Spectral Library Research Articles

Coral Bleaching Detection Using PRISMA Hyperspectral Satellite Imagery in Calatagan, Batangas

Abstract. Coral bleaching arises due to stress from warming seas and pollution. Several studies on bleaching detection utilize multispectral imagery, due to data availability. In this study, the usage of PRISMA hyperspectral data imagery was assessed for coral bleaching detection in Calatagan, Batangas, and sea surface temperature (SST) for confirming bleaching occurrence. In-situ measurements were gathered for spectral data of live and bleached corals, and a collaborative qualitative data interview with a coral bleach patroller and municipal office was conducted to gain further information about bleaching details in the area. Sentinel-2 L2A was also used for comparison during classification. Spectral Angle Mapper (SAM) was utilized for image classification, while using field data and data from related studies for spectral library. A total of 27.12% relative to the coral reef area was classified as bleached corals. T-test statistics were done to analyze the live and bleached corals, which exhibited that only two (2) bands in the green region were found to be not separable. Using box plots, bleached corals showed to have generally lower reflectance values than live corals for RGB region, as compared to Sentinel, with bleached having higher values, which implies that spatial resolution can affect the spectral response of different features. Using ground truth data, 60.6% user’s accuracy for bleached corals, and 63.4% overall accuracy is achieved. Hence, hyperspectral satellite imagery can be utilized in coral bleaching detection, despite its limitations. Depth variables and diverse sample points are recommended for the betterment of coral bleaching detection.

The Proteomics Standards Initiative Standardized Formats for Spectral Libraries and Fragment Ion Peak Annotations: mzSpecLib and mzPAF.

Mass spectral libraries are collections of reference spectra, usually associated with specific analytes from which the spectra were generated, that are used for further downstream analysis of new spectra. There are many different formats used for encoding spectral libraries, but none have undergone a standardization process to ensure broad applicability to many applications. As part of the Human Proteome Organization Proteomics Standards Initiative (PSI), we have developed a standardized format for encoding spectral libraries, called mzSpecLib (https://psidev.info/mzSpecLib). It is primarily a data model that flexibly encodes metadata about the library entries using the extensible PSI-MS controlled vocabulary and can be encoded in and converted between different serialization formats. We have also developed a standardized data model and serialization for fragment ion peak annotations, called mzPAF (https://psidev.info/mzPAF). It is defined as a separate standard, since it may be used for other applications besides spectral libraries. The mzSpecLib and mzPAF standards are compatible with existing PSI standards such as ProForma 2.0 and the Universal Spectrum Identifier. The mzSpecLib and mzPAF standards have been primarily defined for peptides in proteomics applications with basic small molecule support. They could be extended in the future to other fields that need to encode spectral libraries for nonpeptidic analytes.

7Method for Detection of Naturally Occurring Toxins in Human Urine Using Liquid Chromatography High Resolution Mass Spectrometry.

Natural toxins present an ongoing risk for human exposure that requires a rapid, accurate diagnosis for proper response. In this study, a qualitative liquid chromatography high resolution mass spectrometry (LC-HRMS) method was developed and validated for the detection of a large, diverse selection of natural toxins. Data-dependent acquisition was performed to identify compounds with an in-house mass spectral library of 129 hazardous toxins that originate from plants, animals, and fungi. All 129 compounds were spiked into human urine, extracted, and evaluated for spectral library matching. Of these, 92 toxins met the quality criteria and underwent validation in urine matrix based on American National Standards Institute (ANSI) guidelines. A generalized workflow for method expansion was developed and enables the rapid addition of relevant compounds to the established method. This LC-HRMS method achieves efficient detection of natural toxins in urine, and the created workflow can rapidly increase compound coverage via method expansion.

Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California???s Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user???s accuracies of 95% to 100%.

Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T eff), surface gravity (log g), metallicity ([Fe/H]), and the abundance of alpha elements with respect to iron ([α/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for LAMOST. Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within 70 hr. We observed that the internal error dispersions for T eff, log g, [Fe/H], and [α/Fe] across different spectral types lie within the ranges of 15–594 K, 0.03–0.27 dex, 0.02–0.10 dex, and 0.01–0.04 dex, respectively, indicating a good consistency. A comparative analysis with external data highlighted deficiencies in the official LAMOST catalog and issues with MaStar parameters, as well as potential limitations of our method in processing spectra with strong emission lines and bad pixels. The derived atmospheric parameters as a part of this work are available via doi:10.12149/101402.

Simultaneous Analysis of 504 Pesticide Multiresidues in Crops Using UHPLC-QTOF at MS1 and MS2 Levels.

A robust analytical method was developed for the simultaneous detection of 504 pesticide multiresidues in various crops using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF). The method integrates both MS1 and MS2 levels through sequential window acquisition of all theoretical mass spectra (SWATH) analysis, allowing for accurate mass measurements and the construction of a spectral library to enhance pesticide residue identification. An evaluation of the method was carried out according to international standards, including the FAO guidelines and SANTE/11312/2021. Validation across five representative crops-potato, cabbage, mandarin, brown rice, and soybean-demonstrated exceptional sensitivity, with over 80% of the analytes detected at trace levels (≤2.5 μg/kg). Moreover, an impressive 96.8% to 98.8% of the compounds demonstrated LOQs of ≤10 μg/kg. Most compounds exhibited excellent linearity (r2 ≥ 0.980) and satisfactory recovery rates at spiking levels of 0.01 and 0.1 mg/kg. Among 42 crop samples analyzed, pesticides were detected in 1 cabbage, 3 mandarin, and 6 rice samples, with a mass accuracy within ±5 ppm and a Fit score ≥ 70.8, confirming the method's practical applicability and reliability. The detected residues ranged from 12.3 to 339.3 μg/kg, all below the established maximum residue limits (MRLs). This comprehensive approach offers an efficient, reliable, and scalable solution for pesticide multiresidue monitoring, supporting food safety programs and regulatory compliance.

Discovery of calcium sulfate at different hydration states on Mars - based on perseverance SHERLOC analysis

Organic matter on planets is often co-deposited with evaporites, and evaporation of lakes in the Jezero region of Mars may have led to the deposition of abundant organic matter and minerals such as sulfates, making sulfates important minerals for preserving the remains of potential life on Mars, especially in different hydration states, will help to invert the variability of the Martian environment and provide guidance for the exploration of life on Mars. Raman spectroscopy can detect the molecular composition of organic matter and acid anion information of the target of interest, thus enabling in situ analysis of life information and its living environment. In order to realize the detection of life remains on Mars, The Perseverance rover is equipped with SHERLOC in situ material detection payload, which adopts a 248.6 nm deep ultraviolet (DUV) laser to simultaneously excite Raman and fluorescence signals of the target. In this paper, five scans in three modes (survey scan, HDR scan and detail scan) from two sites on Mars (Quartier and Bellegarde) were selected for preliminary analysis, and the presence of sulfate minerals at these two sites was confirmed. The detail scans at Quartier were analyzed in depth, and the presence of four calcium sulfate minerals, gypsum (CaSO4∙2 H2O), bassanite (CaSO4∙0.5 H2O), anhydrite (β-CaSO4) and gamma-calcium sulfate (γ-CaSO4)was inferred from the states of ν1 (SO4), ν3 (SO4) and hydrated peaks. The differences in the degree of hydration of calcium sulfate minerals suggest that there may have been active water activity on Mars. By comparing data collected by SHERLOC with spectra from spectral libraries, the composition of small-scale structures on Mars may be assessable. However, due to the complexity of the Martian geology, more accurate water activity will need to be combined with more detailed ions, temperatures, humidity, and other conditions for a more precise analysis.

Proteome profiling in patients with peripartum cardiomyopathy according to ethnicity a biomarker study on the EORP cohort

Abstract Background Peripartum cardiomyopathy (PPCM) is a pregnancy-associated form of heart failure, which occurs in previously healthy women towards the end of pregnancy and in the first months postpartum. Previous reports showed a varying prevalence of PPCM across different regions and potentially a higher incidence in patients with black African ethnicity. The correct and timely diagnosis remains challenging and there is a need for novel disease-specific biomarkers. Accuracy in diagnosing PPCM can be improved by using serum-protein biomarkers such as QSOX1, adiponectin and ITIH3 in addition to NT-proBNP. However, it still needs to be ascertained whether there are ethnic differences in proteomic profiles amongst patients with PPCM with different ethnicities. Aims To delineate possible differences in serum protein biomarker profiles across different ethnic groups within the EURObservation Research Programme (EORP) multi-national cohort of women with PPCM. Methods Demographic and clinical data, as well as serum samples were collected from 84 patients with PPCM from seven EORP participating countries. Serum proteomic profiling was conducted using DIA-based label-free quantitative (LFQ) LC-MS at the time of diagnosis from depleted serum samples. Mass spectrometry data were analyzed by Spectronaut v15 using a study-specific spectral library. From the 84 patients in the EORP PPCM registry on whom we performed proteomic analyses, 82 patients self-declared their ethnicity. These 82 patients were assessed for any ethnical differences in their proteomic expression profile using unsupervised principal component analysis (PCA). Results In this cohort of 82 women with PPCM, the mean age was 30.5±6.7 years (Table 1). Three quarters of this cohort had no hypertension during pregnancy and only 13.8% had a prior diagnosis of PPCM. The mean left ventricular ejection fraction (LVEF) was 35% at time of first diagnosis, with no difference between the ethnic groups. However, women of Middle Eastern decent had more severe LV dilatation (LVEDD: 64.0 (62.0-65.0) mm). Three hundred and twenty-nine (329) proteins were identified in the serum samples. As depicted in Figure 1, PCA did not yield a distinct separation between the different ethnic groups. Principal component 1 (PC1) accounted for 15.2% of the total variability in protein expression among ethnic groups, PC2 explained a comparatively lower percentage at 12%. Conclusion The protein biomarkers identified in PPCM patients showed no discernible racial differences, demonstrating consistent performance across all ethnic groups.

A complementary approach for detecting biological signals through a semi-automated feature selection tool.

Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches. Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter. RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.

SLB-msSIM: a spectral library-based multiplex segmented SIM platform for single-cell proteomic analysis.

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by msSIM technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity among multiple cell lines and to analyze cellular trajectories during epithelial-mesenchymal transition. Our results demonstrate that SLB-msSIM is a highly sensitive and robust platform applicable to a wide range of single-cell proteomic studies.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Predicting USCS soil texture classes utilizing soil spectra and deep learning

Abstract Purpose Soil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general. Methods Two large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes. Results The results indicate that the Swin transformer model performed the best with an accuracy of 67% for six USCS soil texture class predictions and 81% for three USCS soil texture group predictions. Cohen’s kappa value implies a moderate agreement (0.55) for soil texture class predictions and a substantial agreement (0.64) for soil texture group predictions. Conclusion The proposed methodology offers a novel approach for USCS soil texture class predictions utilizing SSLs and deep learning techniques.

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models.

Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability

Abstract. Painted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the field of heritage conservation. In this study, hyperspectral remote sensing was used to invert the abundance of mixed pigments by spectral unmixing. Spectral variability effects are often present in hyperspectral images. Hyperspectral images of cultural artefacts also suffer from spectral variability due to factors such as particle size and impurities within the pigment, and acquisition conditions. Therefore, spectral variability was incorporated into the Linear Mixed Model of the pigment spectral unmixing. The unmixing methods are classified into two categories based on whether or not spectral libraries are used. In the experiment, computer synthesized data and laboratory-made sample blocks of mixed pigments, which mixed with different ratios of Azurite and Malachite, were selected as validation data. Five commonly used algorithms for solving the spectral variability problem, namely MESMA, Fractional Sparse SU, ELMM, RUSAL, and BCM, are used for pigment unmixing and compared with FCLS, which does not consider spectral variability. The results show that ELMM has the highest unmixing accuracy of aRMSE and xRMSE compared to other methods. At the same time, it can be seen from the metric of SAM that ELMM is able to extract reliable endmember variability spectral, which is more suitable for solving the problem of spectral variability in pigment unmixing. Finally, we apply ELMM to real artefacts Yungang Grottoes mural paintings, and obtain a lower xRMSE than FCLS, which improves the unmixing accuracy.

Study on radiation characteristics of multi-phase plumes containing ice crystals in orbit-control engines

In order to obtain the radiation characteristics of the multiphase plume containing ice crystals in the high-altitude orbit control engine and analyze its influence on the telemetry effect, this paper deduces the radiation characteristics parameters of gases based on Einstein radiation theory and radiation transfer equation. Based on the latest HITRAN2020 spectral line library solving gas radiation and the Mie theory of particle scattering solving particle radiation, the radiation characteristic parameters of the multi-phase plume containing ice crystals in the high-altitude orbital control engine and the radiation intensity distribution in the near-infrared and mid-long wave infrared bands are simulated, and the main influencing factors of the radiation are analyzed. The results show that, in general, the main radiation of the engine plume includes the scattered light in the near infrared band and the thermal radiation in the middle long wave infrared band. In the near-infrared band, considering solar background radiation, the total radiation amount in the calculation domain is about 10 −6 W/sr, and the thermal radiation is dominant within the axial distance of 0.5 m, while the ice crystal scattering is dominant beyond the axial distance of 0.5 m . In the mid-long wave infrared band, the total radiation amount in the calculation domain is about 10 W/sr, and the thermal radiation of gas dominates within 1 m axial distance, while the thermal radiation of ice crystal dominates beyond 1 m axial distance.

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Discriminant analysis using feature extraction from spectral domain responses to achieve accurate delineation for robust evaluation or classification of soil properties

ABSTRACT Soil spectroscopy offers a method for quantitatively analysing soil chromophores and employing a screening approach. However, utilizing general models with this data at once doesn’t consistently yield accurate results in determining soil parameters. Ambiguities in response within certain domains of the reflectance spectrum are observed. To address this issue, a method is proposed where quantities associated with spectral response are outputted within well-defined domains corresponding to specific parameters. This article introduces a novel approach centred on identifying features from reflectance spectra to assess soil properties and mitigate issues arising from a heterogeneous soil dataset. The focus is on swiftly automating the classification of soil spectral data by extracting features directly related to the varied composition of chromophores in the samples. These features are categorized for each chromophore parameter and analysed using linear discriminant analysis (LDA). The study employs the ‘309 soil samples’ from the GEO-CRADLE open spectral soil library in Greece. Specifically, relevant data from six spectral responses are extracted to separate and quantify certain soil parameters such as clay content, NO3− levels, and SOM (Soil Organic Matter), despite their interrelations. We developed a method to visually represent the determination outcomes quantitatively by establishing three threshold parameter values: 1.6% for SOM, 19% for clay, and 13.6 ppm for NO3−. These determinations will span the explored ranges of ’0.3 to 4.18%’ for SOM, “3 to 48%” for clay, and ’0 to 661.2 ppm’ for NO3−. Utilizing supervised LDA proves notable classification rates of 93.3% for the SOM task, 90% for the Clay task, and 86.6% for NO3− with the specific cut-off value parameter. The final result is presented as a map showing the positions of various prediction samples, highlighting the percentages of clay and organic matter relative to 19% and 1.6%, respectively, as well as the nitrate concentration in these samples relative to 13.6 ppm.

Marine Bacillus pumilus substances exhibit antimicrobial effect on multidrug-resistant Staphylococcus aureus.

This study aimed to assess the antimicrobial potential of Bp1-AdE, produced by Bacillus pumilus 64-1, and to investigate its mode of action against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA). Bp-1AdE, derived from sponge-associated B. pumilus, exhibited bactericidal activity at 1,550 µg ml-1 against S. aureus ATCC29213 and MRSA strains. Light and fluorescence microscopy revealed drastic cell lysis of S. aureus treated with Bp-1AdE. Scanning and transmission electron microscopy suggested that Bp-1AdE disrupts the cytoplasmic membrane. Toxicity assays showed that Bp-1AdE was non-toxic to Tenebrio molitor larvae. Liquid chromatography-mass spectrometry (LC-MS) and Global Natural Product Social (GNPS) spectral libraries identified four substances within Bp-1AdE, including aliphatic alcohols [3,4-dipentylhexane-2,5-diol and 1,1'-(4,5-dibutyl-3,6-dimethylcyclohexane-1,2-diyl)bis(ethan-1-one)] and terpenoids (cholic acid and canrenone). Bp-1AdE demonstrated selective toxicity and bactericidal activity, highlighting its potential for controlling infections caused by multidrug-resistant S. aureus strains.

An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.

In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.