Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Background: Raman and fluorescence spectra techniques are potential tools for disease diagnosis. In recent years, the application of Raman and fluorescence spectra techniques in biological studies has increased a great deal, and clinical investigations relevant to cancer detection by spectroscopic means have attracted particularly attention from both clinical and non-clinical researchers. Methods: In this article, Raman and fluorescence spectra were employed for the detection of liver cancer and healthy individuals using their serum samples. These serum samples were compared with their spectral features acquired by Raman and fluorescence spectroscopy to initially establish spectral features that can be considered spectral markers of liver cancer diagnosis. Resuits: The intensity differences from characteristic peaks of carotene, protein and lipid associated Raman spectra were clearly observed in liver cancer patient serum samples versus normal human serum. The changes in the serum fluorescence profiles of liver cancer patients were also analyzed. To probe the capacity and contrast of Raman spectroscopy as an analytical implement for the early diagnosis of liver cancer, principal component analysis (PCA) was used to analyze the Raman spectra of controls , liver cancer patients and healthy individuals. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of Raman spectroscopy for the classification of disease samples and healthy samples.Conclusion: Compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures. Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.

Remote Sensing Image Fusion Algorithm Based on Two-Stream Fusion Network and Residual Channel Attention Mechanism

A two-stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two-channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen-2 and WorldView-2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy

Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.

Molecular chirality detection using plasmonic and dielectric nanoparticles

Nanoscale particles and structures hold promise in circular dichroism (CD) spectroscopy for overcoming the weakness of molecular CD signals. Significant effort have been made to characterize nanophotonic CD enhancement and find efficient ways to boost molecular chirality, but the best solution is yet to be found. In this paper, we present a rigorous analytic study of the nanophotonic CD enhancement of typical nanoparticles. We consider metallic and dielectric nanoparticles capped with chiral molecules and analyze the effect of multipolar nanoparticles on the molecular CD. We identify the spectral features of the molecular CD resulting from the electric and magnetic resonances of nanoparticles and suggest better ways to boost molecular chirality. We also clarify the contribution of particle scattering and absorption to the molecular CD and the dependence on particle size. Our work provides an exact analytic approach to nanophotonic CD enhancement and offers a rule for selecting the most efficient particle for sensitive molecular chirality detection.

Aqueous Solution Equilibria and Spectral Features of Copper Complexes with Tripeptides Containing Glycine or Sarcosine and Leucine or Phenylalanine

Copper(II) complexes of glycyl-L-leucyl-L-histidine (GLH), sarcosyl-L-leucyl-L-histidine (Sar-LH), glycyl-L-phenylalanyl-L-histidine (GFH) and sarcosyl-L-phenylalanyl-L-histidine (Sar-FH) have potential anti-inflammatory activity, which can help to alleviate the symptoms associated with rheumatoid arthritis (RA). From pH 2–11, the MLH, ML, MLH-1 and MLH-2 species formed. The combination of species for each ligand was different, except at the physiological pH, where CuLH-2 predominated for all ligands. The prevalence of this species was supported by EPR, ultraviolet-visible spectrophotometry, and mass spectrometry, which suggested a square planar CuN4 coordination. All ligands have the same basicity for the amine and imidazole-N, but the methyl group of sarcosine decreased the stability of MLH and MLH-2 by 0.1–0.34 and 0.46–0.48 log units, respectively. Phenylalanine increased the stability of MLH and MLH-2 by 0.05–0.29 and 1.19–1.21 log units, respectively. For all ligands, 1H NMR identified two coordination modes for MLH, where copper(II) coordinates via the amine-N and neighboring carbonyl-O, as well as via the imidazole-N and carboxyl-O. EPR spectroscopy identified the MLH, ML and MLH-2 species for Cu-Sar-LH and suggested a CuN2O2 chromophore for ML. DFT calculations with water as a solvent confirmed the proposed coordination modes of each species at the B3LYP level combined with 6-31++G**.

Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

Effects of Asymmetric Coupling Strength on Nonlinear Dynamics of Two Mutually Long-Delay-Coupled Semiconductor Lasers

This study investigates the effects of asymmetric coupling strength on nonlinear dynamics of two mutually long-delay-coupled semiconductor lasers through both experimental and numerical efforts. Dynamical maps and spectral features of dynamical states are analyzed as a function of the coupling strength and detuning frequency for a fixed coupling delay time. Symmetry in the coupling strength of the two lasers, in general, symmetrizes their dynamical behaviors and the corresponding spectral features. Slight to moderate asymmetry in the coupling strength moderately changes their dynamical behaviors from the ones when the coupling strength is symmetric, but does not break the symmetry of their dynamical behaviors and the corresponding spectral features. High asymmetry in the coupling strength not only strongly changes their dynamical behaviors from the ones when the coupling strength is symmetric, but also breaks the symmetry of their dynamical behaviors and the corresponding spectral features. Evolution of the dynamical behaviors from symmetry to asymmetry between the two lasers is identified. Experimental observations and numerical predictions agree not only qualitatively to a high extent but also quantitatively to a moderate extent.

Improved Locality Preserving Projection for Hyperspectral Image Classification in Probabilistic Framework

Unlabeled samples and transformation matrix are two main parts of unsupervised and semi-supervised feature extraction (FE) algorithms. In this manuscript, a semi-supervised FE method, locality preserving projection in the probabilistic framework (LPPPF), to find a sufficient number of reliable and unmixed unlabeled samples from all classes and constructing an optimal projection matrix is proposed. The LPPPF has two main steps. In the first step, a number of reliable unlabeled samples are selected based on the training samples, spectral features, and spatial information in the probabilistic framework. In this way, the spectral and spatial probability distribution function is calculated for each unlabeled sample. Therefore, the spectral features and spatial information are integrated together with a joint probability distribution function. Finally, a sufficient number of unlabeled samples with the highest joint probability distribution are selected. In the second step, the selected unlabeled samples are applied to construct the transformation matrix based on the spectral and spatial information of the unlabeled samples. The adjacency graph is improved by using new weights based on spectral and spatial information. This method is evaluated on three data sets: Indian Pines, Pavia University, and Kennedy Space Center (KSC) and compared with some recent and well-known supervised, semi-supervised, and unsupervised FE methods. Various experiments demonstrate the efficiency of the LPPPF in comparison with the other FE methods. LPPPF has also considerable performance with limited training samples.

Residual Magnetic Moment Influences the Features of Fe3s XPS Spectrum: A Case Study

Background: Fe 3sXPS spectrumexhibits doublet peak instead of predicted singlet peak based on spin-orbit coupling theory. This anomalous behavior is considered to be magnetic origin. However, the effect of residual magnetic moment to the features of Fe3s doublet peakis not understood fully. Objective: This study aims to verify the effect of residual magnetic moment on the spectral features of Fe3s XPS spectrum of magnetic material. Method: As a case study, we have carried out a high temperature XPS study of the Fe 3s spectrum of magnetic domain aligned (MDA) sample with composition composed of SrFe10.8Al1.2O19. In addition, the XPS data have been compared with the data acquired at different temperatures of magnetic domain non-aligned (MDNA) sample. Results: The results show that the majority peak intensity and minority peak width of Fe 3s spectrum of MDA are smaller than those of the MDNA sample, and they increase systematically with increasing temperature. However, it is noted that the features of Fe3s spectrum of both MDA and MDNA samples are completely overlapped near and above the Curie temperature, Tc ~ 670K. Conclusion: The analysis of XPS data suggests that the residual magnetic moment influences the spectral features of Fe3s spectrum. These results provide evidences that it is important to consider the contribution of residual magnetic moment while deriving information from Fe 3s XPS spectrum of MDA sample.