Chemical Patterns Research Articles

Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites.

Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.

Periodical amphiphilic surface with chemical patterning for micelles immobilization and analysis

We present a strategy to produce periodically patterned surface with the controlled hydrophilic and hydrophobic nanoareas. The developed approach comprises colloid lithography using vapor-annealed polystyrene (PS) microspheres mask and two-step electrochemical surface grafting. The grafting was performed using the diazonium surface chemistry before and after colloid mask removal in order to introduce hydrophilic Ph-COOH and hydrophobic Ph-C8F17 moieties. Grafted chemical moities were covalently attached to the surface. The procedure results in creation of a surface with nanoprecisely located periodical hydrophobic and hydrophilic areas, with different sizes and geometry, determined by vapor annealing of PS microspheres. The created amphiphilic surface shows good biorepelency towards bovine serum albumin (BSA) and a high affinity towards amphiphilic micelles. Variation of surface nanoarchitecture elucidates the optimal surface chemistry for immobilization and surface entrapping of drug-loaded micelles. The formation of additional interface between amphilic surface and micelles was proved by dramatic decrease of the contact angle. According to Fourier-transform infrared spectroscopy (FTIR) results, created amphiphilic surfaces can capture drug-loaded micelles even in the presence of BSA biomatrix. Described procedure represents a versatile and scalable method for the creation of stable amphiphilic coating and its potential application in qualitative analysis and identification of micelles.

Characterization and Biological Activity of Fiber-Type Cannabis sativa L. Aerial Parts at Different Growth Stages.

Currently, there is a renewed interest in cannabis-related products in different fields because of the rich phytocomplex of this plant, together with its fiber and agricultural features. In this context, the current study aims to chemically characterize different samples of fiber-type Cannabis sativa L. grown in Italy as a potential health promoting source. An ultrasound-assisted solid-liquid extraction (UA-SLE) method was first developed and optimized to obtain a fingerprinting of the investigated phytocomplex. Analyses were carried out through an ultra high performance liquid chromatography equipped with a photodiode array detector in series with triple quadrupole system with an electrospray ionization (ESI) interface (UHPLC-UV-ESI-MS/MS) and showed that the phytocomplex mainly includes flavonoids and non-psychotomimetic cannabinoids. The method was then applied to characterize and compare 24 samples of fiber-type Cannabis sativa L. aerial parts (mainly stems and leaves), which differed for the growth stages (from mid-vegetative to early flowering), growth land plots, and methods of drying (forced-draft oven or freeze-drying). The quali-quantitative analysis showed that a freeze-drying method seems to better preserve the chemical composition of the samples, while the location of the land plot and the growth stage of the plant (which did not comprise inflorescences) had minor influences on the chemical pattern. These results were also supported by spectrophotometric in-vitro assays (scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and 2,2′-azinobis-3-ethyl-benzthiazoline-6-sulphonate (ABTS+•) radicals and inhibitory activity against tyrosinase and elastase enzymes) to investigate the potential biological activity of these samples and the contribution of non-psychotomimetic cannabinoids.

Effective boundary conditions for dynamic contact angle hysteresis on chemically inhomogeneous surfaces

Recent experiments (Guan et al., Phys. Rev. Lett., vol. 116, issue 6, 2016a, p. 066102; Guan et al., Phys. Rev. E, vol. 94, issue 4, 2016b, p. 042802) showed many interesting phenomena on dynamic contact angle hysteresis while there is still a lack of complete theoretical interpretation. In this work, we study the time averaging of the apparent advancing and receding contact angles on surfaces with periodic chemical patterns. We first derive a new Cox-type boundary condition for the apparent dynamic contact angle on homogeneous surfaces using the Onsager variational principle. Based on this condition, we propose a reduced model for some typical moving contact line problems on chemically inhomogeneous surfaces in two dimensions. Multiscale expansion and averaging techniques are employed to approximate the model for asymptotically small chemical patterns. We obtain a quantitative formula for the averaged dynamic contact angles. It describes explicitly how the advancing and receding contact angles depend on the velocity and the chemical inhomogeneity of the substrate. The formula is a coarse-graining version of the Cox-type boundary condition on inhomogeneous surfaces. Numerical simulations are presented to validate the analytical results. The numerical results also show that the formula characterizes well the complicated behaviour of dynamic contact angle hysteresis observed in the experiments.

Dynamics and timescales of mafic\u2013silicic magma interactions at Soufri\xe8re Hills Volcano, Montserrat

Mafic magma intrusions into silica-rich magmatic reservoirs are commonly proposed to drive the eruptions of andesitic hybrid magmas that characterise many arc volcanoes. However, interactions between contrasting magmas involve large gradients of physical and chemical properties that change over time, and the details of such processes have proven difficult to constrain. In this paper we investigate the dynamics of magma mingling and mixing using chemical and textural zoning patterns recorded in plagioclase crystals from the February 2010 eruption at Soufrière Hills Volcano, Montserrat. This eruption is considered a classic example of interactions between the andesite magma that constitutes the bulk of the erupted volume and the basaltic andesite that occurs as enclaves. We find that plagioclase crystals are characterised by two well-defined zones that record mafic–silicic magma interaction: a crystal interior, often identified by a patchy, dusty and oscillatory zoning, and an overgrowth zone (rim) of a different composition. We use the anorthite and Mg contents to track the thermal and compositional changes experienced by the crystals over time. Our results reveal that the crystal rims formed a few hours to days prior to eruption, during co-eruptive magmatic interactions. The interaction between the two magmas with contrasting rheology is likely increased by a narrowing conduit geometry towards the surface, which facilitates convection and additional interface contact of the two magmas. Our findings shed new light on the nature and timing of magmatic interactions driving the final eruptive phase at Soufrière Hills Volcano and help to propose an interpretative framework of the monitoring signals.

An adjuvant strategy enabled by modulation of the physical properties of microbial ligands expands antigen immunogenicity

Activation of the innate immune system via pattern recognition receptors (PRRs) is key to generate lasting adaptive immunity. PRRs detect unique chemical patterns associated with invading microorganisms, but whether and how the physical properties of PRR ligands influence the development of the immune response remains unknown. Through the study of fungal mannans, we show that the physical form of PRR ligands dictates the immune response. Soluble mannans are immunosilent in the periphery but elicit a potent pro-inflammatory response in the draining lymph node (dLN). By modulating the physical form of mannans, we developed a formulation that targets both the periphery and the dLN. When combined with viral glycoprotein antigens, this mannan formulation broadens epitope recognition, elicits potent antigen-specific neutralizing antibodies, and confers protection against viral infections of the lung. Thus, the physical properties of microbial ligands determine the outcome of the immune response and can be harnessed for vaccine development.

Simultaneous determination of content of eight components in Caesalpinia decapetala by UPLC-MS/MS

The present study established the ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS) method for simultaneous determination of the content of eight major active components in Caesalpinia decapetala and performed the quality evaluation of C. decapetala from different habitats with the chemical pattern recognition. The analysis was carried out on a Waters BEH C_(18) column(2.1 mm×100 mm, 1.7 μm) at 40 ℃, with the mobile phase of water containing 0.1% formic acid(A) and acetonitrile containing 0.1% formic acid under gradient elution, the flow rate of 0.3 mL·min~(-1), and the injection volume of 1 μL. The electrospray ionization(ESI) source in the negative mode and multiple reaction monitoring(MRM) were used for MS quantitative analysis. The content results were analyzed by the hierarchical cluster analysis(HCA) and principal component analysis(PCA) for the evaluation of the quality difference. Eight components showed good linear relationships within their respective concentration ranges(r>0.999), with the average recoveries of 96.85%-103.4% and RSD of 0.52%-2.8%. The analysis results showed that the quality of samples from different batches was different. The samples were classified into three clusters by HCA and PCA. The method is simple, sensitive, accurate, and efficient, and can be used for the quality evaluation of C. decapetala.

Integrated lipidomics and network pharmacology analysis of the protective effects and mechanism of Yuanzhi San on rats with cognitive impairment

Cognitive impairment (CI) can seriously affect people's mental and physical health. Yuanzhi San (YZS) is a classic prescription for treating CI, but the mechanisms need further exploration. The aim of this study is to explore the effect of YZS on promoting the learning and memory ability of CI rats induced by d-galactose combined with aluminum chloride. Behavioral experiments had been used to comprehensively evaluate the established CI model. Brain histological morphology and the expressions of calcium ion signaling pathway related factors in serum were used to evaluate the effect of YZS against CI. Lipids in rat serum were analyzed by ultra-performance liquid chromatography-mass spectrometry (UHPLC-MS) and chemical pattern recognition methods. Network pharmacology was used to find potential chemical compounds, targets, and related signaling pathways against CI with treatment of YZS. The integrated lipidomics and network pharmacology analysis were conducted by Cytoscape software. The results showed that YZS could alleviate neurodegenerative impairment. It was verified that model rats had longer latency time, shorter exploration paths, lower new objects recognition indexes, and shorter exercise time and distances compared with the normal rats in behavioral experiments, indicating that the model rats were successfully established. Rats of YZS 6.67 had significant differences in retention time (p < 0.05), number of entrances (p < 0.01), new object recognition indexes (p < 0.05, p < 0.01), exercise time (p < 0.05), and content of Ca [2]+, CAM, APP, CREB (p < 0.01), CAMK2 (p < 0.05). Rats of YZS 6.67 had five cell layers in hippocampus histological morphology. Behavioral experiments results showed that YZS had an active effect on CI rats. From lipidomics analysis, 129 lipids were screened out by conditions of VIP > 1 and p < 0.05, and 17 lipid markers were identified from the databases, which were divided mainly into five types. Pathway analysis indicated that linoleic acid, α-linolenic acid, arachidonic acid, and glycerophospholipid metabolisms were potential target pathways closely involved in the mechanism YZS’s effects against CI. Network pharmacology focused on 84 chemical compounds, 130 intersection targets, and 10 hub genes of YZS’s effects against CI. Six hub genes and four lipid compounds had intrinsic contact with arachidonic acid metabolism, glycerophospholipid metabolism and linoleate metabolism. The study revealed that YZS could improve animal cognitive behaviors, the expression of factors associated with memory in serum and the histological morphology of hippocampus. Four lipid compounds, three metabolic pathways, and six hub genes of YZS could effectively modulated CI. These results collectively suggest that the main mechanism of YZS in improving CI involves lipid metabolism, which affects biological processes and targets of action in the body.

Exploring the S-process History in the Galactic Disk: Cerium Abundances and Gradients in Open Clusters from the OCCAM/APOGEE Sample

Abstract The APOGEE Open Cluster Chemical Abundances and Mapping survey is used to probe the chemical evolution of the s-process element cerium in the Galactic disk. Cerium abundances were derived from measurements of Ce ii lines in the APOGEE spectra using the Brussels Automatic Code for Characterizing High Accuracy Spectra in 218 stars belonging to 42 open clusters. Our results indicate that, in general, for ages &lt; 4 Gyr, younger open clusters have higher [Ce/Fe] and [Ce/α-element] ratios than older clusters. In addition, metallicity segregates open clusters in the [Ce/X]–age plane (where X can be H, Fe, or the α-elements O, Mg, Si, or Ca). These metallicity-dependent relations result in [Ce/Fe] and [Ce/α] ratios with ages that are not universal clocks. Radial gradients of [Ce/H] and [Ce/Fe] ratios in open clusters, binned by age, were derived for the first time, with d[Ce/H]/d R GC being negative, while d[Ce/Fe]/d R GC is positive. [Ce/H] and [Ce/Fe] gradients are approximately constant over time, with the [Ce/Fe] gradient becoming slightly steeper, changing by ∼+0.009 dex kpc−1 Gyr−1. Both the [Ce/H] and [Ce/Fe] gradients are shifted to lower values of [Ce/H] and [Ce/Fe] for older open clusters. The chemical pattern of Ce in open clusters across the Galactic disk is discussed within the context of s-process yields from asymptotic giant branch (AGB) stars, gigayear time delays in Ce enrichment of the interstellar medium, and the strong dependence of Ce nucleosynthesis on the metallicity of its AGB stellar sources.

Chemical Abundance Scaling Relations for Multiple Elements in z ≃ 2–3 Star-forming Galaxies

Abstract The chemical abundance patterns of gas and stars in galaxies are powerful probes of galaxies’ star formation histories and the astrophysics of galaxy assembly but are challenging to measure with confidence in distant galaxies. In this paper, we report the first measurements of the correlation between stellar mass (M *) and multiple tracers of chemical enrichment (including O, N, and Fe) in individual z ∼ 2–3 galaxies, using a sample of 195 star-forming galaxies from the Keck Baryonic Structure Survey. The galaxies’ chemical abundances are inferred using photoionization models capable of reconciling high-redshift galaxies’ observed extreme rest-UV and rest-optical spectroscopic properties. We find that the M *–O/H relation for our sample is relatively shallow, with moderately large scatter, and is offset ∼0.35 dex higher than the corresponding M *–Fe/H relation. The two relations have very similar slopes, indicating a high level of α-enhancement—O/Fe ≈ 2.2 × (O/Fe)⊙—across two decades in M *. The M *–N/H relation has the steepest slope and largest intrinsic scatter, which likely results from the fact that many z ∼ 2 galaxies are observed near or past the transition from “primary” to “secondary” N production, and may reflect uncertainties in the astrophysical origin of N. Together, these results suggest that z ∼ 2 galaxies are old enough to have seen substantial enrichment from intermediate-mass stars, but are still young enough that Type Ia supernovae have not had time to contribute significantly to their enrichment.

Differential gene expression and chemical patterns of an intertidal crab inhabiting a polluted port and an adjacent marine protected area

The acquisition of data to safeguard marine protected areas located close to ports is important in order to develop plans that allow effective protection from pollution as well as sustainable development of the port. The area Secche della Meloria is a Marine Protected Area (MPA-MEL) three miles from Livorno Harbour (LH), which is characterized by a long history of pollution. Here we studied the bioaccumulation and transcriptomic patterns of the marbled crab, Pachygrapsus marmoratus (Fabricius, 1787) (Crustacea; Brachyura, Grapsidae), inhabiting the two selected sites. Results showed that the two crab populations are significantly different in their chemical composition of trace elements and Polyciclic Aromatic Hydrocarbons (PAHs), and gene expression patterns (1280 DEGs). Enrichment analysis indicated that crabs at LH had the highest stress response genes, and they were associated with higher levels of bioaccumulation detected in body tissues. We are confident that the significant differential gene expression profiles observed between crabs, characterized by significant chemical differences, is associated with responses to contaminant exposure.

Effect of fire on the palatability of plants in an African woodland savanna: varying impacts depending on plant functional groups.

Fire and herbivores are two important drivers of changes in vegetation composition, quality and dynamics and both are highly related to each other. Herbivores are known to respond to fire both in terms of foraging decisions and distribution. However, little is known about the actual changes in plant chemistry following a fire event and how long these changes will last. We investigated the effect of fire on two different plant functional groups (grasses and woody species) in a woodland savanna of southern Africa. We studied chemical compounds known to be important for palatability of five perennial grass and seven woody species (trees and shrubs) common in the woodland savanna and known to be utilized by herbivores. We wanted to know if plant chemistry differs between a recently burned site (burned 2 years ago) and a control site, burned 16 years ago, and if grasses and woody species show similar relative differences between sites (i.e., the plants’ response to fire). We found a clear difference in chemical composition patterns between the plant functional groups, with an almost homogenous response to fire among woody species, but higher variability in response among grass species. Furthermore, we found that woody species maintained a higher nutritional value even 2 years after burning, whereas grasses did not show clear differences among the two investigated sites. Hence, few years after burning, woody plants might still serve as an attraction for herbivores, especially browsers, in contrast to grasses. The knowledge about these differences between the two functional groups in response to fire is beneficial for the development of management strategies for large herbivores whether domestic or wild.

Growth and structural transitions of core-shell nanorods in nanocrystalline Al-Ni-Y

Unique nanorod precipitates with a core-shell structure are found to nucleate from the grain boundaries of a bulk nanocrystalline Al-Ni-Y alloy fabricated via powder consolidation, contributing significantly to stabilization and strengthening. The local structure, chemistry, and evolution of these features during annealing are reported here. In the as-consolidated state, the nanorods can be either structurally ordered or disordered, yet a consistent chemical patterning is found where the core is primarily Al plus C while the shell is enriched with Y. As annealing time increases, more nanorods transform to an ordered structure as they coarsen while the core composition remains unchanged. In contrast, the shell chemistry transitions from Y-rich to Ni-rich with longer annealing treatments, most likely due to the different diffusivities of Y and Ni in Al. Moreover, a spatial and chemical correlation between the nanorods and amorphous complexions is observed, suggesting that these complexions serve as preferential nucleation sites.

Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.

The Galapagos Chip Platform for High-Throughput Screening of Cell Adhesive Chemical Micropatterns.

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and -physical cues at the nano- to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell-material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high-throughput screening platform called "Galapagos chip" is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV-induced two-step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine-learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell-ECM interaction.

Chemical Pattern Recognition for Quality Analysis of Lonicerae Japonicae Flos and Lonicerae Flos Based on Ultra-High Performance Liquid Chromatography and Anti-SARS-CoV2 Main Protease Activity.

Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.

Role of Delta-Notch signalling molecules on cell-cell adhesion in determining heterogeneous chemical and cell morphological patterning.

Cell mechanics and motility are responsible for collective motion of cells that result in overall deformation of epithelial tissues. On the other hand, contact-dependent cell-cell signalling is responsible for generating a large variety of intricate, self-organized, spatial patterns of the signalling molecules. Moreover, it is becoming increasingly clear that the combined mechanochemical patterns of cell shape/size and signalling molecules in the tissues, for example, in cancerous and sensory epithelium, are governed by mechanochemical coupling between chemical signalling and cell mechanics. However, a clear quantitative picture of how these two aspects of tissue dynamics, i.e., signalling and mechanics, lead to pattern and form is still emerging. Although, a number of recent experiments demonstrate that cell mechanics, cell motility, and cell-cell signalling are tightly coupled in many morphogenetic processes, relatively few modeling efforts have focused on an integrated approach. We extend the vertex model of an epithelial monolayer to account for contact-dependent signalling between adjacent cells and between non-adjacent neighbors through long protrusional contacts with a feedback mechanism wherein the adhesive strength between adjacent cells is controlled by the expression of the signalling molecules in those cells. Local changes in cell-cell adhesion lead to changes in cell shape and size, which in turn drives changes in the levels of signalling molecules. Our simulations show that even this elementary two-way coupling of chemical signalling and cell mechanics is capable of giving rise to a rich variety of mechanochemical patterns in epithelial tissues. In particular, under certain parametric conditions, bimodal distributions in cell size and shape are obtained, which resemble experimental observations in cancerous and sensory tissues.

Determination of Global Chemical Patterns in Exhaled Breath for the Discrimination of Lung Damage in PostCOVID Patients Using Olfactory Technology

Determination of Global Chemical Patterns in Exhaled Breath for the Discrimination of Lung Damage in PostCOVID Patients Using Olfactory Technology

Identification of Cordyceps cicadae and Tolypocladium dujiaolongae based on ITS sequences and chemical pattern recognition method

Based on ITS sequences, the molecular identification of Cordyceps cicadae and Tolypocladium dujiaolongae was carried out, and high-performance liquid chromatography(HPLC) fingerprint combined with chemical pattern recognition method was established to differentiate C. cicadae from its adulterant T. dujiaolongae. The genomic DNA from 10 batches of C. cicadae and five batches of T. dujiaolongae was extracted, and ITS sequences were amplified by PCR and sequenced. The stable differential sites of these two species were compared and the phylogenetic tree was constructed via MEGA 7.0. HPLC was used to establish the fingerprints of C. cicadae and T. dujiaolongae, and similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminant analysis(PLS-DA) were applied to investigate the chemical pattern recognition. The result showed that the sources of these two species were different, and there were 115 stable differential sites in ITS sequences of C. cicadae and T. dujiao-longae. The phylogenetic tree could distinguish them effectively. HPLC fingerprints of 18 batches of C. cicadae and 5 batches of T. dujiaolongae were established. The results of CA, PCA, and PLS-DA were consistent, which could distinguish them well, indicating that there were great differences in chemical components between C. cicadae and T. dujiaolongae. The results of PLS-DA showed that six components such as uridine, guanosine, adenosine, and N~6-(2-hydroxyethyl) adenosine were the main differential markers of the two species. ITS sequences and HPLC fingerprint combined with the chemical pattern recognition method can serve as the identification and differentiation methods for C. cicadae and T. dujiaolongae.

Fabrication of heterogeneous chemical patterns on stretchable hydrogels using single-photon lithography.

Curved hydrogel surfaces bearing chemical patterns are highly desirable in various applications, including artificial blood vessels, wearable electronics, and soft robotics. However, previous studies on the fabrication of chemical patterns on hydrogels employed two-photon lithography, which is still not widely accessible to most laboratories. This work demonstrates a new patterning technique for fabricating curved hydrogels with chemical patterns on their surfaces without two-photon microscopy. In this work, we show that exposing hydrogels in fluorophore solutions to single photons via confocal microscopy enables the patterning of fluorophores on hydrogels. By applying this technique to highly stretchable hydrogels, we demonstrate three applications: (1) improving pattern resolution by fabricating patterns on stretched hydrogels and then returning the hydrogels to their initial, unstretched length; (2) modifying the local stretchability of hydrogels at a microscale resolution; and (3) fabricating perfusable microchannels with chemical patterns by winding chemically patterned hydrogels around a template, embedding the hydrogels in a second hydrogel, and then removing the template. The patterning method demonstrated in this work may facilitate a better mimicking of the physicochemical properties of organs in tissue engineering and may be used to make hydrogel robots with specific chemical functionalities.