Spectral Abundance Research Articles

A comparison of multivariate curve resolution with endmember extraction methods in hyperspectral Raman imaging

Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral imaging (HSI) results in a vast amount of data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures and concentration abundance maps of the active spectroscopic compounds, both endmember extraction (EX) and Multivariate Curve Resolution (MCR) techniques are widely employed. The objective of this study is to carry out a systematic investigation based on Raman mapping datasets to highlight the similarities and differences between these two approaches in retrieving pure variables, and ultimately provide guidelines for pure variable extraction. Numerical simulations and Raman mapping experiments on a mixture of pharmaceutical powders and on a layered plastic foil sample were conducted to underscore the distinctions between MCR and EX algorithms (in particular Vertex Component Analysis, VCA) and their outputs. Both methods were found to perform well if the dataset contains pure pixels for each of the individual components. However, in cases where such pure pixels do not exist, only MCR was found to be capable of extracting the pure component spectra.

Learning Single Spectral Abundance for Hyperspectral Subpixel Target Detection.

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection still faces an important bottleneck, that is, subpixel target detection. In this article, we propose a new detector by learning single spectral abundance for hyperspectral subpixel target detection (denoted as LSSA). Different from most existing hyperspectral detectors that are designed based on a match of the spectrum assisted by spatial information or focusing on the background, the proposed LSSA addresses the problem of detecting subpixel targets by learning a spectral abundance of the target of interest directly. In LSSA, the abundance of the prior target spectrum is updated and learned, while the prior target spectrum is fixed in a nonnegative matrix factorization (NMF) model. It turns out that such a way is quite effective to learn the abundance of subpixel targets and contributes to detecting subpixel targets in hyperspectral imagery (HSI). Numerous experiments are conducted on one simulated dataset and five real datasets, and the results indicate that the LSSA yields superior performance in hyperspectral subpixel target detection and outperforms its counterparts.

Attention-based Sparse and Collaborative Spectral Abundance Learning for Hyperspectral Subpixel Target Detection

The subpixel target detection in hyperspectral image processing persists as a formidable challenge. In this paper, we present a novel subpixel target detector termed attention-based sparse and collaborative spectral abundance learning for subpixel target detection in hyperspectral images. To help suppress background during subpixel target detection, the proposed method presents a pixel attention-based background sample selection method for background dictionary construction. Besides, the proposed method integrates a band attention-based spectral abundance learning model, replete with sparse and collaborative constraints, in which the band attention map can contribute to enhancing the discriminative ability of the detector in identifying targets from backgrounds. Ultimately, the detection result of the proposed detector is achieved by the learned target spectral abundance after solving the designed model using the alternating direction method of multipliers algorithm. Rigorous experiments conducted on four benchmark datasets, including one simulated and three real-world datasets, validate the effectiveness of the detector with the probability of detection of 90.88%, 96.86%, and 97.79% on the PHI, RIT Campus, and Reno Urban data, respectively, under fixed false alarm rate equal 0.01, indicating that the proposed method yields superior hyperspectral subpixel detection performance and outperforms existing methodologies.

Isomeric Separation of α2,3/α2,6-Linked 2-Aminobenzamide (2AB)-Labeled Sialoglycopeptides by C18-LC-MS/MS.

Determination of the relative expression levels of the α2,3/α2,6-sialic acid linkage isomers on glycoproteins is critical to the analysis of various human diseases such as cancer, inflammation, and viral infection. However, it remains a challenge to separate and differentiate site-specific linkage isomers at the glycopeptide level. Some derivatization methods on the carboxyl group of sialic acid have been developed to generate mass differences between linkage isomers. In this study, we utilized chemical derivatization that occurred on the vicinal diol of sialic acid to separate linkage isomers on a reverse-phase column using a relatively short time. 2-Aminobenzamide (2AB) labeling derivatization, including periodate oxidation and reductive amination, took only ∼3 h and achieved high labeling efficiency (>90%). Within a 66 min gradient, the sialic acid linkage isomers of 2AB-labeled glycopeptides from model glycoproteins can be efficiently resolved compared to native glycopeptides. Two different methods, neuraminidase digestion and higher-energy collision dissociation tandem mass spectrometry (HCD-MS2) fragmentation, were utilized to differentiate those isomeric peaks. By calculating the diagnostic oxonium ion ratio of Gal2ABNeuAc and 2ABNeuAc fragments, significant differences in chromatographic retention times and in mass spectral peak abundances were observed between linkage isomers. Their corresponding MS2 PCA plots also helped to elucidate the linkage information. This method was successfully applied to human blood serum. A total of 514 2AB-labeled glycopeptide structures, including 152 sets of isomers, were identified, proving the applicability of this method in linkage-specific structural characterization and relative quantification of sialic acid isomers.

Real-time discrimination of contamination source composed of multiple pollutants in surface water based on deep learning and UV–Vis spectral abundance estimation methodology

Water resources are one of the most important strategic resources for human survival and development. At present, surface water pollution incidents occur frequently, most of which are caused by enterprises' over-discharge, stolen discharge, and other activities to evade supervision. Automatic and rapid determination of pollution source types is conducive to further targeting pollution-causing enterprises and realizing scientific accountability in law enforcement. The existing method mainly adopts the pattern recognition method for pollution discrimination, which is only suitable for the situation of a single source of pollutant, and cannot identify the pollution for multiple pollution sources mixed surface water. To solve the problem of identification of mixed chemical pollutants in surface water pollution sources and identification of simultaneous emission of multiple pollution sources, a total pollution source analysis method based on spectral unmixing is proposed in this paper, which is a radial basis bilinear mixing model automatic encoder algorithm. The unsupervised autoencoder neural network method was used to solve the proportion of water pollution types by using the spectral database of water pollution sources to realize the identification function of water pollution types and determine the types of pollutant discharge enterprises. In this paper, surface water was collected as experimental samples, mixed with domestic sewage, industrial sewage, agricultural sewage, and other pollution sources, and simulated experiments were carried out to estimate the type and proportion of water pollution. Experimental results show that the detection accuracy of the proposed algorithm is significantly improved compared with the traditional algorithm. Among them, the accuracy of judging whether there is industrial sewage in the mixed experiment of three types of pollution is as high as 95.2%. This method provides an important basis for pollution source investigation and accountability.

Unraveling the blue shift in porphyrin fluorescence in glioma: The 620 nm peak and its potential significance in tumor biology.

In glioma surgery, the low-density infiltration zone of tumors is difficult to detect by any means. While, for instance, 5-aminolevulinic acid (5-ALA)-induced fluorescence is a well-established surgical procedure for maximizing resection of malignant gliomas, a cell density in tumor tissue of 20-30% is needed to observe visual fluorescence. Hyperspectral imaging is a powerful technique for the optical characterization of brain tissue, which accommodates the complex spectral properties of gliomas. Thereby, knowledge about the signal source is essential to generate specific separation (unmixing) procedures for the different spectral characteristics of analytes and estimate compound abundances. It was stated that protoporphyrin IX (PpIX) fluorescence consists mainly of emission peaks at 634 nm (PpIX634) and 620 nm (PpIX620). However, other members of the substance group of porphyrins fluoresce similarly to PpIX due to their common tetrapyrrole core structure. While the PpIX634 signal has reliably been assigned to PpIX, it has not yet been analyzed if PpIX620 might result from a different porphyrin rather than being a second photo state of PpIX. We thus reviewed more than 200,000 spectra from various tumors measured in almost 600 biopsies of 130 patients. Insufficient consideration of autofluorescence led to artificial inflation of the PpIX620 peak in the past. Recently, five basis spectra (PpIX634, PpIX620, flavin, lipofuscin, and NADH) were described and incorporated into the analysis algorithm, which allowed more accurate unmixing of spectral abundances. We used the improved algorithm to investigate the PpIX620 signal more precisely and investigated coproporphyrin III (CpIII) fluorescence phantoms for spectral unmixing. Our findings show that the PpIX634 peak was the primary source of the 5-ALA-induced fluorescence. CpIII had a similar spectral characteristic to PpIX620. The supplementation of 5-ALA may trigger the increased production of porphyrins other than PpIX within the heme biosynthesis pathway, including that of CpIII. It is essential to correctly separate autofluorescence from the main PpIX634 peak to analyze the fluorescence signal. This article highlights the need for a comprehensive understanding of the spectral complexity in gliomas and suggests less significance of the 620 nm fluorescence peak for PpIX analysis and visualization.

Mixture-Net: Low-rank deep image prior inspired by mixture models for spectral image recovery

This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction. Unlike previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns the prior information through the network. Mixture-Net consists of a deep generative model whose layers are inspired by the linear and non-linear low-rank mixture models, where the recovered image is composed of a weighted sum between the linear and non-linear decomposition. Mixture-Net also provides a low-rank decomposition interpreted as the spectral image abundances and endmembers, helpful in achieving remote sensing tasks without running additional routines. The experiments show the Mixture-Net effectiveness outperforming state-of-the-art methods in recovery quality with the advantage of architecture interpretability.

Structure-Activity Relationship of Novel ACE Inhibitory Undecapeptides from Stropharia rugosoannulata by Molecular Interactions and Activity Analyses.

Undecapeptide is the central peptide molecule in the peptide base material of Stropharia rugosoannulata, and angiotensin-converting enzyme (ACE) plays a crucial role in hypertension. To fully explore the interaction mechanism and ACE-inhibitory activity of long-chain peptides from Stropharia rugosoannulata, the binding conformations of twenty-seven undecapeptides with the ACE receptor were revealed by molecule docking. The undecapeptide GQEDYDRLRPL with better receptor binding capacity and higher secondary mass spectral abundance was screened. All amino acid residues except proline in GQEDYDRLRPL interacted with the ACE receptor. GQEDYDRLRPL interfered with the receptor's overall structure, with significant fluctuations in amino acid residues 340-355, including two residues in the receptor's active pockets. The binding constants of GQEDYDRLRPL to the ACE receptors were at the μM level, with a kinetic binding constant of 9.26 × 10-7 M, which is a strong binding, and a thermodynamic binding constant of 3.06 × 10-6 M. Intermolecular interaction were exothermic, enthalpy-driven, and specific binding reactions. GQEDYDRLRPL had an IC50 value of 164.41 μmol/L in vitro and superior antihypertensive effects at low-gavage administration in vivo. Obtaining information on the interaction mechanism of ACE-inhibitory undecapeptides from S. rugosoannulata with the ACE receptor will help to develop and utilize ACE inhibitors of natural origin.

LC–MS/MS based characterisation and differential expression of proteins in Himalayan snow trout, Schizothorax labiatus using LFQ technique

Molecular characterization of fish muscle proteins are nowadays considered as a key component to understand the role of specific proteins involved in various physiological and metabolic processes including their up and down regulation in the organisms. Coldwater fish specimens including snow trouts hold different types of proteins which help them to survive in highly diversified temperatures fluctuating from 0 to 20 °C. So, in current study, the liquid chromatography mass spectrometry using label free quantification technique has been used to investigate the muscle proteome profile of Schizothorax labiatus. For proteomic study, two weight groups of S. labiatus were taken from river Sindh. The proteomic analysis of group 1 revealed that a total of 235 proteins in male and 238 in female fish were recorded. However, when male and female S. labiatus were compared with each other on the basis of spectral count and abundance of peptides by ProteinLynx Global Server software, a total of 14 down-regulated and 22 up-regulated proteins were noted in this group. The highly down-regulated ones included homeodomain protein HoxA2b, retinol-binding protein 4, MHC class II beta chain and proopiomelanocortin while as the highly expressed up-regulated proteins comprised of gonadotropin I beta subunit, NADH dehydrogenase subunit 4, manganese superoxide dismutase, recombinase-activating protein 2, glycosyltransferase, chymotrypsin and cytochrome b. On the other hand, the proteomic characterisation of group 2 of S. labiatus revealed that a total of 227 proteins in male and 194 in female fish were recorded. When male and female S. labiatus were compared with each other by label free quantification, a total of 20 down-regulated and 18 up-regulated proteins were recorded. The down-regulated protein expression of group 2 comprised hepatic lipase, allograft inflammatory factor-1, NADH dehydrogenase subunit 4 and myostatin 1 while the highly expressed up-regulated proteins included glycogen synthase kinase-3 beta variant 2, glycogen synthase kinase-3 beta variant 5, cholecystokinin, glycogen synthase kinase-3 beta variant 3 and cytochrome b. Significant (P < 0.05) difference in the expression of down-regulated and up-regulated proteins was also noted between the two sexes of S. labiatus in each group. According to MS analysis, the proteins primarily concerned with the growth, skeletal muscle development and metabolism were down-regulated in river Sindh, which indicates that growth of fish during the season of collection i.e., winter was slow owing to less food availability, gonad development and low metabolic activity. While, the proteins related to immune response of fish were also noted to be down-regulated thereby signifying that the ecosystem has less pollution loads, microbial, pathogenic and anthropogenic activities. It was also found that the proteins involved in glycogen metabolism, reproductive and metabolic processes, particularly lipid metabolism were up-regulated in S. labiatus. The significant expression of these proteins may be connected to pre-spawning, gonad development and use of stored food as source of energy. The information generated in this study can be applied to future research aimed at enhancing food traceability, food safety, risk management and authenticity analysis.

Scalar induced gravitational waves in modified teleparallel gravity theories

Primordial black holes (PBHs) forming out of the collapse of enhanced cosmological perturbations provide access to the early Universe through their associated observational signatures. In particular, enhanced cosmological perturbations collapsing to form PBHs are responsible for the generation of a stochastic gravitational-wave background (SGWB) induced by second-order gravitational interactions, usually called scalar induced gravitational waves (SIGWs). This SGWB is sensitive to the underlying gravitational theory; hence it can be used as a novel tool to test the standard paradigm of gravity and constrain possible deviations from general relativity. In this work, we study the aforementioned GW signal within modified teleparallel gravity theories, developing a formalism for the derivation of the GW spectral abundance within any form of gravitational action. At the end, working within viable $f(T,\ensuremath{\phi})$ models without matter-gravity couplings, and accounting for the effect of monoparametric $f(T)$ gravity at the level of the source and the propagation of the tensor perturbations, we show that the respective GW signal is indistinguishable from that within GR. Interestingly, we find that in order to break the degeneracy between different $f(T)$ theories through the portal of SIGWs one should necessarily consider nonminimal matter-gravity couplings at the level of the gravitational action.

A direct examination of microbial specialized metabolites associated with ocean sediments from Baffin Bay and the Gulf of Maine.

Specialized metabolites produced by microorganisms found in ocean sediments display a wide range of clinically relevant bioactivities, including antimicrobial, anticancer, antiviral, and anti-inflammatory. Due to limitations in our ability to culture many benthic microorganisms under laboratory conditions, their potential to produce bioactive compounds remains underexplored. However, the advent of modern mass spectrometry technologies and data analysis methods for chemical structure prediction has aided in the discovery of such metabolites from complex mixtures. In this study, ocean sediments were collected from Baffin Bay (Canadian Arctic) and the Gulf of Maine for untargeted metabolomics using mass spectrometry. A direct examination of prepared organic extracts identified 1468 spectra, of which ∼45% could be annotated using in silico analysis methods. A comparable number of spectral features were detected in sediments collected from both locations, but 16S rRNA gene sequencing revealed a significantly more diverse bacterial community in samples from Baffin Bay. Based on spectral abundance, 12 specialized metabolites known to be associated with bacteria were selected for discussion. The application of metabolomics directly on marine sediments provides an avenue for culture-independent detection of metabolites produced under natural settings. The strategy can help prioritize samples for novel bioactive metabolite discovery using traditional workflows.

Compositional Variations in Sedimentary Deposits in Gale Crater as Observed by ChemCam Passive and Active Spectra

AbstractDuring the first 2934 sols of the Curiosity rover's mission 33,468 passive visible/near‐infrared (NIR) reflectance spectra were taken of the surface by the mast‐mounted Chemistry and Camera (ChemCam) instrument on a range of target types. ChemCam spectra of bedrock targets from the Murray and Carolyn Shoemaker formations on Mt. Sharp were investigated using principal component analysis and various spectral parameters including the band depth at 535 nm and the slope between 840 and 750 nm. Four end‐member spectra were identified. Passive spectra were compared to Laser Induced Breakdown Spectroscopy (LIBS) data to search for correlations between spectral properties and elemental abundances. The correlation coefficient between FeOT reported by LIBS and BD535 from passive spectra was used to search for regions where iron may have been added to the bedrock through oxidation of ferrous‐bearing fluids but no correlations were found. Rocks in the Blunts Point‐Sutton Island transition that have unique spectral properties compared to surrounding rocks, that is flat NIR slopes and weak 535 nm absorptions, are associated with higher Mn and Mg in the LIBS spectra of bedrock. Additionally, calcium‐sulfate cements, previously identified by Ca and S enrichments in the LIBS spectra of bedrock, were also shown to be associated with spectral trends seen in Blunts Point. A shift toward a steeper NIR slope is seen in the Hutton interval, indicative of changing depositional conditions or increased diagenesis.

Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection

Detecting subpixel targets is a considerably challenging issue in hyperspectral image processing and interpretation. Most of the existing hyperspectral subpixel target detection methods construct detectors based on the linear mixing model which regards a pixel as a linear combination of different spectral signatures. However, due to the multiple scattering, the linear mixing model cannot​ illustrate the multiple materials interactions that are nonlinear and widespread in real-world hyperspectral images, which could result in unsatisfactory performance in detecting subpixel targets. To alleviate this problem, this work presents a novel collaborative-guided spectral abundance learning model (denoted as CGSAL) for subpixel target detection based on the bilinear mixing model in hyperspectral images. The proposed CGSAL detects subpixel targets by learning a spectral abundance of the target signature in each pixel. In CGSAL, virtual endmembers and their abundance help to achieve good accuracy for modeling nonlinear scattering accounts for multiple materials interactions according to the bilinear mixing model. Besides, we impose a collaborative term to the spectral abundance learning model to emphasize the collaborative relationships between different endmembers, which contributes to accurate spectral abundance learning and further help to detect subpixel targets. Plentiful experiments and analyses are conducted on three real-world and one synthetic hyperspectral datasets to evaluate the effectiveness of the CGSAL in subpixel target detection. The experiment results demonstrate that the CGSAL achieves competitive performance in detecting subpixel targets and outperforms other state-of-the-art hyperspectral subpixel target detectors.

Application of hyperspectral imaging technology to digitally protect murals in the Qutan temple

Hyperspectral imaging technology is a research hotspot in the field of cultural heritage protection. It can be used to quickly and noninvasively obtain detailed spectral information from the surfaces of cultural relics of different categories. We can intuitively analyse pigment compositions, line characteristics, painting skills and patterns using spectral information. Hyperspectral imaging has high scientific significance and application value for the protection, restoration and research of ancient murals and other cultural relics. In this study, a mural from Daheitian hall in the Qutan temple, Qinghai Province, China, was used as a sample. The hyperspectral data were acquired and analysed for several purposes. Pigment spectral matching and abundance inversion were carried out to obtain the pigment distribution. These data were enhanced by continuum removal and histogram stretching to obtain hidden information. The dark channel prior, Criminisi and Retinex methods were used to virtually restore the image of the mural. The results indicated that by using hyperspectral imaging data, the constructed pure pigment spectrum library and suitable approaches, the types and distributions of mural pigments can be quantitatively analysed, and the lines in murals can be extracted. Hyperspectral images are helpful for identifying information hidden by pigments or surface materials. Mural images can be enhanced, and hidden information can be highlighted using enhancement methods, such as continuum removal and histogram linear stretching. In addition, hyperspectral imaging data have unique advantages in the restoration of mural images, and the combination of defogging methods and image inpainting algorithms can realize the virtual restoration of mural images. In brief, hyperspectral imaging technology was found to have a highly favourable effect on pigment analysis, line extraction, information enhancement, hidden information extraction and the virtual restoration of ancient murals.

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Hyperspectral target detection is a task of primary importance in remote sensing since it allows for location and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with an analogous counterpart by showing the effectiveness of the proposed procedure.

Hyperspectral Remote Sensing Image Synthesis Based on Implicit Neural Spectral Mixing Models

Hyperspectral image (HSI) synthesis, as an emerging research topic, is of great value in overcoming sensor limitations and achieving low-cost acquisition of high-resolution remote sensing HSIs. However, the linear spectral mixing model used in recent studies oversimplifies the real-world hyperspectral imaging process, making it difficult to effectively model the imaging noise and multiple reflections of the object spectrum. As a prerequisite for hyperspectral data synthesis, accurate modeling of nonlinear spectral mixtures has long been a challenge. Considering the above difficulties, we propose a novel method for modeling nonlinear spectral mixtures based on implicit neural representations (INRs) in this article. The proposed method learns from INR and adaptively implements different mixture models for each pixel according to their spectral signature and surrounding environment. Based on the above neural mixing model, we also propose a new method for HSI synthesis. Given an RGB image as input, our method can generate an accurate and physically meaningful HSI. As a set of by-products, our method can also generate subpixel-level spectral abundance as well as the solar atmosphere signature. The whole framework is trained end-to-end in a self-supervised manner. We constructed a new dataset for HSI synthesis based on a wide range of Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Our method achieves a mean peak signal-to-noise ratio (MPSNR) of 52.36 dB and outperforms other state-of-the-art hyperspectral synthesis methods. Finally, our method shows great benefits to downstream data-driven applications. With the HSIs and abundance directly generated from low-cost RGB images, the proposed method improves the accuracy of HSI classification tasks by a large margin, particularly for those with limited training samples.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples.

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Cardiac Alarmins as Residual Risk Markers of Atherosclerosis under Hypolipidemic Therapy.

Increased levels of low-density lipoproteins are the main risk factor in the initiation and progression of atherosclerosis. Although statin treatment can effectively lower these levels, there is still a residual risk of cardiovascular events. We hypothesize that a specific panel of stress-sensing molecules (alarmins) could indicate the persistence of silent atherosclerosis residual risk. New Zealand White rabbits were divided into: control group (C), a group that received a high-fat diet for twelve weeks (Au), and a treated hyperlipidemic group with a lipid diet for eight weeks followed by a standard diet and hypolipidemic treatment (atorvastatin and PCSK9 siRNA-inhibitor) for four weeks (Asi). Mass spectrometry experiments of left ventricle lysates were complemented by immunologic and genomic studies to corroborate the data. The hyperlipidemic diet determined a general alarmin up-regulation tendency over the C group. A significant spectral abundance increase was measured for specific heat shock proteins, S100 family members, HMGB1, and Annexin A1. The hypolipidemic treatment demonstrated a reversed regulation trend with non-significant spectral alteration over the C group for some of the identified alarmins. Our study highlights the discriminating potential of alarmins in hyperlipidemia or following hypolipidemic treatment. Data are available via ProteomeXchange with identifier PXD035692.

ZY-1 02D Hyperspectral Imagery Super-Resolution via Endmember Matrix Constraint Unmixing

This paper proposes an endmember matrix constraint unmixing method for ZY-1 02D hyperspectral imagery (HSI) super-resolution reconstruction (SRR) to overcome the low resolution of ZY-1 02D HSI. The proposed method combines spectral unmixing and adds novel smoothing constraints to traditional non-negative matrix factorization to improve details and preserve the spectral information of traditional SRR methods. The full utilization of the endmember spectral matrix and endmember abundance matrix of HSI and multispectral imagery (MSI) reconstructs the high spatial resolution and high spectral fidelity HSI. Furthermore, given the ZY-1 02D HSI infrared bands are seriously corrupted by noise, the influence of denoising on the SRR accuracy is also discussed. Experiments show that the proposed method restores spatial details and spectral information and is robust for noise, preserving more spectral information. Therefore, the proposed method is a ZY-1 02D HSI SRR method with high spatial resolution and high spectral fidelity, which improves the spatial resolution while simultaneously solving spectral mixing and provides the possibility for the data further expansion.

Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning

Compressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder. Reconstruction algorithms play a pivotal role in compressive imaging systems. Traditional model-based reconstruction approaches are computationally burdensome and achieve limited success. Deep learning-based approaches, while improving in reconstruction accuracy and speed, depend heavily on data, which is a major challenge for satellite-borne hyperspectral compressed imaging. In this article, we combine the respective advantages of model-based and learning-based approaches in a distributed compressed hyperspectral sensing framework, employing linear mixed model assumptions and spectral library learning to simultaneously improve the reconstruction speed and accuracy without using a large amount of additional hyperspectral data. First, the relationship between the CS band and the key band is learned from the spectral library to ensure that the key band endmembers can be accurately predicted. Then, the joint horizontal and vertical difference operators are proposed to enhance the estimation of the initial values of abundance. Finally, the CS band endmembers and residuals are updated in the reconstruction module to deal with the endmember and abundance mismatch. Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods.