Mass Spectra Research Articles

Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals.

There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.

Innovative pyrazole–phosphoramidate derivative for effective mercury adsorption and their application for Egyptian industrial waste samples

ABSTRACT Mercury, known for its severe health and environmental impacts even in trace amounts, necessitates effective removal from water systems. This research focuses on preparation of a novel adsorbent of 3,9−bis((4−amino−5−hydroxy−1 H−pyrazol−3−yl)amino)−2,4,8,10−tetraoxa−3,9−diphosphaspiro[5.5] undecane 3, 9−dioxide (PADP) for Hg(II) ions adsorption. To validate functionalization steps and elucidate PADP’s physical and chemical properties, a comprehensive array of assays, including FTIR, surface area, BET, TGA, 13CNMR, Mass spectra, 31PNMR and1HNMR were employed. The study systematically examined several factors controlling mercury sorption, including temperature, adsorbent dosage, pH, interaction time, and mercury concentration using the batch technique. Optimal sorption effectiveness was achieved at pH 5, with an interaction time of 50 min, an 80 mg adsorbent dose, at ambient temperature. The newly devised adsorbent exhibited an impressive adsorption capacity (qe) of 357 mg of Hg(II) per Litre, with empirical data fitting the Langmuir and Dubinin−Radushkevich adsorption isotherm models. The kinetics of mercury sorption followed second-order and Elovich kinetics models. Thermodynamic aspects, including ΔGº, ΔSº, and ΔHº, were also investigated, providing appreciated perceptions into the energetic dynamics of the sorption process. Thermodynamics has shown that the sorption of mercury ions is both endothermic and spontaneous. Additionally, mercury adsorption in the occurrence of other ions was explored, highlighting the selectivity of the modified Pyrazole – Phosphoramidate adsorbent for mercury removal. PADP demonstrates superior performance in removing Hg(II) from wastewater effluents, outshining a range of both natural and synthetic adsorbents previously used for this purpose. This superiority is evident when comparing its adsorption capacity (qo) against those of other adsorbents, showcasing its exceptional efficacy in purifying water from mercury contamination.

Molecular Changes during Germination of Cocoa Beans, Part 1.

Some germination is known to occur during the process of fermentation in cocoa beans. The impact of this biological process on the course of cocoa fermentation is not known and was thus investigated. In order to determine the impact of germination at the molecular level as well as on flavor, an untargeted metabolomics approach using Ultra Performance Liquid Chromatography-Electrospray Ionization-Time of Flight-Mass Spectrometry (UPLC-ESI-ToF-MS) with simultaneous acquisition of low- and high-collision energy mass spectra (MSe) was performed. Extracts of raw and germinated cocoa beans of the same origin were measured and compared for characteristic differences by unsupervised principal component analysis. OPLS-DA revealed 12-hydroxyjasmonic acid (HOJA) sulfate, (+)-catechin and (-)-epicatechin as most down-regulated compounds as well as two hydroxymethylglutaryl (HMG) glucosides A and B among others as decisive up-regulated compounds in the germinated material. Additionally, further HMG glucosides and 12-hydroxyjasmonic acid could be identified in cocoa for the first time by coelution with isolated and synthesized reference compounds. HOJA sulfate, which has been postulated in cocoa, and HOJA were revealed to impart bitter and astringent taste qualities.

Bottomonia in quark-antiquark confining potential

This paper comprehensively explores bottomonia mass spectra and their decay properties by solving the non-relativistic Schrodinger wave equation numerically with an approximate quark-antiquark potential form. We also incorporate spin-dependent terms—spin-spin, spin-orbit, and tensor terms to remove mass degeneracy and to obtain excited states (nS, nP, nD, nF, n = 1, 2, 3, 4, 5) mass spectra. Using the Van Royen—Weisskopf formula, we investigate leptonic decay constants, di-leptonic, di-gamma, tri-gamma, di-gluon decay widths and incorporate first-order radiative corrections. We also computed radiative transition widths, which provide better insight into the non-perturbative aspects of QCD. The states ϒ(10750), ϒ(10860), ϒ(11020) are analyzed as pure states 33 D 1, 53 S 1 and 43 D 1, respectively. The present mass spectroscopy and decay properties results align with available experimental values and other theoretical predictions. Our results may provide better insight into upcoming experimental information in the near future.

Enhanced N-Glycan Profiling of Therapeutic Monoclonal Antibodies through the Application of Upper-Hinge Middle-Up Level LC-HRMS Analysis

Therapeutic monoclonal antibodies (mAbs) are crucial in modern medicine due to their effectiveness in treating various diseases. However, the structural complexity of mAbs, particularly their glycosylation patterns, presents challenges for quality control and biosimilarity assessment. This study explores the use of upper-hinge middle-up (UHMU)-level ultra-high-performance liquid chromatography–high-resolution mass spectrometry (LC-HRMS) analysis to improve N-glycan profiling of mAbs. Two specific enzymes, known as IgG degradation enzymes (IGDEs), were used to selectively cleave therapeutic mAbs above the hinge region to separate antibody subunits for further Fc glycan analysis by means of the UHMU/LC-HRMS workflow. The complexity of the mass spectra of IGDEs-digested mAbs was significantly reduced compared to the intact MS level, enabling reliable assignment and relative quantitation of paired Fc glycoforms. The results of the UHMU/LC-HRMS analysis of nine approved therapeutics highlight the significance of this approach for in-depth glycoform profiling.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization.

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

A comparative study for the efficiency of Pd (II) and Fe (III) complexes as efficient catalysts for synthesis of dihydro‐7H‐5‐thia‐hexaaza‐s‐indacen‐6‐one derivatives supported with DFT approach

Two novel complexes were synthesized by the reaction of benzothiazol‐pyrimidin‐2‐ylidene ligand (BTP) with Pd (II) and Fe (III) ions. A variety of various spectral and analytical methods (infrared, 1H/NMR, 13C/NMR, electronic spectra, CHN analyses, mass spectra, thermogravimetric analysis, and magnetic susceptibility) were used to characterize the investigated BTP ligand and its complexes. Correlation of experimental results with density functional theory calculation proves that the geometry of BTP‐Fe complex is octahedral, whereas BTP‐Pd complex is square planner. The catalytic effectiveness of BTP complexes were tested for three‐component condensation process under moderate and environmentally friendly reaction conditions. Moreover, the effects of different Lewis acid, basic, and ionic liquid catalysts, as well as solvent and catalyst dose on the catalytic reaction were investigated. Both catalysts demonstrated strong catalytic capability in the carefully regulated ideal reaction circumstances. Heterogeneous catalyst BTP‐Pd exhibited superior catalytic performance compared to homogeneous catalyst BTP‐Fe. All products were obtained in high TOF (turnover frequency) numbers in the presence of these catalysts, which indicate the high efficiency of these catalysts in the synthesis of dihydro‐7H‐5‐thia‐hexaaza‐s‐indacen‐6‐one derivatives. Moreover, the two catalysts' recycling and reusability in reactions were also investigated. Heterogeneous BTP‐Pd catalyst could be reused up to seven times with high efficiency, but the homogeneous catalyst (BTP‐Fe) could only be recycled up to four times. Furthermore, the mechanism of catalytic reaction was suggested and supported by DFT calculation. The simplicity, safety, stability, use of commercially available catalysts, quick reaction times, and excellent yields make it promising for future industrial use.

Machine Learning Assisted MALDI Mass Spectrometry for Rapid Antimicrobial Resistance Prediction in Clinicals.

Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant Enterococcus fecium (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible E. faecium (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.

Feasibility of organic micropollutant removal from municipal wastewaters by algal treatment vs. activated sludge treatment: Comparison based on non-targeted organic compound analysis

Sustainability and life-cycle concerns about the conventional activated sludge (CAS) process for wastewater treatment have been driving the development of energy-efficient, greener alternatives. Feasibility of an algal-based wastewater treatment (A-WWT) system has been demonstrated recently as a possible alternative, capable of simultaneous nutrient and energy recovery. This study compared capabilities of the A-WWT and CAS systems in removing organic micropollutants (OMP). Initial assessments based on surrogate organic measures and fluorescence excitation-emission matrix (FEEM) scans revealed that the A-WWT system achieved higher removals of organics than the CAS system. However, effluents of both systems contained residual organic matter, necessitating further OMP assessment for a rigorous comparison. A novel ultrahigh-performance liquid chromatography- Fourier transform mass spectrometry (UPLC-FTMS)-based non-targeted screening approach was adopted here for residual OMP analysis. This approach confirmed that the A-WWT system resulted in better OMP removal, eliminating 329 compounds and partially reducing 472 compounds, compared to 206 eliminations and 410 partial reductions by the CAS system. Mass spectra signal corresponding to some OMPs increased with treatment while some transformation products were observed following treatment. Higher OMP reduction in the A-WWT system with concurrent reductions of biodegradable carbon, nutrients, and pathogens in a single-step while producing energy and nutrient rich algal biomass underscore its potential as a greener alternative for wastewater treatment.

Impact of Higher Harmonics of Gravitational Radiation on the Population Inference of Binary Black Holes

Templates modeling just the dominant mode of gravitational radiation are generally sufficient for the unbiased parameter inference of near-equal-mass compact binary mergers. However, neglecting the subdominant modes can bias the inference if the binary is significantly asymmetric, very massive, or has misaligned spins. In this work, we explore if neglecting these subdominant modes in the parameter estimation of nonspinning binary black hole mergers can bias the inference of their population-level properties such as mass and merger redshift distributions. Assuming the design sensitivity of the advanced LIGO-Virgo detector network, we find that neglecting subdominant modes will not cause a significant bias in the population inference, although including them will provide more precise estimates. This is primarily because asymmetric binaries are expected to be rarer in our detected sample, due to their intrinsic rareness and the observational selection effects. The increased precision in the measurement of the maximum black hole mass can help in better constraining the upper mass gap in the mass spectrum.

Isolation and whole genomic analysis of mesophilic bacterium Pseudoglutamicibacter cumminsii in epithelial mesothelioma

The relationship between bacteria and tumors has been the hot spot of clinical research in recent years. Pseudoglutamicibacter cumminsii is an aerobic Gram-positive bacterium commonly found in soil. Recent studies have identified P. cumminsii in patients with cutaneous and urinary tract infections. However, little is known on its pathogenesis as well as involvement in other clinical symptoms. In this study, we first report the isolation of P. cumminsii in blood of an epithelial mesothelioma patient. The clinical and laboratory characteristics of P. cumminsii were first described and evaluated. The pure colony of P. cumminsii was then identified using automated microorganism identification system and mass spectrum. The whole genome of the newly identified strain was sequenced with third generation sequencing (TGS). The assembled genome was further annotated and analyzed. Whole genomic and comparative genomic analysis revealed that the isolated P. cumminsii strain in this study had a genome size of 2,179,930 bp and had considerable unique genes compared with strains reported in previous findings. Further phylogenetic analysis showed that this strain had divergent phylogenetic relationship with other P. cumminsii strains. Based on these results, the newly found P. cumminsii strain was named P. cumminsii XJ001 (PC1). Virulence analysis identified a total of 71 pathogenic genes with potential roles in adherence, immune modulation, nutrition/metabolism, and regulation in PC1. Functional analysis demonstrated that the annotated genes in PC1 were mainly clustered into amino acid metabolism (168 genes), carbohydrate metabolism (107 genes), cofactor and vitamin metabolisms (98 genes), and energy metabolism (68 genes). Specifically, six genes including yodJ, idh, katA, pyk, sodA, and glsA were identified within cancer pathways, and their corresponding homologous genes have been documented with precise roles in human cancer. Collectively, the above results first identified P. cumminsii in the blood of tumor patients and further provide whole genomic landscape of the newly identified PC1 strain, shedding light on future studies of bacteria in tumorigenesis.

Differential expression analysis of cutaneous squamous cell carcinoma and basal cell carcinoma proteomic profiles sampled with e-biopsy

Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) affects treatment plans. We report a tissue sampling approach with molecular biopsy using electroporation. This method, coined e-biopsy, enables non-destructive non-thermal permeabilization of cells in the skin for vacuum-assisted extraction of biomolecules. We used e-biopsy for ex vivo proteome extraction from 3 locations per patient in 21 cSCC, 20 BCC and 7 actinic keratosis human skin samples. Using liquid chromatography mass spectrometry (LC/MS/MS), we identified 5,966 proteins observed with non-zero intensity in at least one samples. The intra-patient Pearson correlation of 0.888 ± 0.065 for BCC patients; 0.858 ± 0.077 for SCC patients; 0.876 ± 0.116 for solar actinic keratosis (AK) patients indicates high consistency of the e-biopsy sampling. The mass spectra presented significantly different proteome profiles for cSCC, BCC and AK, with several hundreds of proteins differentially expressed. Notably, our study showed that proteomes sampled with e-biopsy from cSCC and BCC lesions are different, and that proteins of CRNN, SULT1E1 and ITPK1 genes are significantly overexpressed in BCC in comparison to cSCC. Our results provide evidence that the e-biopsy approach could potentially be used as a tool to support cutaneous lesions classification with molecular pathology.

Quantifying assumptions underlying peak oxygen consumption equations across the body mass spectrum.

The goal of this study is toquantify the assumptions associated with the Wasserman-Hansen (WH) and Fitness Registry and the Importance of Exercise: A National Database (FRIEND) predictive peak oxygen consumption (pVO2) equations across body mass index (BMI). Assumptions in pVO2 for both equations were first determined using a simulation and then evaluated using exercise data from the Stanford Exercise Testing registry. We calculated percent-predicted VO2 (ppVO2) values for both equations and compared them using the Bland-Altman method. Assumptions associated with pVO2 across BMI categories were quantified by comparing the slopes of age-adjusted VO2 ratios (pVO2/pre-exercise VO2) and ppVO2 values for different BMI categories. The simulation revealed lower predicted fitness among adults with obesity using the FRIEND equation compared to the WH equations. In the clinical cohort, we evaluated 2471 patients (56.9% male, 22% with BMI >30 kg/m2, pVO2 26.8 mlO2/kg/min). The Bland-Altman plot revealed an average relative difference of -1.7% (95% CI: -2.1 to -1.2%) between WH and FRIEND ppVO2 values with greater differences among those with obesity. Analysis of the VO2 ratio to ppVO2 slopes across the BMI spectrum confirmed the assumption of lower fitness in those with obesity, and this trend was more pronounced using the FRIEND equation. Peak VO2 estimations between the WH and FRIEND equations differed significantly among individuals with obesity. The FRIEND equation resulted in a greater attributable reduction in pVO2 associated with obesity relative to the WH equations. The outlined relationships between BMI and predicted VO2 may better inform the clinical interpretation of ppVO2 values during cardiopulmonary exercise test evaluations.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 μm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 μm—most harmful as they can reach the alveoli—whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 μm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential reveal significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition captured by leaves. Moreover, we identified how specific leaf wax compositions—beyond the known sticky nature of hydrophobic waxes—contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles. Environmental ImplicationsPM2.5, known for its adverse effects including severe respiratory and cardiovascular diseases in humans, and its hazards to degrade ecosystem health, poses significant environmental threats. This study reveals how tree leaves interact with these fine particulates, focusing on the distinct capabilities of various tree species based on their leaf characteristics. By demonstrating that specific leaf waxy and physiological attributes enhance PM2.5 constituent capture, the research supports bio-inspired approaches to air quality management. Strategic tree planting and potential genetic modifications of trees emerge as viable strategies for reducing atmospheric PM2.5, offering sustainable solutions to mitigate particulate pollution.

Sequential window acquisition of all theoretical mass spectra (SWATH-MS) as an emerging proteomics approach for the discovery of dark-cutting beef biomarkers

Recent advances in “omics” technologies have enabled the identification of new beef quality biomarkers and have also allowed for the early detection of quality defects such as dark-cutting beef, also known as DFD (dark, firm, and dry) beef. However, most of the studies conducted were carried out on a small number of animals and mostly applied gel-based proteomics. The present study proposes for the first time a Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) proteomics approach to characterize and comprehensively quantify the post-mortem muscle proteome of DFD (pH24 ≥ 6.2) and CONTROL (5.4 ≤ pH24 ≤ 5.6) beef samples within the largest database of DFD/CONTROL beef samples to date (26 pairs of the Longissimus thoracis muscle samples of young bulls from Asturiana de los Valles breed, n = 52). The pairwise comparison yielded 35 proteins that significantly differed in their abundances between the DFD and CONTROL samples. Chemometrics methods using both PLS-DA and OPLS-DA revealed 31 and 36 proteins with VIP > 2.0, respectively. The combination of different statistical methods these being Volcano plot, PLS-DA and OPLS-DA allowed us to propose 16 proteins as good candidate biomarkers of DFD beef. These proteins are associated with interconnected biochemical pathways related to energy metabolism (DHRS7B and CYB5R3), binding and signaling (RABGGTA, MIA3, BPIFA2B, CAP2, APOBEC2, UBE2V1, KIR2DL1), muscle contraction, structure and associated proteins (DMD, PFN2), proteolysis, hydrolases, and activity regulation (AGT, C4A, GLB1, CAND2), and calcium homeostasis (ANXA6). These results evidenced the potential of SWATH-MS and chemometrics to accurately identify novel biomarkers for meat quality defects, providing a deeper understanding of the molecular mechanisms underlying dark-cutting beef condition.

Optimization of headspace high-capacity tool coupled to two-dimensional gas chromatography–mass spectrometry for mapping the volatile organic compounds of raw pistachios. A proof-of-concept on the classification ability by geographic origin

An optimized procedure for extracting and analyzing raw pistachio volatiles was developed through headspace sampling with high-capacity tools and subsequent analysis using comprehensive two-dimensional gas chromatography coupled with mass spectrometry. The examination of 18 pistachio samples belonging to different geographic areas led to the identification of a set of 99 volatile organic compounds (VOCs). Molecules were putatively identified using linear retention index, mass spectra similarity, and two-dimensional plot location. The impact of preprocessing and processing techniques on the aligned data matrix from a set of samples of different geographical origins, after removing contaminants, was evaluated. The combination of scaling with log-transformation, normalization with z-score, and data reduction with random forest machine learning algorithm generated a panel of 16 discriminatory VOC molecules. As a proof of concept, raw pistachios' VOC profile was employed for the first time to tentatively classify them based on their geographical origin.

Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets.

The ability to quantify individual components of complex mixtures is a challenge found throughout the life and physical sciences. An improved capacity to generate large data sets along with the uptake of machine-learning (ML)-based analysis tools has allowed for various "omics" disciplines to realize exceptional advances. Other areas of chemistry that deal with complex mixtures often do not leverage these advances. Environmental samples, for example, can be more difficult to access, and the resulting small data sets are less appropriate for unconstrained ML approaches. Herein, we present an approach to address this latter issue. Using a very small environmental data set─35 high-resolution mass spectra gathered from various solvent extractions of Canadian petroleum fractions─we show that the application of specific domain knowledge can lead to ML models with notable performance.

Flavor mixing and solution structures in Dyson–Schwinger equations for a two-flavor system

We solved the Dyson–Schwinger (DS) equations for a two-flavor system with symmetry to study its flavor mixing effects. Initially, we employed the point interaction model and bare vertex approximation to reveal the structure of the solutions. Using the point interaction model, the DS equations can be solved analytically, and we found that these solutions can be classified into three groups, each forming an ellipse. These solutions exhibit SO(2) symmetry, while the original SU(2) symmetry at the Lagrangian level is dynamically broken to SO(2), corresponding to the emergence of flavor mixing effects. However, this flavor mixing effect does not manifest in the final physical state. By utilizing the system’s SO(2) symmetry, we can diagonalize the propagators of the DS equations, eliminating the flavor mixing effect but causing the originally degenerate masses at the Lagrangian level to split. These mass eigenstates have identical quantum numbers but different masses. If we can correspond these to quark particles of different generations, we can explain why the three generations of quarks have different masses and obtain the corresponding quark mass spectrum. Finally, we provide the corresponding numerical results using a more realistic interaction model.

Transition magnetic moment of Majorana neutrinos in the triplets next-to-minimal MSSM

The next-to-minimal supersymmetric Standard Model with triplets is an attractive extension of the Standard Model. It combines the advantages of the next-to-minimal supersymmetric Standard Model and the minimal supersymmetric Standard Model with triplets to give three tiny Majorana neutrinos masses via a type-I+II seesaw mechanism. With the on-shell renormalization scheme, we consider the neutrino masses up to one loop approximation. Applying the effective Lagrangian method, we study the transition magnetic moments of Majorana neutrinos and consider the normal hierarchy and inverse hierarchy neutrino mass spectra within the constraints of experimental data on neutrino oscillations. The solar neutrino transition magnetic moment is further deduced, and compared with the XENONnT experiment limit. Published by the American Physical Society 2024

Non-Target Screening of Chemicals in Selected Cotton Products by GC/MS and Their Safety Assessment

Cotton is used for the production of textiles, hygiene and cosmetic materials. During cultivation and technological processes, various types of substances (surfactants, softeners, lubricants, etc.) penetrate cotton, which can have a harmful effect on both the human body and the environment. The aim of this study was to analyze selected cotton products in order to identify the substances contained and to describe the potential possibilities of inducing textile contact dermatitis (CD). The impact of the identified compounds on the aquatic environment was also taken into account. Nine samples of cotton clothing and seven samples of cotton pads from various manufacturers were tested. Samples after extraction using the FUSLE (Focused Ultrasonic Liquid Extraction) technique were analyzed with GC/MS. Qualitative analysis was based on comparing mass spectra with library spectra using the following mass spectra deconvolution programs: MassHunter (Agilent), AMDIS (NIST), and PARADISE (University of Copenhagen). The parameter confirming the identification of the substance was the retention index. Through the non-target screening process, a total of 36 substances were identified, with an average AMDIS match factor of approximately 900 (“excellent match”). Analyzing the properties of the identified compounds, it can be concluded that most of them have potential properties that can cause CD, also due to the relatively high content in samples. This applies primarily to long-chain alkanes (C25–C31), saturated fatty acids, fatty alcohols (e.g., oleyl alcohol), and fatty acid amides (e.g., oleamide). However, there are not many reports describing cases of cotton CD. Information on the identified groups of compounds may be helpful in the case of unexplained sources of sensitization when the skin comes into contact with cotton materials. Some of the identified compounds are also classified as dangerous for aquatic organisms, especially if they can be released during laundering.