Advanced Methods for Processing and Analyzing Eye-Tracking Data Using R

The software R can provide powerful and flexible tools for researchers to process and analyze data. This chapter introduces (1) using R to import and manage eye-tracking data and export results; (2) the apply family, which increases the efficiency of eye-tracking data extraction and manipulation; (3) statistical methods, such as correlation and regression; and (4) processing scanpath data and applying the R package GrpString to discover common patterns in a scanpath group. By using data from a previous science education study, the examples through this chapter illustrate how all of these tasks can be seamlessly accomplished in the R environment.<br

Molecular Evolution of Poly(2-isopropyl-2-oxazoline) Aqueous Solution during the Liquid–Liquid Phase Separation and Phase Transition Process

A detailed phase transition process of poly­(2-isopropyl-2-oxazoline) (PIPOZ) in aqueous solution was investigated by means of DSC, temperature-variable ¹H NMR, Raman, optical micrographs, and FT-IR spectroscopy measurements. Gradual phase separation accompanied by large dehydration degree and big conformational changes above the lower critical solution temperature (LCST) and facile reversibility were identified. Based on the two-dimensional correlation (2Dcos) and perturbation correlation moving window (PCMW) analyses, the sequence order of chemical group motions in phase transition process was elucidated. Additionally, a newly assigned CH₃···OC intermolecular hydrogen bond at 3008 cm^–1 in the PIPOZ system provides extra information on the interactions between C–H and CO groups. The formation of cross-linking “bridging” hydrogen bonds CO···D–O–D···OC (1631 cm^–1) is proposed as the key process to induce the liquid–liquid phase separation and polymer-rich phase formation of PIPOZ solution. With slow heating, more and more “bridging” hydrogen bonds were formed and D₂O were expelled with an ordered and mostly all-trans conformation adopted in the PIPOZ chains. On the basis of these observations, a physical picture on the molecular evolution of PIPOZ solution during the phase transition process has been derived

Data_Sheet_1_The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching.ZIP

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.</p

Aqueous Solutions of Poly(ethylene oxide)-Poly(<i>N</i>‑isopropylacrylamide): Thermosensitive Behavior and Distinct Multiple Assembly Processes

Detailed phase transition and conformational changes taking place as a function of temperature in poly­(ethylene oxide)-<i>b</i>-poly­(<i>N</i>-isopropylacrylamide) (PEO-<i>b</i>-PNIPAM) semidiluted aqueous solutions are elucidated in the present study. By the use of elaborate vibrational spectroscopy techniques in combination with two-dimensional correlation spectroscopy (2Dcos), three transition regions including respective rich domains (<29 °C), loose aggregations (30–36 °C), and dense sphere micelles (>37 °C) are depicted. In particular, subtle variations of hydrogen bonds are detected even under the lower critical solution temperature (LCST), and the respective rich domain regime is marked with strong participation from hydrogen bonding at different concentrations and compositions. Both the formation of intermolecular hydrogen bonds and the less hydration degrees of PNIPAM segments compared with PNIPAM homopolymer at elevated temperatures verify the evolution of PNIPAM from their own domains to loose aggregations with PEO shells. Dense micelles are formed beyond the LCST of PNIPAM, while the outmost PEOs act as buffer layers and postpone the shrinkage of PNIPAM chains. Due to the existence of a buffer layer, higher phase transition temperatures compared with PNIPAM homopolymer are observed

Table_2_The Use of Deep Learning-Based Intelligent Music Signal Identification and Generation Technology in National Music Teaching.XLSX

The research expects to explore the application of intelligent music recognition technology in music teaching. Based on the Long Short-Term Memory network knowledge, an algorithm model which can distinguish various music signals and generate various genres of music is designed and implemented. First, by analyzing the application of machine learning and deep learning in the field of music, the algorithm model is designed to realize the function of intelligent music generation, which provides a theoretical basis for relevant research. Then, by selecting massive music data, the music style discrimination and generation model is tested. The experimental results show that when the number of hidden layers of the designed model is 4 and the number of neurons in each layer is 1,024, 512, 256, and 128, the training result difference of the model is the smallest. The classification accuracy of jazz, classical, rock, country, and disco music types can be more than 60% using the designed algorithm model. Among them, the classification effect of jazz schools is the best, which is 77.5%. Moreover, compared with the traditional algorithm, the frequency distribution of the music score generated by the designed algorithm is almost consistent with the spectrum of the original music. Therefore, the methods and models proposed can distinguish music signals and generate different music, and the discrimination accuracy of different music signals is higher, which is superior to the traditional restricted Boltzmann machine method.</p

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.</p

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.</p

Interpretation of Carbonyl Band Splitting Phenomenon of a Novel Thermotropic Liquid Crystalline Polymer without Conventional Mesogens: Combination Method of Spectral Analysis and Molecular Simulation

A combination method of spectral analysis and molecular simulation was employed to interpret the carbonyl band splitting phenomenon of poly[di(butyl)vinylterephthalate] (PDBVT), a novel thermotropic liquid crystalline polymer, which can self-assembly into a two-dimensional hexagonal, columnar (2D ΦH) phase without conventional mesogens. Two-dimensional correlation infrared spectroscopy results of PDBVT during heating showed four splitting bands at 1707, 1712, 1731, and 1741 cm−1. Accordingly, four PDBVT conformers were classified on the basis of carbonyls rotating in a π-electron resonance system. Detailed spectral comparison and molecular dynamics (MD) simulation for the columnar phase of PDBVT were carried out to make a clear assignment of splitting bands to different conformers. The internal self-assembly nature of PDBVT can be concluded that the rotation of carbonyls at the 2-position (close to backbone) of the phenylenes would take place, along with the formation of the 2D ΦH phase. Meanwhile, the consecutive motions of PDBVT backbones with a distortion and extension in the mesophase formation and preparation processes have also been examined and reproduced by MD simulation. Finally, a good simulated conformity of the side chain size dependence of the liquid crystallinity of PDAVTs with experimental observations was achieved. This work combining spectral analysis and molecular simulation may bring some new insight into a better understanding of various physical chemical phenomena unintelligible before

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.</p

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx

Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.</p