. In IMS, the time cost and instrument load required for large data sets must be considered, as IMS typically involves tens of thousands of pixels or more. In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice with a pitch size of 75 μm and then downsampled the data to a pitch size of 150 μm, selecting 22 m/z peak intensity values per pixel. We established an AAE model to generate three pixels from the surrounding nine pixels within a window to reconstruct the image data at a pitch size of 75 μm. Compared with two alternative interpolation methods, Bilinear and Bicubic interpolation, our window-based AAE model demonstrated superior performance on image evaluation metrics for the validation data sets. A similar model was constructed for larger mouse kidney tissues, where the AAE model achieved high image evaluation metrics. Our method is expected to be valuable for IMS measurements of large animal organs across extensive areas.

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Spatial distribution of the Shannon entropy for mass spectrometry imaging

Cited by 6 publications

References 38 publications

Encryption of digital images and voices using the Arneodo-Coullet-Tresser chaotic system

Encryption of digital images and voices using the Arneodo-Coullet-Tresser chaotic system

Perplexity for mass spectrometry imaging

Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method

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