Search Results (45)

In this study, six club raspberry varieties were examined for their aromatic profiles and sensory qualities, and statistical approaches were used to determine how aroma components affect consumer impressions. Analysis of the aroma’s chemical composition was performed utilizing headspace SPME and GC-MS. MS-DIAL -v5.5.250627 software was used to identify components from commercial libraries, after 10 repetitions for each variety, followed by manual verification. A sensory evaluation of fresh fruits, with 55 volunteers, was statistically analyzed and linked to chemical composition using multivariate analysis and the OPLS-HDA classification method, which was employed for the first time. Tula Magic was scored the highest in the sensory evaluation compared to Adelita, Himbo Top, Glen Dee, San Rafael, and Cascade Harvest. 2-Heptanol (fresh, lemongrass-like, herbal, floral, fruity, green), heptanal (fresh, aldehydic, fatty, green, herbal), and 2-methyl-6-hepten-1-ol (oily-green, herbaceous-citrusy) separated Tula Magic from the other varieties assessed. The same components were recognized in OPLS as positive contributors to the flavor score, while terpenoids like trans-β-ionone, α-ionone, and α,β-dihydro-β-ionone, as well as 2-heptanone, scored slightly lower. This suggests that a fine balance between the individual components is key to the overall aroma sensation. Full article

Background: Broccoli contains diverse phytochemicals, including glucosinolates and their hydrolysis products, with potential nutritional and bioactive properties. Accurate metabolite profiling requires optimized sample preparation and comprehensive databases. Methods: A rapid enzymatic deactivation method with 70% methanol, implemented prior to cryogrinding, was evaluated for processing freeze-dried and fresh broccoli florets, which were compared as plant materials. A widely targeted, organ-resolved metabolite database was constructed by integrating over 612 reported phytochemicals with glucosinolate degradation products. LC-HRMS combined with MS-DIAL and GNPS was employed for metabolite detection and annotation. Results: Freeze-dried samples yielded nearly twice the number of glucosinolates, isothiocyanates, and nitriles compared with standard-processed fresh tissue. Methanol pre-treatment preserved metabolite integrity in fresh samples, achieving comparable sensitivity to freeze-dried material. Using the integrated database, 998 metabolites were identified or tentatively characterized, including amino acids, carboxylic acids, phenolics, alkaloids, terpenoids, and glucosinolate derivatives. Cross-platform reproducibility was improved and false positives reduced. Conclusions: Optimized sample preparation combined with a curated metabolite database enables high-confidence, comprehensive profiling of broccoli florets phytochemicals. The resulting dataset provides a valuable reference for studies on genotype–environment interactions, nutritional quality, and functional bioactivity of cruciferous vegetables. Full article

The Pulsed Electric Field (PEF) technique represents a modern technology for treating and processing food and agricultural raw materials. The application of high-voltage electric pulses has been shown to modify macrostructure, improve extractability, and enhance the microbial safety of the treated matrix. In this study, we investigated metabolomic changes occurring during the individual technological steps of malting following PEF treatment. Methanolic extracts of technological intermediates of malting barley were analyzed using metabolomic fingerprinting performed with UHPLC-HRMS/MS. For data processing and interpretation, the freely available MS-DIAL—MS-CleanR—MS-Finder software platform was used. The metabolomes of the treated and untreated barley samples revealed significant changes. Tentatively identified PEF-related biomarkers included 1,2-diacylglycerol-3-phosphates, triacylglycerols, linoleic acids and their derivatives, octadecanoids, N-acylserotonins, and very long-chain fatty acids, and probably reflect abiotic stress response. Monitoring of the profiles of selected biomarkers in PEF malting batch indirectly revealed a potential enhancement of enzymatic activity after the PEF treatment. These results contribute to fundamental knowledge regarding the influence of PEF on final malt from a metabolomic perspective. Full article

Background/Objectives: Cocaine is a widely used illicit stimulant with significant toxicity. Despite its clinical relevance, the broader metabolic alterations associated with cocaine use remain incompletely characterized. This study aims to identify novel biomarkers for cocaine exposure by applying untargeted metabolomics to retrospective urine drug screening data. Methods: We conducted a retrospective analysis of a raw mass spectrometry (MS) dataset from urine comprehensive drug screening (UCDS) from 363 patients at the University of Pittsburgh Medical Center Clinical Toxicology Laboratory. The liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-qToF-MS) data were preprocessed with MS-DIAL and subjected to multiple statistical analyses to identify features significantly associated with cocaine-enzyme immunoassay (EIA) results. Significant features were further evaluated using MS-FINDER for feature annotation. Results: Among 14,883 features, 262 were significantly associated with cocaine-EIA results. A subset of 37 more significant features, including known cocaine metabolites and impurities, nicotine metabolites, norfentanyl, and a tryptophan-related metabolite (3-hydroxy-tryptophan), was annotated. Cluster analysis revealed co-varying features, including parent compounds, metabolites, and related ion species. Conclusions: Features associated with cocaine exposure, including previously underrecognized cocaine metabolites and impurities, co-exposure markers, and alterations in an endogenous metabolic pathway, were identified. Notably, norfentanyl was found to be significantly associated with cocaine -EIA, reflecting current trends in illicit drug use. This study highlights the potential of repurposing real-world clinical toxicology data for biomarker discovery, providing a valuable approach to identifying exposure biomarkers and expanding our understanding of drug-induced metabolic disturbances in clinical toxicology. Further validation and exploration using complementary analytical platforms are warranted. Full article

In substation working environments, analog dial gauges are widely used for equipment monitoring. Accurate reading of dial values is crucial for real-time understanding of equipment operational status and enhancing the intelligence of substation equipment operation and maintenance. However, existing dial reading recognition algorithms face significant errors in complex scenarios and struggle to adapt to dials with different measurement ranges. To address these issues, this paper proposes an automatic reading method for analog dial gauges consisting of two stages: dial segmentation and reading recognition. In the dial segmentation stage, an improved DeepLabv3+ network is used to achieve precise segmentation of the dial scale and pointer, and the network is made lightweight to meet real-time requirements. In the reading recognition stage, the distorted image is first corrected, and PGNet is used to obtain scale information for scale matching. Finally, an angle-based method is employed to achieve automatic reading recognition of the analog dial gauge. The experimental results show that the improved Deeplabv3+ network has 4.25 M parameters, with an average detection time of 19 ms per image, an average Pixel Accuracy of 92.7%, and an average Intersection over Union (IoU) of 79.7%. The reading recognition algorithm achieves a reading accuracy of 92.3% across dial images in various scenarios, effectively improving reading recognition accuracy and providing strong support for the development of intelligent operation and maintenance in substations. Full article

Capirona macrophylla is a Rubiaceae known as “mulateiro”. Ethnobotanical extracts have been used for skin treatment and in the management of leishmaniasis and malaria. Objectives: The metabolites in aqueous extracts from wood bark, leaves, and stems were identified, and their in silico docking and in vitro cellular efficacy against Leishmania amazonensis and Plasmodium falciparum were evaluated. Methods: The extracts were analyzed by UHPLC/HRMSⁿ using untargeted metabolomics approach with MSDial, MSFinder, and GNPS software for metabolite identification and spectra clustering. The most abundant metabolites underwent molecular docking using AutoDock via PyRx, targeting the dihydroorotate dehydrogenase from Leishmania and P. falciparum, and evaluated through molecular dynamics simulations using Gromacs. In vitro biological assays were conducted on 60 HPLC-fractions against these parasites. Results: Metabolomics analysis identified 5100 metabolites in ESI+ and 2839 in ESI− spectra among the “mulateiro” samples. GNPS clustering highlighted large clusters of quercetin and chlorogenic acid groups. The most abundant metabolites were isofraxidin, scopoletin, 5(S)-5-carboxystrictosidine, loliolide, quercetin, quinic acid, caffeoylquinic acid (and isomers), chlorogenic acid, neochlorogenic acid, tryptophan, N-acetyltryptophan, epicatechin, procyanidin, and kaempferol-3-O-robinoside-7-O-rhamnoside. Molecular docking pointed to 3,4-dicaffeoylquinic acid and kaempferol as promising inhibitors. The in vitro assays yielded four active HPLC-fractions against L. amazonensis with IC50 values ranging from 175.2 μg/mL to 194.8 μg/mL, and fraction G29 showed an IC50 of 119.8 μg/mL against P. falciparum. Conclusions: The ethnobotanical use of “mulateiro” wood bark tea as an antimalarial and antileishmanial agent was confirmed through in vitro assays. We speculate that these activities are attributed to linoleic acids and quinic acids. Full article

Fusarium fungi are widespread pathogens of food crops, primarily associated with the formation of mycotoxins. Therefore, effective mitigation strategies for these toxicogenic microorganisms are required. In this study, the potential of pulsed electric field (PEF) as an advanced technology of increasing use in the food processing industry was investigated to minimize the viability of Fusarium pathogens and to characterize the PEF-induced changes at the metabolomic level. Spores of four Fusarium species (Fusarium culmorum, Fusarium graminearum, Fusarium poae, and Fusarium sporotrichioides) were treated with PEF and cultured on potato dextrose agar (PDA) plates. The viability of the Fusarium species was assessed by counting the colony-forming units, and changes in the mycotoxin content and metabolomic fingerprints were evaluated by using UHPLC-HRMS/MS instrumental analysis. For metabolomic data processing and compound identification, the MS-DIAL (v. 4.80)–MS-CleanR–MS-Finder (v. 3.52) software platform was used. As we found out, both fungal viability and the ability to produce mycotoxins significantly decreased after the PEF treatment for all of the species tested. The metabolomes of the treated and untreated fungi showed statistically significant differences, and PEF-associated biomarkers from the classes oxidized fatty acid derivatives, cyclic hexapeptides, macrolides, pyranocoumarins, carbazoles, and guanidines were identified. Full article

Background: The rise of drug-resistant Leishmania strains presents a significant challenge in the treatment of Leishmaniasis, a neglected tropical disease. Extracellular vesicles (EVs) produced by these parasites have gained attention for their role in drug resistance and host–pathogen interactions. Methods: This study developed and applied a novel lipidomics workflow to explore the lipid profiles of EVs from three types of drug-resistant Leishmania infatum strains compared to a wild-type strain. EVs were isolated through ultracentrifugation, and their lipid content was extracted using a modified Matyash protocol. LC-MS analysis was performed, and data processing in MS-DIAL enabled lipid identification and quantification. Statistical analysis in MetaboAnalyst revealed strain-specific lipid alterations, highlighting potential links between lipid composition and drug resistance mechanisms. Results: Our results show distinct alterations in lipid composition associated with drug resistance. Specifically, drug-resistant strains exhibited reduced levels of phosphatidylcholine (PC) and phosphatidylglycerol (PG), particularly in the amphotericin B-resistant strain LiAmB1000.1. Sterol and glycerolipid species, including cholesteryl ester (CE) and triacylglycerol (TG) were also found to be diminished in LiAmB1000.1. These changes suggest significant lipid remodeling under drug pressure, potentially altering the biophysical properties of EV membranes and their capacity for molecule transfer. Furthermore, the lipidomic profiles of EVs from the other resistant strains, LiSb2000.1 and LiMF200.5, also displayed unique alterations, underscoring strain-specific adaptations to different drug resistance mechanisms. Conclusions: These significant alterations in lipid composition suggest potential lipid-based mechanisms underlying drug resistance in Leishmania, providing new avenues for therapeutic intervention. Full article

Human–machine interaction (HMI) is becoming increasingly important, especially in the automotive industry, where advancements in automated driving and driver assistance systems are key to enhancing driver safety and convenience. Among the many HMI interfaces, tactile sensing has been widely used in automotive applications as it enables instant and direct interactions with drivers. An area that remains underexplored among the tactile HMI interfaces is the application of haptic feedback to gear shifter modules. Therefore, this study investigates the design optimization of a dial gear shifter by analyzing the vibrations transmitted to the knob surface from an integrated haptic actuator. Specifically, we first tuned the mechanical properties of the haptic actuator (in terms of the resonance frequency and vibration level) in a simulation model by referring to experimental results. Next, a numerical model of a dial gear shifter was constructed, integrated with a haptic actuator, and tuned with the experimental results. The model was further optimized based on the design of the experiment and sensitivity analyses. The optimized design yielded a 24.5% improvement in the vibration level compared with the reference design, exceeding the minimum threshold (>~2.5 m/s² at 200 Hz) required for tactile sensing. The vibration enhancement (>22.x%) was also confirmed under the simulated hand-grabbing condition. This study is technically significant as it demonstrates that the haptic vibration in a dial gear shifter can be efficiently optimized through numerical analyses. This research will be used for the actual prototyping of a dial gear shifter to provide a safe driving experience for drivers. Full article

Identification of features with high levels of confidence in liquid chromatography–mass spectrometry (LC–MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC–MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS² spectra. This highlights the critical importance of validation across positive and negative LC–MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors. Full article

In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model’s speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models. Full article

Doliocarpus dentatus is thought to have a wide variety of therapeutic phytochemicals that allegedly improve libido and cure impotence. Although a few biomarkers have been identified with potential antinociceptive and cytotoxic properties, an untargeted mass spectrometry-based metabolomics approach has never been undertaken to identify therapeutic biofingerprints for conditions, such as erectile dysfunction, in men. This study executes a preliminary phytochemical screening of the woody vine of two ecotypes of D. dentatus with renowned differences in therapeutic potential for erectile dysfunction. Liquid chromatography–mass spectrometry-based metabolomics was used to screen for flavonoids, terpenoids, and other chemical classes found to contrast between red and white ecotypes. Among the metabolite chemodiversity found in the ecotype screens, using a combination of GNPS, MS-DIAL, and SIRIUS, approximately 847 compounds were annotated at levels 2 to 4, with the majority of compounds falling under lipid and lipid-like molecules, benzenoids and phenylpropanoids, and polyketides, indicative of the contributions of the flavonoid, shikimic acid, and terpenoid biosynthesis pathways. Despite the extensive annotation, we report on 138 tentative compound identifications of potentially therapeutic compounds, with 55 selected compounds at a level-2 annotation, and 22 statistically significant therapeutic biomarkers, the majority of which were polyphenols. Epicatechin methyl gallate, catechin gallate, and proanthocyanidin A2 had the greatest significant differences and were also relatively abundant among the red and white ecotypes. These putatively identified compounds reportedly act as antioxidants, neutralizing damaging free radicals, and lowering cell oxidative stress, thus aiding in potentially preventing cellular damage and promoting overall well-being, especially for treating erectile dysfunction (ED). Full article

Significant efforts have been spent in the modern era towards implementing environmentally friendly procedures like composting to mitigate the negative effects of intensive agricultural practices. In this context, a novel fertilizer was produced via the hydrolysis of an onion-derived compost, and has been previously comprehensively chemically characterized. In order to characterize its efficacy, the product was applied to tomato plants at five time points to monitor plant health and growth. Control samples were also used at each time point to eliminate confounding parameters due to the plant’s normal growth process. After harvesting, the plant leaves were extracted using aq. MeOH (70:30, v/v) and analyzed via UPLC-QToF-MS, using a C18 column in both ionization modes (±ESI). The data-independent (DIA/bbCID) acquisition mode was employed, and the data were analyzed by MS-DIAL. Statistical analysis, including multivariate and trend analysis for longitudinal monitoring, were employed to highlight the differentiated features among the controls and treated plants as well as the time-point sequence. Metabolites related to plant growth belonging to several chemical classes were identified, proving the efficacy of the fertilizer product. Furthermore, the efficiency of the analytical and statistical workflows utilized was demonstrated. Full article

Furan and 2-methylfuran (2-MF) are food contaminants that are classified as potentially carcinogenic to humans. The main source of exposure for adults via food is coffee consumption. Furan and 2-MF are volatile, which complicates exposure assessment because their content measured in food prior to consumption does not afford a reliable dosimetry. Therefore, other ways of exposure assessment need to be developed, preferably by monitoring exposure biomarkers, e.g., selected metabolites excreted in urine. In this study, cis-2-buten-1,4-dial (BDA)-derived urinary furan metabolites Lys-BDA (l-2-amino-6-(2,5-dihydro-2-oxo-1H-pyrrol-1-yl)hexanoic acid), AcLys-BDA (l-2-(acetylamino)-6-(2,5-dihydro-2-oxo-1H-pyrrol-1-yl)hexanoic acid) and GSH-BDA (N-[4-carboxy-4-(3-mercapto-1H-pyrrol-1-yl)-1-oxobutyl]-l-cysteinyl-glycine cyclic sulfide), as well as acetyl acrolein (AcA, 2-oxo-pent-2-enal)-derived metabolites Lys-AcA (l-2-(acetylamino)-6-(2,5-dihydro-5-methyl-2-oxo-1H-pyrrol-1-yl)-hexanoic acid) and AcLys-AcA (l-2-amino-6-(2,5-dihydro-5-methyl-2-oxo-1H-pyrrol-1-yl)-hexanoic acid) and their stable isotopically labeled analogs, were synthesized and characterized through NMR and MS, and a stable isotope dilution analysis (SIDA) with UPLC-ESI-MS/MS was established. As a proof of concept, urinary samples of a four-day human intervention study were used. In the frame of this study, ten subjects ingested 500 mL of coffee containing 0.648 µmol furan and 1.059 µmol 2-MF. Among the furan metabolites, AcLys-BDA was the most abundant, followed by Lys-BDA and GSH-BDA. Exposure to 2-MF via the coffee brew led to the formation of Lys-AcA and AcLys-AcA. Within 24 h, 89.1% of the ingested amount of furan and 15.4% of the ingested amount of 2-MF were detected in the urine in the form of the investigated metabolites. Therefore, GSH-BDA, Lys-BDA, AcLys-BDA, Lys-AcA and AcLys-AcA may be suitable as short-term-exposure biomarkers of furan and 2-MF exposure. Full article

We present a new fiber-amplifier-based differential absorption light detection and ranging (DIAL) system for range-resolved detection of carbon dioxide (CO₂) and water vapor (H₂O) over a range of a few kilometers. The fiber amplifier chain is seeded with a 7.14 kHz fast-switching high-spectral purity wavelength source near 1572 nm to cover ten different frequencies across the CO₂/H₂O line pair in ≈1.4 ms. We demonstrate the system in a study of CO₂ emissions from a local power plant in Boulder, CO, USA. We use real-time wind information to predict the plume location for tracking and modeling of the CO₂ emission rate to compare with the reported data from the power plant over a 13 h period. There is overall agreement with the reported burn rate, but we see periods of bias towards underestimation of the CO₂ emission rate. We attribute the dropout periods to uncertainties between the measured and the plant’s local wind speed data that impact both the tracking location and the plume model predictions. Upcoming studies that will make use of real-time Doppler wind data are expected to significantly decrease these uncertainties. Full article