High Q2 Research Articles

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity.

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Double scattering in deuteron electrodisintegration

We demonstrate that at sufficiently high energies when the eikonal regime is established for hadronic interactions, the double scattering subprocess can be clearly identified and isolated in quasi-elastic deuteron electro-disintegration processes. Comparing theoretical calculations with the recent high precision experimental data we present a “proof of principle” that these processes can be used to study advanced issues related to hadron formation in QCD. In this case, the double scattering represents as a fermi-scale “detector” which probes products of high Q2 scattering from the bound nucleon through their rescattering from the spectator nucleon in the deuteron.

Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies

Fungal infections are a growing public health problem worldwide. Despite the availability of several medicines, their efficacy is still constrained by fungal resistance. This research conducted the 2D/3D-QSAR analysis on twenty-nine triazole molecules previously evaluated for their antifungal activity. The HQSAR/B-H, CoMFA and CoMSIA models were built using twenty-three molecules in the training set. They show high Q2 values (0.646, 0.564 and 0.561, respectively) and important R2 values (0.764, 0.805 and 0.787, respectively). The predictive capacity of the established models was validated by external validation; they performed well. The contour maps derived from the HQSAR/B-H, CoMFA and CoMSIA models provide more detail to identify favorable and unfavorable groupings impacting the activity. Then, 4 proposed new triazole molecules with significant antifungal activity were suggested. In addition, the molecular docking results showed good binding energies and interactions of the proposed inhibitors in the active site of the receptor studied. The molecular dynamics and MM/PBSA methods confirmed and validated the molecular docking results. The new triazole molecules were evaluated for their oral bioavailability and toxicity using ADME/Tox properties. Finally, the retrosynthesis method created a synthetic pathway for the candidate inhibitor Z1.

Integrated computational approaches for designing potent pyrimidine-based CDK9 inhibitors: 3D-QSAR, docking, and molecular dynamics simulations

CDK9 is an emerging target for the development of anticancer drugs. The development of CDK9 inhibitors with significant potency had consistently posed a formidable challenge. In the current research, a number of computational methodologies, such as, 3D-QSAR, molecular docking, fingerprint analysis, molecular dynamic (MD) simulations followed by MMGB/PBSA and ADMET studies were used systemically to uncover the binding mechanism of pyrimidine derivatives against CDK9. The CoMFA and CoMSIA models having high q2 (0.53, 0.54) and r2 values (0.96, 0.93) respectively indicating that model could accurately predict the bioactivities of CDK9 inhibitors. Using the R-group exploration technique implemented by the Spark™ by Cresset group, the structural requirements revealed by the contour maps of model were utilized strategically to create an in-house library of 100 new CDK9 inhibitors. Additionally, the compounds from the in-house library were mapped into 3D-QSAR model which predicted pIC50 values comparable to the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to elucidate the essentials of CDK9 inhibitor design. MD simulations (100 ns) were performed on the selected docked complexes A21, A14 and D98 which contributed in validating the binding interactions. According to the findings of binding free energy analysis (MMGB/PBSA), It was observed that residues CYS106 and GLU107 had a considerable tendency to facilitate ligand-protein interactions via H-bond interactions. The aforementioned findings have the potential to enhance researchers comprehension of the mechanism underlying CDK9 inhibition and may be utilized in the development of innovative and efficacious CDK9 inhibitors.

Insights on the current semi-leptonic B-decay discrepancies — and how Bs→ μ+μ−γ can help

Bs → μ+μ−γ, measured at high q2 as a partially reconstructed decay, can probe the origin of the existing discrepancies in semi-leptonic b → s and b → c decays. We perform a complete study of this possibility. We start by reassessing the alleged discrepancies, with a focus on a unified EFT description. Using the SMEFT, we find that the tauonic Wilson coefficient required by R(D(*)) implies a universal muonic Wilson coefficient of precisely the size required by semi-muonic BR data and, separately, by semi-muonic angular analyses. We thus identify reference scenarios. Importantly, Bs → μ+μ−γ offers a strategy to access them without being affected by the long-distance issues that hamper the prediction of semi-leptonic B decays at low q2. After quantifying to the best of our knowledge the Bs → μ+μ−γ experimental over the long haul, we infer the Bs → μ+μ−γ sensitivity to the couplings relevant to the anomalies. In the example of the real-δC9,10 scenario, we find significances below 3σ. Such figure is to be compared with other single-observable sensitivities that one can expect from e.g. BR and angular data, whether at low or high q2, and not affected by long-distance issues such as narrow resonances or intermediate charmed di-meson rescattering.

Unbinned deep learning jet substructure measurement in high Q2ep collisions at HERA

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the kT jet clustering algorithm. Results are reported at high transverse momentum transfer Q2>150GeV2, and inelasticity 0.2<y<0.7. The analysis is also performed in sub-regions of Q2, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models.

From Ds → γ in lattice QCD to Bs → μμγ at high q2

We use a recent lattice determination of the vector and axial Ds → γ form factors at high squared momentum transfer q2 to infer their Bs → γ counterparts. To this end, we introduce a phenomenological approach summarized as follows. First, we describe the lattice data with different fit templates motivated by vector-meson dominance, that is expected to hold in the high-q2 region considered. We identify reference fit ansaetze with one or two physical poles, that we validate against alternative templates. Then, the pole residues can be unambiguously related to the appropriate couplings involving the pseudoscalar, the vector mesons concerned, and the photon — or tri-couplings — and the latter can be expressed as sums over quark magnetic moments, weighed by their e.m. charges. This description obeys a well-defined heavy-quark scaling, that allows to parametrically scale up the form factors to the Bs → γ case. We discuss a number of cross-checks of the whole approach, whose validation rests ultimately in a first-principle determination, e.g. in lattice QCD. Finally, we use our obtained form factors to reassess the SM prediction of ℬ(Bs → μ+μ−γ) in the range sqrt{q^2}in [4.2, 5.0] GeV, where an experimental measurement is awaited.

High accuracy prediction of dipeptide angiotensin-converting enzyme (ACE) inhibitory activity by improving the credibility of the 3D-quantitative structure-activity relationship (3D-QSAR) model database and investigating inhibition mechanism

The 3D-QSAR model is one of the most effective techniques for predicting the peptide bioactivity based on their structure properties. The accuracy of model prediction is highly dependent on the bio-activity of the peptides in its dataset. However, the same peptide used for constructing the database was reported to have obviously different activities in different published articles, which may significantly decrease the prediction accuracy. Based on this, we chose ACE inhibitory (ACE-I) dipeptides that can be absorbed directly into the human small intestine as research object. Ten dipeptides were randomly selected from the literatures for molecular docking with ACE and synthesized to test their activity. Ultimately, a linear equation was developed between bio-activities and docking scores, which was used to revalidate the reliable ACE-I activities of all reported dipeptides for constructing a credible dataset of 3D-QSAR model. The prediction accuracy was significantly enhanced and the model we established had a high Q2 = 0.840 and R2 = 0.998. Furthermore, dipeptide CW was predicted and tested in vitro to achieve the highest ACE-I activity (IC50 = 0.16 μM), and the inhibition mechanism was also investigated. This study introduced a new method for predicting bio-activity with high precision using the 3D-QSAR model.

3D-QSAR, docking and molecular dynamics simulations of novel Pyrazolo-pyridazinone derivatives as covalent inhibitors of FGFR1: a scientific approach for possible anticancer agents

Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model’s contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the SparkTM software. The compounds from the in-house library were also mapped in the 3D-QSAR model that predicts comparable pIC50 values with the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to reveal the fundamentals to design potent FGFR1 covalent inhibitors. The estimated binding free energies (MMGB/PBSA) for the selected compounds were in agreement with the experimental value ranking of their binding affinities towards FGFR1. Furthermore, per-residue energy decomposition analysis has identified Arg627 and Glu531 to contribute significantly in improved binding affinity of compound W16. During ADME analysis, the majority of in-house library compounds exhibited pharmacokinetic properties superior to those of experimentally produced compounds. These new compounds may help researchers better understand FGFR1 inhibition and lead to the creation of novel, potent FGFR1 inhibitors. Communicated by Ramaswamy H. Sarma

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma.

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma.

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q2 (0.63) and R 2 (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.

A multidimensional study of the structure function ratio [formula omitted] from hard exclusive π+ electro-production off protons in the GPD regime

A multidimensional extraction of the structure function ratio σLT′/σ0 from the hard exclusive e→p→e′nπ+ reaction above the resonance region has been performed. The study was done based on beam-spin asymmetry measurements using a 10.6 GeV incident electron beam on a liquid-hydrogen target and the CLAS12 spectrometer at Jefferson Lab. The measurements focus on the very forward regime (t/Q2 ≪ 1) with a wide kinematic range of xB in the valence regime (0.17 <xB < 0.55), and virtualities Q2 ranging from 1.5 GeV2 up to 6 GeV2. The results and their comparison to theoretical models based on Generalized Parton Distributions demonstrate the sensitivity to chiral-odd GPDs and the directly related tensor charge of the nucleon. In addition, the data is compared to an extension of a Regge formalism at high photon virtualities. It was found that the Regge model provides a better description at low Q2, while the GPD model is more appropriate at high Q2.

An analysis of polarized parton distribution functions with nonlinear QCD evolution equations

We present the polarized parton distribution functions from a QCD analysis of the worldwide polarized deep inelastic scattering data, based on the dynamical parton distribution model. All the sea quarks and gluons are dynamically generated from QCD radiations, with the nonperturbative input comprising only the polarized valence quark distributions. This approach leads to a simple parametrization, which has only seven free parameters. In the analysis, we apply the DGLAP equations with parton-parton recombination corrections. For the first step, the DGLAP equations, parton-parton recombination corrections, and experimental observable g1 are all evaluated at the leading order. The parameterized nonperturbative input at an extremely low Q02 reproduces well the spin-dependent structure functions measured at high Q2. Comparisons with experimental observations and some other polarized parton distribution functions are also shown. Our results are in good agreement with the experimental data and consistent with some other parameterized models. Furthermore, our analysis presents the positive polarized gluon distribution and it suggests that the gluon distribution plays an important role to the proton spin content. The polarized antiquark distributions are non-zero at high Q2 but quite small compared to polarized valence quark distributions, based on this dynamical parton model analysis. This analysis gives smaller statistical uncertainties for the polarized sea quark and gluon distributions, thanks to the fewer free parameters used for the parametrization of the initial polarized PDFs.

Relationship between dietary nutrients and hypertension, overweight and obesity in pupil grades 3 to 6 in Wuhan City

To explore the relationship between children&apos;s dietary nutrient intakes and hypertension, overweight and obesity. In April 2016, 1033 children aged 9 to 12 from grades 3 to 6 in two primary schools in Hongshan District of Wuhan City were selected by cluster sampling method, with an average age of(10.55±1.10) years, including 545 boys(52.8%) and 488 girls(47.2%), for physical examination and dietary survey. Physical examination included height, weight, blood pressure, etc. Dietary survey was conducted using a 3-day 24 h retrospective method. Principal component factor analysis was used to explore the dietary nutrient patterns of children, and logistic regression was used to analyze the effects of different dietary nutrient patterns on children&apos;s hypertension, overweight and obesity. In terms of M(P25, P75), compared with normal blood pressure group, hypertensive group had higher energy intake(1761.97(1617.17, 1940.43)kcal vs.1730.95(1556.06, 1905.71)kcal, P=0.011). Compared with normal weight group, overweight and obese group had higher carbohydrate(250.65(226.55, 281.29)g vs.244.41(220.04, 273.01)g, P=0.011), vitamin B_1(0.29(0.22, 0.36)mg vs.0.27(0.22, 0.34)mg, P=0.022), vitamin B_2(0.45(0.33, 0.60)mg vs.0.39(0.32, 0.51)mg, P=0.001), iron(12.64(11.06, 14.48)mg vs.12.39(10.57, 14.21)mg, P=0.033), zinc(5.11(4.00, 6.77)mg vs.4.60(3.87, 5.84)mg, P&lt;0.001)intakes, lower calcium intake(185.52(136.92, 264.73)mg vs.207.39(141.25, 300.92)mg, P=0.007). Low cholesterol, low vitamin, high calcium pattern(OR=0.644, 95%CI 0.421-0.985) and Q2 of low protein, low fat and low cholesterol(OR=0.626, 95%CI 0.412-0.951) were protective factors for overweight and obesity. High carbohydrate may be a risk factor for overweight and obesity, while high calcium, low fat and low cholesterol may be protective factors for overweight and obesity.

Onset of Color Transparency in Holographic Light-Front QCD

The color transparency (CT) of a hadron, propagating with reduced absorption in a nucleus, is a fundamental property of QCD (quantum chromodynamics) reflecting its internal structure and effective size when it is produced at high transverse momentum, Q. CT has been confirmed in many experiments, such as semi-exclusive hard electroproduction, eA→e′πX, for mesons produced at Q2&gt;3GeV2. However, a recent JLab (Jefferson Laboratory) measurement for a proton electroproduced in carbon eC→e′pX, where X stands for the inclusive sum of all produced final states, fails to observe CT at Q2 up to 14.2 GeV2. In this paper, the onset of CT is determined by comparing the Q2-dependence of the hadronic cross sections for the initial formation of a small color-singlet configuration using the generalized parton distributions from holographic light-front QCD. A critical dependence on the hadron’s twist, τ, the number of hadron constituents, is found for the onset of CT, with no significant effects from the nuclear medium. This effect can explain the absence of proton CT in the present kinematic range of the JLab experiment. The proton is predicted to have a “two-stage” color transparency with the onset of CT differing for the spin-conserving (twist-3, τ=3) Dirac form factor with a higher onset in Q2 for the spin-flip Pauli (twist-4) form factor. In contrast, the neutron is predicted to have a “one-stage” color transparency with the onset at higher Q2 because of the dominance of its Pauli form factor. The model also predicts a strong dependence at low energies on the flavor of the quark current coupling to the hadron.

On the effective lifetime of Bs\u2192 \u03bc\u03bc\u03b3

We consider the {B}_s^0 → μ+μ−γ effective lifetime, and the related CP-phase sensitive quantity {A}_{{Delta Gamma}_s}^{mu mu gamma} , as a way to obtain qualitatively new insights on the current B-decay discrepancies. Through a fit comparing pre- to post-Moriond-2021 data we identify a few theory benchmark scenarios addressing these discrepancies, and featuring large CP violation in addition. We then explore the possibility of telling apart these scenarios with {A}_{{Delta Gamma}_s}^{mu mu gamma} , once resonance-modeling and form-factor uncertainties are taken into account. We do so in both regions of low and high invariant di-lepton mass-squared q2. For low q2, we show how to shape the integration range in order to reduce the impact of the ϕ-resonance modelling on the {A}_{{Delta Gamma}_s}^{mu mu gamma} prediction. For high q2, we find that the corresponding pollution from broad-charmonium resonances has a surprisingly small effect on {A}_{{Delta Gamma}_s}^{mu mu gamma} . This is due to a number of cancellations, that can be traced back to the complete dominance of semi-leptonic operator contributions for high q2 — at variance with low q2 — and to {A}_{{Delta Gamma}_s}^{mu mu gamma} behaving like a ratio-of-amplitudes observable. Our study suggests that {A}_{{Delta Gamma}_s}^{mu mu gamma} is — especially at high q2 — a potentially valuable probe of short-distance CP-violating effects in the very same Wilson coefficients that are associated to current b → s discrepancies. Its discriminating power, however, relies on progress in form-factor uncertainties. Interestingly, high q2 is the region where {B}_s^0 → μ+μ−γ is already being accessed experimentally, and the region where form factors are more accessible through non-perturbative QCD methods.

Discovery of Structural Prospects of Imidazo[1,5-a]pyrazine Derivatives as BTK Inhibitors Against Cancer: A Computational Study

Background: Bruton’s tyrosine kinase (BTK) plays an important role in cell development and proliferation. BTK inhibitors are encouraging novel agents against B-cell malignancies and autoimmune diseases. Although BTK inhibitors have been approved by the FDA to lower off-target effects and reduce emerging resistances, it is necessary to develop novel BTK inhibitors with better outcomes and minimum side effects. Objective: The present study includes pharmacophore hypothesis, 3D QSAR, virtual screening, docking, ADME analysis, and screening of potential imidazo[1,5-a]pyrazine derivatives as BTK inhibitors. Methods: Generation of pharmacophore hypothesis, virtual screening, 3D QSAR, molecular docking, and ADME analysis were conducted. Results: The pharmacophore study generated 20 pharmacophore hypotheses as BTK inhibitors. The five-point hypothesis DPRRR_1 was selected, consisting of one hydrogen bond donor, one positive ionic, and three-ring aromatic features. 3D QSAR study of the compounds provided the best model with high Q2 (0.8683), R2 (0.983), and R2CV (0.5338) values. The developed pharmacophore model was further taken for screening of ZINC database ligands for evaluation of docking interaction and physiochemical properties. Potent compounds of the series 15, 27, 8n, and 38 showed good docking scores -8.567, -7.465, -6.922, -6.137, respectively. Conclusion: All the pharmacokinetic parameters analyzed, including human oral absorption of active compounds of the series, were found to be within the permissible range. The present geometry and features included in the pharmacophore hypothesis can be used for the development of novel BTK inhibitors as anticancer agents.

In Situ Quality Assessment of Dried Sea Cucumber (Stichopus japonicus) Oxidation Characteristics during Storage by iKnife Rapid Evaporative Ionization Mass Spectrometry.

Sea cucumber (Stichopus japonicus) is one of the most luxurious and nutritious seafoods in Asia. It is always processed into dried products to prevent autolysis, but its quality is easily destructed during storage. Herein, an extremely simplified workflow was established for real-time and in situ quality assessment of dried sea cucumbers (DSCs) during storage based on the lipid oxidation characteristics using an intelligent surgical knife (iKnife) coupled with rapid evaporative ionization mass spectrometry (REIMS). The lipidomic phenotypes of DSCs at different storage times were acquired successfully, which were then processed by multivariate statistical analysis. The results showed that the discrepancy in the characteristic ions in different DSCs was significant (p < 0.05) with high R2(Y) and Q2 values (0.975 and 0.986, respectively). The receiver operating characteristic curve revealed that the ions of m/z 739.5, m/z 831.5, m/z 847.6, and m/z 859.6 were the most specific and characteristic candidate biomarkers for quality assessment of DSCs during accelerated storage. Finally, this method was validated to be qualified in precision (RSDintraday ≤ 9.65% and RSDinterday ≤ 9.36%). In conclusion, the results showed that the well-established iKnife-REIMS method was high-throughput, rapid, and reliable in the real-time quality assessment of DSCs.

Study on the fluidity of ash slag of liquefaction solid product and lignite co‐gasification

AbstractThe utilization of liquefaction solid product (LSP) obtained from fluidization reaction pyrolysis has become a pressing obstacle limiting the industrialization of coal liquefaction technology. In this work, gasification of LSP and Hami lignite coal (LC) and their blend at different ratios was investigated. In addition, the melt flow, mineral transformation, and crystallization behavior in the fusion process and cooling process were investigated. The results show that both LSP and LC are typical crystalline slags and cannot be applied to entrained flow gasification alone. The co‐gasification of LC and LSP is a feasible method to improve their fluidity. At the ratio of 2:1 (LC to LSP), the blend has the lowest AFT. For molten slag, viscosity of the sample corresponded well to the structure of the slag. LC1:2LSP with higher Q2 and Q3 content showed a high viscosity, while LC1:2LSP with higher Q0 and Q1 content showed a lower viscosity. Besides, the size of the crystals generated during cooling is inversely proportional to the high‐temperature viscosity of the sample, the migration of ions became difficult, and the growth of crystal size was inhibited at high viscosity. Meanwhile, the slag type of samples was influenced by the precipitated crystals. Tcv can be effectively reduced by blending LC with LSP in a certain proportion. The results provide a theoretical basis for the utilization of LSP for the operation of entrained flow gasification.

Lipid classification of fish oil omega-3 supplements by 1H NMR and multivariate analysis

The worldwide advent of concentrated supplements containing omega-3 fatty acids (FA) in the form of triacylglycerols (TAG) or ethyl esters (EE) has increased the interest in developing methods to classify these products. The quality control based on their lipid composition has become necessary since EE bioavailability has been proved to be lower when compared to the TAG. In this preliminary study, eight models based on 1H NMR and supervised discriminant analysis (PLS-DA/OPLS-DA) were applied to classify omega-3 fish oil in TAG or EE forms. The 4.0–4.5 ppm region was selected for modeling since it bracketed spectral features to discriminate TAG and EE. The non-supervised principal component analysis was employed to visually evaluate the distribution of samples and revealed a clear separation of TAG from EE marine oils along PC1. In addition, representative TAG and EE samples were 100 % correctly classified using any of the eight supervised models studied. The developed models resulted in high R2Y (≥ 0.977) and Q2 (≥ 0.953), and low root mean square error for prediction (≤ 0.009), which demonstrates the high potential of this rapid and straightforward procedure to evaluate the lipid form of supplements and mislabeling.