Search Results (73)

Based on low-resolution OSIRIS/GTC optical spectra, we assign spectral classes to 38 poorly studied ultracool/brown dwarf candidates from the 2MASS database. For almost all of the targets, this is the first optical spectral classification. For the dwarfs showing H$\alpha$ emission, we calculate the ratio of H$\alpha$ to bolometric luminosity, which is the most common characteristic of magnetic activity in cool stars. For the others, we give 3$\sigma$ upper limits. We also include estimates of the effective temperatures and log g and distances from Gaia based on a comparison with models. For one of our targets—LSR J2105+2514, previously classified as a dwarf carbon star—we confirm this classification and report H$\alpha$ and H$\beta$ line emission in the spectrum for the first time. Dwarf carbon stars (dC) are low-mass main sequence stars that have undergone mass-transfer binary evolution. The Balmer line emission from these objects most likely indicates coronal activity of the dwarf, which in turn may be due to either intrinsic magnetic activity or spin-up from accretion or tidal locking. Full article

Background/Objectives: Anaerobic gut fungi (Neocallimastigomycota) are biotechnologically relevant, lignocellulose-degrading microbes with under-explored biosynthetic potential for secondary metabolites. Untargeted metabolomic profiling with gas chromatography–mass spectrometry (GC-MS) was applied to two gut fungal strains, Anaeromyces robustus and Caecomyces churrovis, to establish a foundational metabolomic dataset to identify metabolites and provide insights into gut fungal metabolic capabilities. Methods: Gut fungi were cultured anaerobically in rumen-fluid-based media with a soluble substrate (cellobiose), and metabolites were extracted using the Metabolite, Protein, and Lipid Extraction (MPLEx) method, enabling metabolomic and proteomic analysis from the same cell samples. Samples were derivatized and analyzed via GC-MS, followed by compound identification by spectral matching to reference databases, molecular networking, and statistical analyses. Results: Distinct metabolites were identified between A. robustus and C. churrovis, including 2,3-dihydroxyisovaleric acid produced by A. robustus and maltotriitol, maltotriose, and melibiose produced by C. churrovis. C. churrovis may polymerize maltotriose to form an extracellular polysaccharide, like pullulan. GC-MS profiling potentially captured sufficiently volatile products of proteomically detected, putative non-ribosomal peptide synthetases and polyketide synthases of A. robustus and C. churrovis. The triterpene squalene and triterpenoid tetrahymanol were putatively identified in A. robustus and C. churrovis. Their conserved, predicted biosynthetic genes—squalene synthase and squalene tetrahymanol cyclase—were identified in A. robustus, C. churrovis, and other anaerobic gut fungal genera. Conclusions: This study provides a foundational, untargeted metabolomic dataset to unmask gut fungal metabolic pathways and biosynthetic potential and to prioritize future efforts for compound isolation and identification. Full article

Background: Liuwei Dihuang Pills, a classic traditional Chinese medicine formula, has been widely used in clinical practice for its multiple pharmacological effects. However, the systematic characterization and identification of its chemical constituents, especially the aqueous decoction, remain insufficient, which hinders in-depth research on its pharmacodynamic material basis. Thus, there is an urgent need for a comprehensive analysis of its chemical components using advanced analytical techniques. Methods: After screening chromatographic columns, the ACQUITY UPLC™ HSS T3 column (100 mm × 2.1 mm, 1.8 μm) was selected. The column temperature was set to 40 °C, and the mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). A gradient elution program was adopted, and the separation was completed within 20 min. Ultra-high performance liquid chromatography–Orbitrap mass spectrometry (UPLC-Orbitrap-MS) combined with a self-established information database was used for the analysis. Results: A total of 80 compounds were tentatively identified, including 13 monoterpenoids, 6 phenolic acids, 16 iridoids, 11 flavonoids, 25 triterpenoids, and 9 other types. Triterpenoids are mainly derived from Poria cocos and Alisma orientale; iridoids are mainly from Rehmannia glutinosa; monoterpenoids are mainly from Moutan Cortex; and flavonoids are mainly from Dioscorea opposita. Among them, monoterpenoids, iridoids, and triterpenoids are important pharmacodynamic components. The cleavage pathways of typical compounds (such as pachymic acid, catalpol, oxidized paeoniflorin, and puerarin) are clear, and their mass spectral fragment characteristics are consistent with the literature reports. Conclusions: Through UPLC-Orbitrap-MS technology and systematic optimization of conditions, this study significantly improved the coverage of chemical component identification in Liuwei Dihuang Pills, providing a comprehensive reference for the research on its pharmacodynamic substances. However, challenges remain in the identification of trace components and isomers. In the future, analytical methods will be further improved by combining technologies such as ion mobility mass spectrometry or multi-dimensional liquid chromatography. Full article

Non-intentionally added substances (NIAS) in plastic food contact materials represent a critical undercharacterized chemical safety concern, caused by their inherent diversity, potential toxicity, and regulatory challenges. This review synthesizes recent advances and persistent gaps in NIAS analysis, with a primary focus on analytical workflows for non-targeted analysis, alongside a consideration of risk assessment and toxicological prioritization frameworks. Conventional plastics (e.g., polyethylene, polypropylene, or polyethylene terephthalate) as well as emerging materials (e.g., bioplastics and recycled polymers) exhibit different NIAS profiles, including oligomers, degradation products, additives, and contaminants, requiring specific approaches for migration testing, extraction, and detection. Advanced techniques, such as ultra-high-performance liquid chromatography or two-dimensional gas chromatography coupled with high-resolution mass spectrometry, have enabled non-targeted analysis approaches. However, the field remains constrained by spectral library gaps, limited reference standards, and inconsistent data processing protocols, resulting in heavy reliance on tentative identifications. Risk assessment procedures mainly employ the Threshold of Toxicological Concern and classification by Cramer’s rules. Nevertheless, addressing genotoxicity, mixture effects, and novel hazards from recycled or bio-based polymers remains challenging with these approaches. Future priorities and efforts may include expanding spectral databases, harmonizing analytical protocols, and integrating in vitro bioassays with computational toxicology to refine hazard characterization. Full article

With the global rise in antimicrobial resistance (AMR), rapid and reliable microbial diagnostics have become more critical than ever. Traditional culture-based and molecular diagnostic techniques often fall short in terms of speed, cost-efficiency, or scalability, particularly in resource-limited settings. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) has emerged as a transformative tool in clinical microbiology. Its unparalleled speed and accuracy in microbial identification, along with expanding applications in AMR profiling, make it a leading candidate for next-generation diagnostic workflows. This review aims to provide a comprehensive update on recent advances in MALDI–TOF MS, focusing on its technological evolution, clinical applications, and future potential in microbial diagnostics and resistance detection. We conducted a critical synthesis of peer-reviewed literature published over the last decade, with emphasis on innovations in sample preparation, instrumentation, data interpretation, and clinical integration. Key developments in AMR detection, including growth-based assays, resistance biomarker profiling, and machine learning-driven spectral analysis, are discussed. MALDI–TOF MS is increasingly deployed not only in clinical laboratories but also in environmental surveillance, food safety, and military biodefense. Despite challenges such as database variability and limited access in low-income regions, it remains a cornerstone of modern microbial diagnostics and holds promise for future integration into global AMR surveillance systems. Full article

Studying the host galaxies of fast radio bursts (FRBs) is critical to understanding the formation processes of their sources and, hence, the mechanisms by which they radiate. Toward this end, we have extended the Blinkverse database version 1.0, which already included burst information about FRBs observed by various telescopes, by adding information about 92 published FRB host galaxies to make version 2.0. Each FRB host has 18 parameters describing it, including redshift, stellar mass, star-formation rate, emission line fluxes, etc. In particular, each FRB host includes images collated by FASTView, streamlining the process of looking for clues to understanding the origin of FRBs. FASTView is a tool and API for quickly exploring astronomical sources using archival imaging, photometric, and spectral data. This effort represents the first step in building Blinkverse into a comprehensive tool for facilitating source observation and analysis. Full article

Structural characterization of natural products in complex herbal extracts remains a major challenge in phytochemical analysis. In this study, we present a novel post-acquisition data-processing strategy—key ion diagnostics–neutral loss filtering (KID-NLF)—combined with ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) for systematic profiling of the medicinal plant Terminalia chebula. The strategy consists of four main steps. First, untargeted data are acquired in negative electrospray ionization (ESI⁻) mode. Second, a genus-specific diagnostic ion database is constructed by leveraging characteristic fragment ions (e.g., gallic acid, chebuloyl, and HHDP groups) and conserved substructures. Third, MS/MS data are high-resolution filtered using key ion diagnostics and neutral loss patterns (302 Da for HHDP; 320 Da for chebuloyl). Finally, structures are elucidated via detailed spectral analysis. The methanol extract of T. chebula was separated on a C18 column using a gradient of acetonitrile and 0.1% aqueous formic acid within 33 min. This separation enabled detection of 164 compounds, of which 47 were reported for the first time. Based on fragmentation pathways and diagnostic ions (e.g., m/z 169 for gallic acid, m/z 301 for ellagic acid, and neutral losses of 152, 302, and 320 Da), the compounds were classified into three major groups: gallic acid derivatives, ellagitannins (containing HHDP, chebuloyl, or neochebuloyl moieties), and triterpenoid glycosides. KID-NLF overcomes key limitations of conventional workflows—namely, isomer discrimination and detection of low-abundance compounds—by exploiting genus-specific structural signatures. This strategy demonstrates high efficiency in resolving complex polyphenolic and triterpenoid profiles and enables rapid annotation of both known and novel metabolites. This study highlights KID-NLF as a robust framework for phytochemical analysis in species with high chemical complexity. It also paves the way for applications in quality control, drug discovery, and mechanistic studies of medicinal plants. Full article

Theobroma grandiflorum (copoazu) is a plant native to South America, widely cultivated in countries within the Amazon region. Its unique phytochemical composition imparts distinctive organoleptic properties, making it an exotic fruit. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass spectrometry (GC-MS) was used to identify the volatile organic compounds (VOCs) produced by copoazu. The optimal conditions for sample pretreatment were first determined using a Design of Experiments (DoE) approach. Analysis of the volatile profiles enabled the identification of 96 copoazu VOCs across three ripening stages. Of these, 79 VOCs were classified into chemical compound families using spectral correlation analysis across various libraries and databases, as well as molecular network analysis. Additionally, a volatilomic analysis was conducted to examine the changes in VOCs throughout the ripening process. Molecular network analysis showed that the VOCs emitted by the fruit are linked to the interconversion of compounds, which can be observed through the study of the metabolic pathways. These findings provide a comprehensive analysis of the copoazu volatilome, providing valuable insights into the organoleptic characteristics of this Amazonian fruit. Esters and terpenes such as α-terpineol, trans-4-methoxythujane, linalool, 2-methylbutyl butanoate, 3-methylbut-2-enoic acid, 2-methylpentyl ester, and 2-methylpropyl hexanoate were identified as potential biomarkers associated with the copoazu ripening process. Full article

Be stars are rapidly rotating B-type stars surrounded by circumstellar disks formed from self-ejected material. Understanding the mechanisms driving mass ejection and disk formation, known as the Be phenomenon, requires a detailed investigation of their variability and underlying physical processes. In this study, we analyze the photometric, spectroscopic, and seismic characteristics of three Be stars—HD 212044, 28 Cyg, and HD 174237—using high-cadence data from the TESS mission and spectral data from the BeSS database. Photometric variability was analyzed through iterative prewhitening and wavelet techniques, revealing distinct frequency groups associated with non-radial pulsations (NRPs). Spectral data provided equivalent width measurements of the H$\alpha$ line, which correlated with photometric changes, reflecting dynamic interactions between the stars and their disks. Seismic analysis identified core rotation rates and buoyancy travel times for HD 212044 and 28 Cyg, offering insights into internal stellar processes and angular momentum distribution. HD 212044 exhibits a strong negative correlation between photometric brightness and H$\alpha$ equivalent width, whereas this correlation is weaker in the case of 28 Cyg. The findings for these two stars highlight the interplay between NRPs, rapid rotation, and circumstellar disk dynamics. In contrast, the case of HD 174237 presents an example of how a binary system with mass transfer and a B-type component is revealed when observed simultaneously with space-based photometry and ground-based spectroscopy, demonstrating the importance of distinguishing classical Be stars from interacting binaries. Full article

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation. Full article

Betalains, which contain nitrogen and are water soluble, are the pigments responsible for many traits of plants and biological activities in different organisms that do not produce them. To better annotate and identify betalains using a spectral library and fingerprint, a database catalog of 140 known betalains (112 betacyanins and 28 betaxanthins) was made in this work to simplify betalain identification in mass spectrometry analysis. Fragmented peaks obtained using MassFrontier, along with chemical structures and protonated precursor ions for each betalain, were added to the database. Product ions made in MS/MS and multistage MS analyses of betanin, beetroot extract, and red pitaya extract revealed the fingerprint of betalains, distinctive ions of betacyanin, betacyanin derivatives such as decarboxylated and dehydrogenated betacyanins, and betaxanthins. A distinctive ion with m/z 211.07 was found in betaxanthins. By using the fingerprint of betalains in the analysis of red pitaya extracts, the catalog of betalains in red pitaya was expanded to 86 (31 betacyanins, 36 betacyanin derivatives, and 19 betaxanthins). Four unknown betalains were detected to have the fingerprint of betalains, but further research will aid in revealing the complete structure. Taken together, we envisage that the further use of the fingerprint of betalains will increase the annotation coverage of identified molecules in studies related to revealing the biological function of betalains or making technologies based on these natural colorants. Full article

Ectopic pregnancy (EP) is the leading cause of maternal morbidity and mortality in the first trimester. Using an untargeted metabolomic approach, we sought to identify putative plasma biomarkers using tandem liquid chromatography–mass spectrometry for the detection of tubal EP. This case-control study included the prospective recruitment of 50 tubal EP cases and 50 early intrauterine pregnancy controls. To avoid over-fitting, logistic regression models were developed in a randomly selected discovery group (30 cases vs. 30 controls) and validated in the test group (20 cases vs. 20 controls). In total, 585 mass spectral features were detected, of which 221 molecular features were significantly altered in EP plasma (p < 0.05). Molecular networking and metabolite identification was employed using the Global Natural Products Social Molecular Networking (GNPS) database, which identified 97 metabolites at a high confidence level. Top significant metabolites include subclasses of sphingolipids, carnitines, glycerophosphocholines, and tryptophan metabolism. The top regression model, consisting of D-erythro-sphingosine and oleoyl-carnitine, was validated in a test group and achieved an area under receiving operating curve (AUC) (95% CI) = 0.962 (0.910–1) with a sensitivity of 100% and specificity of 95.9%. Metabolite alterations indicate alterations related to inflammation and abnormal placentation in EP. The validation of these metabolite biomarkers in the future could potentially result in improved early diagnosis. Full article

Wendan (a type of pomelo), a popular fruit in China, is less known in other countries. Its peel is widely used in food and traditional medicine. Four different origins (Xuzhou, Taizhou, Zhangzhou, and Meizhou) of Wendan pomelo were selected, and crude extracts were obtained by the Soxhlet extraction method. The composition of Wendan peel was analyzed by high-performance liquid chromatography–mass spectrometry (HPLC-MS) in positive ion mode. The compounds were identified by searching the Metabolite Link (METLIN), the Spectral Database for Organic Compounds (SDBS), and by referring to literature reports. A total of 20 compounds were identified among the samples from the four origins, of which 8 compounds were common. The majority of the compounds belonged to the flavonoid and coumarin classes; Meranzin hydrate was identified for the first time in pomelo peel. In vitro antioxidant activity experiments showed that samples from Taizhou, Zhejiang, exhibited the highest antioxidant activity in the DPPH, ABTS, and FRAP assays, with values of 0.59 mg/mL, 97.06 μmol TE/g, and 60.62 μmol Fe²⁺/g, respectively. Samples from Zhangzhou, Fujian, showed antioxidant activity second only to the samples from Taizhou, Zhejiang. The sample from Zhangzhou, Fujian Province, showed excellent inhibitory activity in the α-glucosidase inhibition assay (IC₅₀ = 7.99 mg/mL). Full article

Key leaf functional traits, such as chlorophyll and carotenoids content (C_ab and C_xc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm⁻²; 1.3 µg·cm⁻²; 0.0009 cm; and 0.0009 g·cm⁻² for C_ab, C_xc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for C_ab, C_xc, and EWT (RMSE = 7.8 µg·cm⁻²; 2.0 µg·cm⁻²; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm⁻²) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm⁻² respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons. Full article

The present study was intended for the identification of secondary metabolites in acetone extract of the lichen Hypotrachyna cirrhata using UPLC-ESI-QToF-MS/MS and the detection of bioactive compounds. This study led to the identification of 22 metabolites based on their MS/MS spectra, accurate molecular masses, molecular formula from a comparison of the literature database (DNP), and fragmentation patterns. In addition, potent antioxidant and α-glucosidase inhibitory potentials of acetone extract of H. cirrhata motivated us to isolate 10 metabolites, which were characterized as salazinic acid (11), norlobaridone (12), atranorin (13), lecanoric acid (14), lichesterinic acid (15), protolichesterinic acid (16), methyl hematommate (17), iso-rhizonic acid (18), atranol (19), and methylatratate (20) based on their spectral data. All these isolates were assessed for their free radicals scavenging, radical-induced DNA damage, and intestinal α-glucosidase inhibitory activities. The results indicated that norlobaridone (12), lecanoric acid (14), methyl hematommate (17), and atranol (19) showed potent antioxidant activity, while depsidones (salazinic acid (11), norlobaridone (12)) and a monophenolic compound (iso-rhizonic acid, (18)) displayed significant intestinal α-glucosidase inhibitory activities (p < 0.001), which is comparable to standard acarbose. These results were further correlated with molecular docking studies, which indicated that the alkyl chain of norlobaridione (12) is hooked into the finger-like cavity of the allosteric pocket; moreover, it also established Van der Waals interactions with hydrophobic residues of the allosteric pocket. Thus, the potency of norlobaridone to inhibit α-glucosidase enzyme might be associated with its allosteric binding. Also, MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) binding free energies of salazinic acid (11) and norlobaridone (12) were superior to acarbose and may have contributed to their high activity compared to acarbose. Full article