Chemical Patterns Research Articles

The Cosmic Hunt for members in the outskirts of ultra-faint dwarf galaxies: Ursa Major I, Coma Berenices, and Boötes I

ABSTRACT Gaia EDR3 data were used to identify potential members in the outskirts of three ultra-faint dwarf (UFD) galaxies: Coma Berenices (>2Rh), Ursa Major I (∼4Rh), and Boötes I (∼4Rh), as well as a new member in the central region of Ursa Major I. These targets were observed with the Gemini GRACES spectrograph, which was used to determine precision radial velocities and metallicities that confirm their associations with the UFD galaxies. The spectra were also used to measure absorption lines for 10 elements (Na, Mg, K, Ca, Sc, Ti, Cr, Fe, Ni, and Ba), which confirm that the chemical abundances of the outermost stars are in good agreement with stars in the central regions. The abundance ratios and chemical patterns of the stars in Coma Berenices are consistent with contributions from SN Ia, which is unusual for its star formation history and in conflict with previous suggestions that this system evolved chemically from a single core collapse supernova event. The chemistries for all three galaxies are consistent with the outermost stars forming in the central regions, then moving to their current locations through tidal stripping and/or supernova feedback. In Boötes I, however, the lower metallicity and lack of strong carbon enrichment of its outermost stars could also be evidence of a dwarf galaxy merger.

Visible-Light Azidation and Chemical Patterning of Graphene via Photoredox Catalysis

Visible-Light Azidation and Chemical Patterning of Graphene via Photoredox Catalysis

Analysis of 12 Chemical Compounds And Pattern Recognition of Different Parts of Angelicae Sinensis Radix by Liquid Chromatography-Tandem Mass Spectrometry And Chemometrics Methods.

To evaluate the quality and quantify bioactive constituents in different parts of Angelicae Sinensis Radix, an efficient, high-speed, high-sensitivity high-performance liquid chromatography and triple quadrupole mass spectrometry method was used for simultaneous detection of 12 chemical compounds including L-tryptophan, chlorogenic acid, caffeic acid, ferulic acid, isoferulic acid, senkyunolide I, guanosine, proline, L-glutamine, γ-aminobutyric acid, glutamic acid, and arginine in 52 batches of Angelicae Sinensis Radix from Gansu, China. The established methods were validated by good linearity (R2≥0.9921), limits of detection (0.0001-0.0156 μg/mL), limits of quantitation (0.0006-0.0781 μg/mL), stability (RSD≤7.77%), repeatability (RSD≤6.79%), intra- and interday precisions (RSD≤6.00% and RSD≤6.39%, respectively) and recovery (90.90-107.16%). According to the quantitative results, the contents of the hydrophilic compounds were higher in the head, while the medium and weak polar components were mainly concentrated in the tail. Finally, principal component analysis results revealed that Angelicae Sinensis Radix could be divided into different medicinal sites based on polar components such as amino acids, nucleosides. The combination of liquid chromatography-tandem mass spectrometry and principal component analysis is a simple and reliable method for pattern recognition and quality evaluation of Angelicae Sinensis Radix.

Site-Selective Assembly of Centimeter-Scale Arrays of Precisely Oriented Magnetic Nanoellipsoids.

The precise organization and orientation of anisotropic nanoparticles (NPs) on substrates over a large area is key to the application of NP assemblies in functional optical, electronic, and magnetic devices, but achieving such high-precision NP assembly still remains challenging. Here, we demonstrate the site-selective assembly of magnetic nanoellipsoids into large-area precisely positioned, orientationally controlled arrays via a combination of chemical patterning and magnetic manipulation. Magnetic ellipsoidal NPs are selectively positioned on predetermined chemical patterns with high fidelity through electrostatic interactions and aligned uniformly in line with an applied magnetic field. The position, orientation, and interparticle spacing of the ellipsoids can be precisely tuned by controlling the chemical patterns and magnetic field. This approach is simple to implement and can generate centimeter-scale arrays in high yield (up to 99%). The arrays exhibit collective magnetic responses that are dependent on the orientation of the ellipsoids. This work offers a tool for the fabrication of precisely engineered arrays of anisotropic NPs for applications such as metasurface and artificial spin ice.

Are Am stars and hot-Jupiter planets related?

Context. Metallic-lined A stars (Am stars) are often components of short-period binary systems, where tidal interactions would result in low rotational velocities and help to develop the chemical peculiarities observed. However, the origin of single Am stars and Am stars that belong to wide binary systems is unclear. Aims. There is very recent evidence of an Am star hosting a likely synchronized hot brown dwarf (hot BD) and other possible Am stars hosting hot Jupiter planets. Following literature suggestions, we wonder if these hot low-mass companions could play a role in the development of an Am star, that is to say, if they could help to mitigate the “single Am” problem. Methods. We carried out a detailed abundance determination via spectral synthesis of 19 early-type stars hosting hot BDs and hot Jupiter planets, in order to determine the possible presence of Am stars in this sample. The abundances were determined iteratively for 25 different species by fitting synthetic spectra using the SYNTHE program together with local thermodynamic equilibrium (LTE) ATLAS12 model atmospheres. The abundances of C I, O I, and Mg I were corrected by non-LTE effects. The complete chemical patterns of the stars were then compared to those of Am stars and other chemically peculiar stars. Results. We studied a sample of 19 early-type stars, seven of them hosting hot BDs and 12 of them hosting hot Jupiter planets. We detected four Am stars in our sample (KELT-19A, KELT-17, HATS-70, and TOI-503) and two possible Am stars (TOI-681 and HAT-P-69). In particular, we detected the new Am star HATS-70, which hosts a hot BD, and we ruled out this class for the hot Jupiter host, WASP-189. Both stars show a different composition than previously reported. For the first time, we estimated the incidence of Am stars among stars hosting hot BDs (50−75%) and among stars hosting hot Jupiters (20−42%). The incidence of Am stars hosting hot BDs has shown to be higher than the frequency of Am stars in general. This would imply that the presence of hot BDs could play a role in the development of Am stars and possibly help to mitigate the single Am problem, which is different for the case of hot Jupiter planets. Notably, these results also indicate that the search for hot BDs may benefit from the targeting of single Am stars or Am stars in wide binary systems. We encourage the analysis of additional early-type stars hosting hot companions in order to improve the significance of the initial trends found here.

Quality evaluation of Fufang Jinqiancao granules based on ultra performance liquid chromatography-ultraviolet detection quantitative fingerprint combined with chemical pattern recognition

Fufang Jinqiancao granules have a large market demand due to the fact that they contain diuretics, inhibit urinary calculi formation, and exhibit both anti-inflammatory and antioxidant effects. In the current study, a fast and efficient quantitative ultra performance liquid chromatography-ultraviolet detection (UPLC-UV) fingerprinting method was established to analyze the Fufang Jinqiancao granules, while a chemical pattern recognition technology was used to evaluate the quality of the granules over different years. More specifically, the UPLC-UV system consisted of a Waters Acquity UPLC BEH C18 column (100 mm×2.1 mm, 1.7 μm), acetonitrile (mobile phase A), and a 0.1% formic acid aqueous solution (mobile phase B), wherein a gradient elution protocol was followed. Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS, Agilent Infinity Ⅱ 1290-6545) was used in combination with reference substances and literature comparisons to identify the common peaks present in the quantitative fingerprint. The fingerprints of 35 batches of Fufang Jinqiancao granules were established by means of the quantitative UPLC-UV fingerprinting method, and the fingerprint data obtained for these samples were further analyzed by chemical pattern recognition techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). The quality difference markers, namely mangiferin and isomangiferin, were screened, and their contents were determined. It was found that 12 common peaks existed in the fingerprint of the Fufang Jinqiancao granules, and the similarities of all 35 batches of samples were greater than 0.952. In addition, for the purpose of HCA, the 35 batches were divided into two categories, of which sample years 2018 and 2019 belonged to one category, and sample years 2020 and 2021 belonged to another category. Notably, PCA gave the same clustering trends as HCA. Based on the obtained results, the mangiferin and isomangiferin components responsible for the differences between the 2018, 2019 and 2020, 2021 samples were further screened by orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, the contents of the 35 batches of samples were determined using the two differential markers mangiferin and isomangiferin as indicators. The obtained results indicated that the chromatographic peaks of all 35 batches had acceptable resolutions, with mangiferin exhibiting a good linear relationship in the range of 5.3291-133.2276 mg/L, and isomangiferin exhibiting a similar linear relationship in the range of 4.1847-104.6170 mg/L. The average recovery of mangiferin was 101.7%-105.6%, with a relative standard deviation (RSD) of 0.63%-1.43%, while that of isomangiferin was 103.4%-105.5%, with an RSD of 0.60%-1.18%. Importantly, all RSD values were less than 1.43%, thereby indicating that our method meets the requirements of the Chinese Pharmacopoeia (2020 Edition). Among the 35 batches of samples, the contents of mangiferin and isomangiferin were higher in the 2020 and 2021 samples, and the content fluctuation range was smaller. Overall, the development of an accurate and reliable quality control method for Fufang Jinqiancao granules, and a reasonable and effective quality evaluation of Fufang Jinqiancao granule samples from different years was realized. We therefore expect that this study will provide a reference for establishing a more systematic and comprehensive quality control system.

Inverse Design of Pore Wall Chemistry To Control Solute Transport and Selectivity.

Next-generation membranes for purification and reuse of highly contaminated water require materials with precisely tuned functionality to address key challenges, including the removal of small, charge-neutral solutes. Bioinspired multifunctional membrane surfaces enhance transport properties, but the combinatorically large chemical space is difficult to navigate through trial and error. Here, we demonstrate a computational inverse design approach to efficiently identify promising materials and elucidate design rules. We develop a combined evolutionary optimization, machine learning, and molecular simulation workflow to spatially design chemical functional group patterning in a model nanopore that enhances transport of water relative to solutes. The genetic optimization discovers nonintuitive functionalization strategies that hinder the transport of solutes through the pore, simply by patterning hydrophobic methyl and hydrophilic hydroxyl functional groups. Examining these patterns, we demonstrate that they exploit an unexpected diffusive solute hopping mechanism. This inverse design procedure and the identification of novel molecular mechanisms for pore chemical heterogeneity to impact solute selectivity demonstrate new routes to the design of membrane materials with novel functionalities. More broadly, this work illustrates how chemical design is a powerful strategy to modulate water-mediated surface-solute interactions in complex, soft material systems that are relevant to diverse technologies.

The Gaia-ESO Survey: Chemical tagging in the thin disk

Context. The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars that formed within the same stellar association. This idea – known as chemical tagging – has not produced the expected results, especially within the thin disk where open stellar clusters have chemical patterns that are difficult to disentangle. Aims. The ultimate goal of this study is to probe the feasibility of chemical tagging within the thin disk population using high-quality data from a controlled sample of stars. We also aim at improving the existing techniques of chemical tagging and giving some kind of guidance on different strategies of clustering analysis in the elemental abundance space. Methods. Here we develop the first blind search of open clusters’ members through clustering analysis in the elemental abundance space using the OPTICS algorithm applied to data from the Gaia-ESO survey. First, we evaluate different strategies of analysis (e.g., choice of the algorithm, data preprocessing techniques, metric, space of data clustering), determining which ones are more performing. Second, we apply these methods to a data set including both field stars and open clusters attempting a blind recover of as many open clusters as possible. Results. We show how specific strategies of data analysis can improve the final results. Specifically, we demonstrate that open clusters can be more efficaciously recovered with the Manhattan metric and on a space whose dimensions are carefully selected. Using these (and other) prescriptions we are able to recover open clusters hidden in our data set and find new members of these stellar associations (i.e., escapers, binaries). Conclusions. Our results indicate that there are chances of recovering open clusters’ members via clustering analysis in the elemental abundance space, albeit in a data set that has a very high fraction of cluster members compared to an average field star sample. Presumably, the performances of chemical tagging will further increase with higher quality data and more sophisticated clustering algorithms, which will likely became available in the near future.

Three-dimensional chiral morphodynamics of chemomechanical active shells

Morphogenesis of active shells such as cells is a fundamental chemomechanical process that often exhibits three-dimensional (3D) large deformations and chemical pattern dynamics simultaneously. Here, we establish a chemomechanical active shell theory accounting for mechanical feedback and biochemical regulation to investigate the symmetry-breaking and 3D chiral morphodynamics emerging in the cell cortex. The active bending and stretching of the elastic shells are regulated by biochemical signals like actomyosin and RhoA, which, in turn, exert mechanical feedback on the biochemical events via deformation-dependent diffusion and inhibition. We show that active deformations can trigger chemomechanical bifurcations, yielding pulse spiral waves and global oscillations, which, with increasing mechanical feedback, give way to traveling or standing waves subsequently. Mechanical feedback is also found to contribute to stabilizing the polarity of emerging patterns, thus ensuring robust morphogenesis. Our results reproduce and unravel the experimentally observed solitary and multiple spiral patterns, which initiate asymmetric cleavage in Xenopus and starfish embryogenesis. This study underscores the crucial roles of mechanical feedback in cell development and also suggests a chemomechanical framework allowing for 3D large deformation and chemical signaling to explore complex morphogenesis in living shell-like structures.

Chemical profiling and rapid discrimination of Blumea riparia and Blumea megacephala by UPLC-Q-Exactive-MS/MS and HPLC

ObjectiveTo rapidly identify the two morphologies and chemical properties of similar herbal medicines, Blumea riparia and B. megacephala as the basis for chemical constituent analysis. MethodsUPLC-Q-Exactive-MS/MS was utilized for profiling and identification of the constituents in B. riparia and B. megacephala. Chemical pattern recognition (CPR) was further used to compare and distinguish the two herbs and to identify their potential characteristic markers. Then, an HPLC method was established for quality evaluation. ResultsA total of 93 constituents are identified, including 54 phenolic acids, 35 flavonoids, two saccharides, one phenolic acid glycoside, and one other constituent, of which 67 were identified in B. riparia and B. megacephala for the first time. CPR indicates that B. riparia and B. megacephala samples can be distinguished from each other based on the LC–MS data. The isochlorogenic acid A to cryptochlorogenic acid peak area ratio calculated from the HPLC chromatograms was proposed as a differentiation index for distinguishing and quality control of B. riparia and B. megacephala. ConclusionThis study demonstrates significant differences between B. riparia and B. megacephala in terms of chemical composition. The results provide a rapid and simple strategy for the comparison and evaluation of the quality of B. riparia and B. megacephala.

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005–2012

BackgroundThe effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated. ObjectivesTo discover obesity-related mixed chemical exposure patterns in the general U.S. population. MethodsSparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR). ResultsEight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity. ConclusionOur study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

Learning the relationship between nanoscale chemical patterning and hydrophobicity

The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities.

The Pristine survey – XIX. Cu and Zn abundances in metal-poor giants

ABSTRACTMetal-poor stars formed from a gas enriched by the ejecta of the explosion of one/few generations of first massive stars. With the Pristine photometry combined with the Gaia data, we selected a sample of bright giants metal-poor candidates to be observed at high resolution. Of the 43 stars observed, 36 were confirmed to be metal-poor, supporting the high success-rate of Pristine in selecting metal-poor stars. We centred the investigation on Cu and Zn, which are elements usually neglected, also because they are the ‘killing elements’ for identifying the Pair Instability Supernovae (PISN) descendants, the explosion of very massive first stars. We derived detailed chemical abundances of 22 species of 18 elements for 36 giant stars. The study of the kinematics of the stars in the sample revealed the presence of metal-poor stars on disc-like orbits (17 per cent of the sample) and even on thin disc orbits (8 per cent of the sample). Four stars are members of the Gaia–Sausage–Enceladus, one star is also likely a member of it. Three stars in the sample (TYC 1118−595−1, TYC 2207−992−1, and TYC 1194−507−1) show a chemical pattern compatible with the one theoretically derived for the PISN descendants, i.e. stars formed out from a gaseous environment enriched by PISN (>50 per cent level) and subsequent generations of normal stars evolving as core-collapse SNe.

The significance of trophic transfer of microplastics in the accumulation of plastic additives in fish: An experimental study using brominated flame retardants and UV stabilizers

Marine organisms ingest microplastics directly from water and indirectly from food sources. Ingesting microplastics can lead to the accumulation of plastic-derived chemicals. However, the relative contributions of the two exposure routes to the accumulation of plastic-derived chemicals in organisms are unknown. Using microplastics containing two brominated flame retardants (BFRs; BDE209 and DBDPE) and three UV stabilizers (UVSs; UV-234, UV-327, and BP-12), we performed exposure experiments to compare chemical accumulation patterns in fish (Myoxocephalus brandti) between exposure from water and prey (Neomysis spp.). We found significantly higher concentrations of BFRs in fish fed microplastic-contaminated prey than fish exposed to microplastics in the water. However, we observed similar concentrations of UVSs in fish exposed to both sources. As BFRs are more hydrophobic than UVSs, the differences may reflect the hydrophobic nature of the additives. Our findings indicate that both exposure routes are crucial to understanding the accumulation of plastic additives in fish.

Understanding the hydrochemical functioning of glacierized catchments of the Upper Indus Basin in Ladakh, Indian Himalayas.

Recent studies have endorsed that surface water chemical composition in the Himalayas is impacted by climate change-induced accelerated melting of glaciers. Chemical weathering dynamics in the Ladakh region is poorly understood, due to unavailability of in situ dataset. The aim of the present study is to investigate how the two distinct catchments (Lato and Stok) drive the meltwater chemistry of the Indus River and its tributary, in the Western Himalayas. Water samples were collected from two glaciated catchments (Lato and Stok), Chabe Nama (tributary) and the Indus River in Ladakh. The mildly alkaline pH (range 7.3-8.5) and fluctuating ionic trend of the meltwater samples reflected the distinct geology and weathering patterns of the Upper Indus Basin (UIB). Gibbs plot and mixing diagram revealed rock weathering outweighed evaporation and precipitation. The strong associations between Ca2+-HCO3-, Mg2+-HCO3-, Ca2+-Mg2+, Na+-HCO3-, and Mg2+-Na+ demonstrated carbonate rock weathering contributed to the major ion influx. Principal component analysis (PCA) marked carbonate and silicates as the most abundant minerals respectively. Chemical weathering patterns were predominantly controlled by percentage of glacierized area and basin runoff. Thus, Lato with the larger glacierized area (~ 25%) and higher runoff contributed low TDS, HCO3-, Ca2+, and Na+ and exhibited higher chemical weathering, whereas lower chemical weathering was evinced at Stok with the smaller glacierized area (~ 5%). In contrast, the carbonate weathering rate (CWR) of larger glacierized catchments (Lato) exhibits higher average value of 15.7 t/km2/year as compared to smaller glacierized catchment (Stok) with lower average value 6.69 t/km2/year. However, CWR is high in both the catchments compared to silicate weathering rate (SWR). For the first time, in situ datasets for stream water chemical characteristics have been generated for Lato and Stok glaciated catchments in Ladakh, to facilitate healthy ecosystems and livelihoods in the UIB.

Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins

NMR spectroscopy is key in the study of intrinsically disordered proteins (IDPs). Yet, even the first step in such an analysis-the assignment of observed resonances to particular nuclei-is often problematic due to low peak dispersion in the spectra of IDPs. We show that the assignment process can be aided by finding "hidden" chemical shift patterns specific to the amino acid residue types. We find such patterns in the training data from the Biological Magnetic Resonance Bank using linear discriminant analysis, and then use them to classify spin systems in an α-synuclein sample prepared by us. We describe two situations in which the procedure can greatly facilitate the analysis of NMR spectra. The first involves the mapping of spin systems chains onto the protein sequence, which is part of the assignment procedure-a prerequisite for any NMR-based protein analysis. In the second, the method supports assignment transfer between similar samples. We conducted experiments to demonstrate these cases, and both times the majority of spin systems could be unambiguously assigned to the correct residue types.

Directed self-assembly of poly(styrene-b-vinyl acetate) block copolymers on chemical patterns for sub-10 nm nanopatterning via thermal annealing

Directed self-assembly (DSA) of block copolymers (BCPs) is an effective approach for the fabrication of sub-10 nm features for semiconductor devices. Although many high-χ BCPs have been developed for DSA, the majority of them has a significant difference in surface energies (γs) between two blocks. Here we report the synthesis and self-assembly of poly (styrene- b -vinyl acetate) (PS- b -PVAc). PS- b -PVAc has a higher Flory-Huggins interaction parameter (χ) than poly (styrene- b -methyl methacrylate) (PS- b -PMMA) and the smallest achievable period ( L O ) is 18.2 nm. PS and PVAc have nearly equal γs and a perpendicular orientation of domains is obtained on random copolymer brushes under thermal annealing. DSA of PS- b -PVAc (SV-1 and SV-2) is successfully demonstrated in the thin films on chemical patterns, and the feature size down to 9.5 nm is obtained in DSA of SV-4 with 2 × density multiplication. These results demonstrate that PS- b -PVAc could be a candidate BCP for next-generation DSA lithography. • PS- b -PVAc has a higher χ than PS- b -PMMA. • Perpendicularly orientated domains of PS- b -PVAc are obtained under thermal annealing. • Feature size down to 9.5 nm is obtained in DSA with 2x density multiplication.

Quality evaluation of the classical prescription, Danggui Jianzhong decoction, using ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology.

Danggui Jianzhong decoction is a classical prescription that has been widely used for thousands of years. However, the quality of this formula is difficult to control owing to its complex chemical component system. In this study, a simple and efficient method comprising ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology was established to evaluate the quality of this decoction. A total of 20 common peaks were obtained by fingerprint analysis and 19 chemicals were identified. The fingerprint similarity of 15 batch samples ranged from 0.963 to 0.991. Chemical pattern recognition analysis could clearly classify 15 batches of Danggui Jianzhong decoction into three groups. Further, seven chemical markers were screened out. A herbs-active components-targets-disease network was constructed and enrichment analyses were performed, which indicated that these 19 chemical components are the medicinal substances of Danggui Jianzhong decoction. Further, the mechanism employed by this formula to treat primary dysmenorrhea may be related to the regulation of inflammatory response. In conclusion, this combination approach enables accurate evaluation and prediction of the quality of Danggui Jianzhong decoction, and lays the foundation for studies on the material basis and exploration of the mechanism of Danggui Jianzhong decoction in the treatment of primary dysmenorrhea.

Front Cover: Quality evaluation of the classical prescription, Danggui Jianzhong decoction, using ultra‐high‐performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology

Front Cover: Quality evaluation of the classical prescription, Danggui Jianzhong decoction, using ultra‐high‐performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology

The chemical abundance pattern of the extremely metal-poor thin disc star 2MASS J1808−5104 and its origins

ABSTRACT We present a high-resolution (R ∼ 35 000), high signal-to-noise (S/N = 350) Magellan/MIKE spectrum of the bright extremely metal-poor star 2MASS J1808−5104. We find [Fe/H] = −4.01 (spectroscopic LTE stellar parameters), [Fe/H] = −3.8 (photometric stellar parameters), and [Fe/H] = −3.7 (spectroscopic NLTE stellar parameters). We measured a carbon-to-iron ratio of [C/Fe] = 0.38 from the CH G-band. J1808−5104 is thus not carbon-enhanced, contrary to many other stars with similarly low-iron abundances. We also determine, for the first time, a barium abundance ([Ba/Fe] = −0.78), and obtain a significantly reduced upper limit for the nitrogen abundance ([N/Fe] < −0.2). For its [Ba/Fe] abundance, J1808−5104 has a lower [Sr/Ba] ratio compared to other stars, consistent with behaviour of stars in ultra-faint dwarf galaxies. We also fit the abundance pattern of J1808−5104 with nucleosynthesis yields from a grid of Population III supernova models. There is a good fit to the abundance pattern that suggests J1808−5104 originated from gas enriched by a single massive supernova with a high explosion energy of E = 10 × 1051 erg and a progenitor stellar mass of M = 29.5 M⊙. Interestingly, J1808−5104 is a member of the Galactic thin disc, as confirmed by our detailed kinematic analysis and calculated stellar actions and velocities. Finally, we also established the orbital history of J1808−5104 using our time-dependent Galactic potential the ORIENT. J1808−5104 appears to have a stable quasi-circular orbit and been largely confined to the thin disc. This unique orbital history, the star’s very old age (∼13.5 Gyr), and the low [C/Fe] and [Sr/Ba] ratios suggest that J1808−5104 may have formed at the earliest epoch of the hierarchical assembly of the Milky Way, and it is most likely associated with the primordial thin disc.