Quantitative evaluation for four methods on simulation image.

Quantitative evaluation for four methods on simulation image.</p

Features used in our approach.

Features used in our approach.</p

Segmentation results of four methods on microarray images drawn from six data sets.

Segmentation results of four methods on microarray images drawn from six data sets.</p

Description of six real microarray data sets.

Description of six real microarray data sets.</p

Gene expression for images in Fig 4 with four different methods.

Gene expression for images in Fig 4 with four different methods.</p

Segmentation results of various shape spots.

Segmentation results of various shape spots.</p

Scatter plot of two channel intensities for four methods on image drawn from SMD dataset.

Scatter plot of two channel intensities for four methods on image drawn from SMD dataset.</p

Modelling infrared spectra of the O-H stretches in liquid H₂O based on a deep learning potential, the importance of nuclear quantum effects

In this study, we have trained a deep learning (DL) potential for water using training datasets obtained from the DPLibrary (https://dplibrary.deepmd.net/). Subsequently, we conducted classical molecular dynamics simulation (DeePMD) and path-integral MD simulation (PI-DeePMD) of liquid water based on this deep potential (DP). Using the velocity-velocity auto-correlation function (VVAF) formula, we constructed infrared (IR) spectra for the O-H stretching mode based on the DeePMD simulation trajectories. Our results showed that the DeePMD/VVAF approach accurately captured the experimental result for O-H stretching vibration, with the O-H vibrational peak located at 3400 cm−1. Additionally, the PI-DeePMD approach successfully predicted the red-shift and broadening of the O-H stretching band of water, emphasising the importance of nuclear quantum effects (NQEs). In particular, the PI-DeePMD simulation correctly reproduced the shoulder located at 3250 cm−1, which was underestimated by classical DeePMD simulation. The success of the PI-DeePMD/VVAF approach can be attributed to the following reasons: (1) the DeePMD simulation improves the accuracy of calculating atomic velocities due to the DFT-level accuracy of the DP model; (2) the PI-DeePMD simulation takes into account the contribution from nuclear quantum effects; (3) the non-Condon effect is considered in the VVAF formalism.</p

Flowchart of improved clustering-based image segmentation method.

Flowchart of improved clustering-based image segmentation method.</p

Examples of segmentation results on simulation images.

Examples of segmentation results on simulation images.</p