The microbiome dynamics and interaction of endosymbiotic Symbiodiniaceae and fungi are associated with thermal bleaching susceptibility of coral holobionts.

The thermal bleaching percentage of coral holobionts shows interspecific differences under heat-stress conditions, which are closely related to the coral-associated microbiome. However, the ecological effects of community dynamics and interactions between Symbiodiniaceae and fungi on coral thermal bleaching susceptibility remain unclear. In this study, we analyzed the diversity, community structure, functions, and potential interaction of Symbiodiniaceae and fungi among 18 coral species from a high thermal bleaching risk atoll using next-generation sequencing. The results showed that heat-tolerant C3u sub-clade and Durusdinium dominated the Symbiodiniaceae community of corals and that there were no core amplicon sequence variants in the coral-associated fungal community. Fungal richness and the abundance of confirmed functional animal-plant pathogens were significantly positively correlated with the coral thermal bleaching percentage. Fungal indicators, including Didymellaceae, Chaetomiaceae, Schizophyllum, and Colletotrichum, were identified in corals. Each coral species had a complex Symbiodiniaceae-fungi interaction network (SFIN), which was driven by the dominant Symbiodiniaceae sub-clades. The SFINs of coral holobionts with low thermal bleaching susceptibility exhibited low complexity and high betweenness centrality. These results indicate that the extra heat tolerance of coral in Huangyan Island may be linked to the high abundance of heat-tolerant Symbiodiniaceae. Fungal communities have high interspecific flexibility, and the increase of fungal diversity and pathogen abundance was correlated with higher thermal bleaching susceptibility of corals. Moreover, fungal indicators were associated with the degrees of coral thermal bleaching susceptibility, including both high and intermediate levels. The topological properties of SFINs suggest that heat-tolerant coral have limited fungal parasitism and strong microbial network resilience.IMPORTANCEGlobal warming and enhanced marine heatwaves have led to a rapid decline in coral reef ecosystems worldwide. Several studies have focused on the impact of coral-associated microbiomes on thermal bleaching susceptibility in corals; however, the ecological functions and interactions between Symbiodiniaceae and fungi remain unclear. We investigated the microbiome dynamics and potential interactions of Symbiodiniaceae and fungi among 18 coral species in Huangyan Island. Our study found that the Symbiodiniaceae community of corals was mainly composed of heat-tolerant C3u sub-clade and Durusdinium. The increase in fungal diversity and pathogen abundance has close associations with higher coral thermal bleaching susceptibility. We first constructed an interaction network between Symbiodiniaceae and fungi in corals, which indicated that restricting fungal parasitism and strong interaction network resilience would promote heat acclimatization of corals. Accordingly, this study provides insights into the role of microorganisms and their interaction as drivers of interspecific differences in coral thermal bleaching.

Potential adaptation of scleractinian coral Pocillopora damicornis during hypo-salinity stress caused by extreme pre-flood rainfall over south China.

Global warming intensifies the water cycle, resulting in significant increases in precipitation and river runoff, which brings severe hypo-salinity stress to nearshore coral reefs. Ecological investigations have found that some corals exhibit remarkable adaptability to hypo-salinity stress during mass-bleaching events. However, the exact cause of this phenomenon remains unclear. To elucidate the potential molecular mechanism leading to high tolerance to hypo-salinity stress, Pocillopora damicornis was used as a research object in this study. We compared the differences in transcriptional responses and symbiotic microbiomes between bleaching and unbleaching P. damicornis during hypo-salinity stress caused by extreme pre-flood rainfall over South China in 2022. The results showed that: (1) Under hypo-salinity stress, the coral genes related to immune defense and cellular stress were significantly upregulated in bleaching corals, indicating more severe immune damage and stress, and the Symbiodiniaceae had no significant gene enrichment. Conversely, metabolic genes related to glycolysis/gluconeogenesis were significantly downregulated in unbleaching corals, whereas Symbiodiniaceae genes related to oxidative phosphorylation were significantly upregulated to meet the energy requirements of coral holobiont; (2) C1d was the dominant Symbiodiniaceae subclade in all samples, with no significant difference between the two groups; (3) The symbiotic bacterial community structure was reorganized under hypo-salinity stress. The abundance of opportunistic bacteria increased significantly in bleaching coral, whereas the relative abundance of probiotics was higher in unbleaching coral. This may be due to severe immune damage, making the coral more susceptible to opportunistic infection and bleaching. These results suggest that long-term hypo-salinity acclimation in the Pearl River Estuary enhances the tolerance of some corals to hypo-salinity stress. Corals with higher tolerance may reduce energy consumption by slowing down their metabolism, improve the energy metabolism of Symbiodiniaceae to meet the energy requirements of the coral holobiont, and alter the structure of symbiotic bacterial communities to avoid bleaching.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method.

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

Comprehensive Analysis Reveals the Difference in Volatile Oil between Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li and the Other Four Medicinal Bupleurum Species.

Volatile oil serves as a traditional antipyretic component of Bupleuri Radix. Bupleurum marginatum var. stenophyllum (Wolff) Shan et Y. Li belongs to the genus Bupleurum and is distinguished for its high level of saikosaponins and volatile oils; nonetheless, prevailing evidence remains inconclusive regarding its viability as an alternative resource of other official species. This study aims to systematically compare the volatile oil components of both dried and fresh roots of B. marginatum var. stenophyllum and the four legally available Bupleurum species across their chemical, molecular, bionics, and anatomical structures. A total of 962 compounds were determined via GC-MS from the dried roots; B. marginatum var. stenophyllum showed the greatest differences from other species in terms of hydrocarbons, esters, and ketones, which was consistent with the results of fresh roots and the e-nose analysis. A large number of DEGs were identified from the key enzyme family of the monoterpene synthesis pathway in B. marginatum var. stenophyllum via transcriptome analysis. The microscopic observation results, using different staining methods, further showed the distinctive high proportion of phloem in B. marginatum var. stenophyllum, the structure which produces volatile oils. Together, these pieces of evidence hold substantial significance in guiding the judicious development and utilization of Bupleurum genus resources.

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning.

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.

Measuring the intensive care experience of intensive care unit patients: A cross-sectional study in western China.

BACKGROUND: The negative experiences of intensive care unit (ICU) patients seriously affect their quality of life and survival outcomes. Thus, it is of great significance to evaluate the monitoring experience of ICU patients for the clinical improvement of their experiences and promote interventions. OBJECTIVES: The objective of this study was to investigate patients' experiences of ICU and to understand the sources of patient experience and influencing factors. METHODS: From November 2021 to September 2022, a cross-sectional survey was conducted with 600 inpatients from four grade A-III hospitals in western China. Data were collected using the Chinese version of the Intensive Care Experience Questionnaire. RESULTS: 585 valid questionnaires were collected, the response rate was 97.5%. ICU patients in western China scored below-the-average for their intensive care experience. Family monthly income, occupation types, medical payment method, type of ICU, ICU admission plan, ICU admission times, mechanical ventilation use, fertility status, analgesia, sedation, and Acute Physiology and Chronic Health Evaluation II scores are important factors influencing ICU patients' intensive care experience. CONCLUSIONS: Medical staff need to pay attention to patient experience, improve the awareness of patient stressors and influencing factors, design nursing programs conducive to patient-positive experience, and promote interventions to further improve the long-term prognosis of patients. The results of this study can also be used as a set of nursing-sensitive indicators for evaluating nursing structure, process, and outcomes.

Long-term poor sleep quality is associated with adverse donor reactions in college students in Central China: A population-based cross-sectional study.

BACKGROUND AND OBJECTIVES: Adverse donor reaction (ADR) could adversely impact the recruitment and retention of blood donors, but the evidence of effect of sleep quality on ADR is limited and controversial. The goal of this study was to explore the association between the sleep quality and ADR among college students in Wuhan. MATERIALS AND METHODS: The college student blood donors in Wuhan from March to May 2022 were recruited. Self-compiled general information questionnaire and Pittsburgh sleep quality index (PSQI) were investigated by convenience sampling. Univariable and multivariable logistic regression analyses were used to estimate the association. RESULTS: Among 1014 participants included in this study, 63 were in the ADR group and 951 were in the non-ADR group. Compared with the non-ADR group, the PSQI scores of ADR group were higher (3.44 ± 1.81 vs. 2.78 ± 1.82, p < 0.01). The results of multivariable logistic regression analysis showed that after adjusting gender, body mass index, blood donation history and other potential confounding factors, higher PSQI scores were related to the occurrence of ADRs (odds ratio = 1.231, 95% confidence interval 1.075-1.405), that is, the worse the sleep quality, the more likely the ADRs will occur. CONCLUSION: The long-term poor sleep quality of college students is a risk factor for the occurrence of ADRs. It should be identified early before blood donation to reduce the incidence of ADRs and improve the safety and satisfaction of donors.

Surface Enhancement Effects of Tiny SnO2 Nanoparticle Modification on α-Fe2O3 for Room-Temperature NH3 Sensing.

The development of a gas sensor capable of detecting ammonia with high selectivity and rapid response at room temperature has consistently posed a formidable challenge. To address this issue, the present study utilized a one-step solvothermal method to co-assemble α-Fe2O3 and SnO2 by evenly covering SnO2 nanoparticles on the surface of α-Fe2O3. By controlling the morphology and Fe/Sn mole ratio of the composite, the as-prepared sample exhibits high-performance detection of NH3. At room temperature conditions, a gas sensor composed of α-Fe2O3@3%SnO2 demonstrates a rapid response time of 14 s and a notable sensitivity of 83.9% when detecting 100 ppm ammonia. Experiments and density functional theory (DFT) calculations suggest that the adsorption capacity of α-Fe2O3 to ammonia is enhanced by the surface effect provided by SnO2. Meanwhile, the existence of SnO2 tailors the pore structure and effective surface area of α-Fe2O3, creating multiple channels for the diffusion and adsorption of ammonia molecules. Additionally, an N-N heterostructure is formed between α-Fe2O3 and SnO2, which enhances the potential energy barrier and improves the ammonia sensing performance. Demonstration experiments have proved that the sensor shows significant advantages over commercial sensors in the process of ammonia detection in agricultural facilities. This work provides new insights into the perspectives on ammonia detection at room temperature.

Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology.

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

First total syntheses of kanjone and its natural analogues.

Kanjone (1), a bioactive furanoflavone and a potent biomolecule, was first isolated from Pongamia pinnata (L.). Herein, we have developed two approaches to synthesize kanjone as well as its natural analogues 6-methoxyisopongaglabol (2) and 6,3'-dimethoxy-[2″,3″:7,8]furanoflavone (3) starting from khellin and 3-hydroxy-4-methoxy-benzaldehyde, respectively.

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems.

Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading.

With the promotion of intelligent substations, more and more robots have been used in industrial sites. However, most of the meter reading methods are interfered with by the complex background environment, which makes it difficult to extract the meter area and pointer centerline, which is difficult to meet the actual needs of the substation. To solve the current problems of pointer meter reading for industrial use, this paper studies the automatic reading method of pointer instruments by putting forward the Faster Region-based Convolutional Network (Faster-RCNN) based object detection integrating with traditional computer vision. Firstly, the Faster-RCNN is used to detect the target instrument panel region. At the same time, the Poisson fusion method is proposed to expand the data set. The K-fold verification algorithm is used to optimize the quality of the data set, which solves the lack of quantity and low quality of the data set, and the accuracy of target detection is improved. Then, through some image processing methods, the image is preprocessed. Finally, the position of the centerline of the pointer is detected by the Hough transform, and the reading can be obtained. The evaluation of the algorithm performance shows that the method proposed in this paper is suitable for automatic reading of pointer meters in the substation environment, and provides a feasible idea for the target detection and reading of pointer meters.

Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges.

Surface-enhanced Raman spectroscopy (SERS) inherits the rich chemical fingerprint information on Raman spectroscopy and gains sensitivity by plasmon-enhanced excitation and scattering. In particular, most Raman peaks have a narrow width suitable for multiplex analysis, and the measurements can be conveniently made under ambient and aqueous conditions. These merits make SERS a very promising technique for studying complex biological systems, and SERS has attracted increasing interest in biorelated analysis. However, there are still great challenges that need to be addressed until it can be widely accepted by the biorelated communities, answer interesting biological questions, and solve fatal clinical problems. SERS applications in bioanalysis involve the complex interactions of plasmonic nanomaterials with biological systems and their environments. The reliability becomes the key issue of bioanalytical SERS in order to extract meaningful information from SERS data. This review provides a comprehensive overview of bioanalytical SERS with the main focus on the reliability issue. We first introduce the mechanism of SERS to guide the design of reliable SERS experiments with high detection sensitivity. We then introduce the current understanding of the interaction of nanomaterials with biological systems, mainly living cells, to guide the design of functionalized SERS nanoparticles for target detection. We further introduce the current status of label-free (direct) and labeled (indirect) SERS detections, for systems from biomolecules, to pathogens, to living cells, and we discuss the potential interferences from experimental design, measurement conditions, and data analysis. In the end, we give an outlook of the key challenges in bioanalytical SERS, including reproducibility, sensitivity, and spatial and time resolution.

Multiwalled carbon nanotubes coated with cobalt(II) sulfide nanoparticles for electrochemical sensing of glucose via direct electron transfer to glucose oxidase.

Multiwalled carbon nanotubes coated with cobalt(II) sulfide nanoparticles were prepared and used for immobilization of glucose oxidase (GOx) to obtain an electrochemical glucose biosensor. The nanocomposite was synthesized through an in-situ hydrothermal method and characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and electrochemical impedance spectroscopy. The results show that the nanocomposite possesses a large specific surface area and apparently enhances the direct electron transfer between GOx and the surface of the electrode, best at a potential near -0.43 V (vs. SCE). The immobilized GOx retains its good bioactivity even at a high surface coverage of 30 pmol cm-2. Under the optimum conditions. The biosensor exhibits a wide linear range (from 8 µM to 1.5 mM), a high sensitivity (15 mA M -1 cm-2), and a 5 µM detection limit (at S/N = 3). The sensor is selective, acceptably repeatable, specific and stable. Graphical abstractMultiwalled carbon nanotubes coated with cobalt(II) sulfide nanoparticles (CoS-MWCNTs) were synthesized through in situ hydrothermal method for the construction of a sensitive electrochemical glucose biosensor.

Dietary Supplementation of Vine Tea Ameliorates Glucose and Lipid Metabolic Disorder via Akt Signaling Pathway in Diabetic Rats.

A traditional Chinese tea with many pharmacological effects, vine tea (VT) is considered a potential dietary supplement to improve type 2 diabetes (T2D). To investigate the effect and mechanism of VT on glucose and lipid metabolic disorders in T2D rats, Wistar rats fed a normal diet served as the normal control, while rats fed a high-fat diet combined with low-dose streptozotocin (STZ)-induced T2D were divided into three groups: The model group (MOD); the positive control group (MET, metformin at 200 mg/kg/d); and the VT-treated group (VT500, allowed to freely drink 500 mg/L VT). After four weeks of intervention, biochemical metrics indicated that VT significantly ameliorated hyperglycemia, hyperlipidemia and hyperinsulinemia in T2D rats. Metabolomics research indicated that VT regulated the levels of metabolites closely related to glucose and lipid metabolism and promoted glycogen synthesis. Furthermore, VT had a significant influence on the expression of key genes involved in the Akt signaling pathway, inhibited gluconeogenesis through the Akt/Foxo1/Pck2 signaling pathway, and reduced fatty acid synthesis via the SREBP1c/Fasn signaling pathways. In conclusion, VT has great potential as a dietary supplement to ameliorate glucose and lipid metabolic disorders via the Akt signaling pathway in T2D rats.

Dihydromyricetin protects human umbilical vein endothelial cells from injury through ERK and Akt mediated Nrf2/HO-1 signaling pathway.

Atherosclerosis-related cardiovascular disease is the predominant cause of death worldwide. Ox-LDL-induced vascular endothelial cell injury is a major factor in the pathogenesis of atherosclerosis. Dihydromyricetin (DMY) is a flavonoid extracted from vine tea that exerts multiple pharmacological activities, including cardio-protective, anti-tumor, and anti-oxidative effects. However, it is unreported that DMY shows protective effects on ox-LDL-induced endothelial cell injury. In this study, we used an ox-LDL injured human umbilical vein endothelial cell (HUVEC) in vitro model to explore the protective effects and mechanism of DMY. HUVECs were pretreatment with DMY and then exposed to ox-LDL, the cell viability was measured. Then, the anti-oxidative enzymes were tested by commercial kits and intracellular reactive oxygen species (ROS) was measured by flow cytometry, cell apoptosis was determined by Annexin-V/PI assay and apoptosis-related proteins were detected by western blot. Our results showed that DMY pretreatment provided cytoprotective effects by suppressing ox-LDL-induced endothelial cell apoptosis, mitochondrial membrane depolarization, caspase-3 activation, and modulation of oxidative enzymes, thereby inhibiting ROS generation. The anti-oxidative and anti-apoptotic effects of DMY were abrogated by the transfection of Nrf2 siRNAs and HO-1 inhibitor ZnPP. Furthermore, DMY might activate the Nrf2/HO-1 pathway through activation of the Akt and ERK1/2 pathways, as shown by the inhibition of Nrf2/HO-1 signaling by the inhibitors PD98059 or LY294002 and the transfection of ERK, Akt siRNAs. In this study, DMY protects HUVECs from ox-LDL-induced oxidative injury by activating Akt and ERK1/2, which subsequently activates Nrf2/HO-1 signaling, thereby up-regulating antioxidant enzymes and anti-apoptotic proteins.

DPPH Radical Scavenging and Postprandial Hyperglycemia Inhibition Activities and Flavonoid Composition Analysis of Hawk Tea by UPLC-DAD and UPLC-Q/TOF MSE.

Hawk tea (Litsea coreana Lévl. var. Lanuginosa (Migo) Yen C. Yang & P.H. Huang), a very popular herbal tea material, has attracted more and more attention due to its high antioxidant properties and possible therapeutic effect on type II diabetes mellitus. The raw materials of Hawk tea are usually divided into three kinds: bud tea (BT), primary leaf tea (PLT) and mature leaf tea (MLT). In this study, the DPPH radical scavenging activity and the antimicrobial properties of these three kinds of Hawk tea from different regions were comparatively investigated, and a ultra-high performance liquid chromatographic coupled with a photodiode array detector (UPLC-DAD) method was employed for comparison of the three major flavonoid constituents, including hyperoside, isoquercitrin and astragalin, in different samples of Hawk tea. At the same time, the effect of methanol extract (ME) of PLT on the mouse postprandial blood glucose and the effect of ME and its different fractions (petroleum ether fraction (PE), ethyl acetate fraction (EA), n-butanol fraction (n-BuOH), and water fraction (WF)) on the activity of α-glucosidase were studied. The results showed that Hawk BT and Hawk PLT possessed the higher radicals scavenging activity than Hawk MLT, while the antibacterial activity against P. vulgaris of PLT and MLT was higher than Hawk BT. The contents of the three major flavonoid constituents in samples of Hawk PLT are higher than Hawk BT and Hawk MLT. The mouse postprandial blood glucose levels of the middle dose (0.5 g/kg) group and the high dose (1 g/kg) group with oral administration of the ME of PLT were significantly lower than the control group. What's more, the inhibitory effect of ME of PLT and its EA and n-BuOH fractions on α-glucosidase was significantly higher than that of acarbose. Rapid ultra-high performance liquid chromatography/quadrupole time-of-flight-mass spectrometry (UPLC-ESI-QTOF-MS) was used to identify the flavonoids in Hawk PLT, and a total of 20 flavonoids were identified or tentatively identified by comparing their retention times and accurate mass measurements with reference compounds or literature data. The bioactive flavonoid composition and DPPH radical scavenging activities present in different Hawk tea raw materials are quite different due to the different ontogenesis of these raw materials. Further studies on PLT showed that the substances in PLT ME could reduce the level of mouse postprandial blood glucose through inhibiting the activity of α-glucosidase.

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Label-free surface-enhanced Raman spectroscopy detection of DNA with single-base sensitivity.

Direct, label-free detection of unmodified DNA is a great challenge for DNA analyses. Surface-enhanced Raman spectroscopy (SERS) is a promising tool for DNA analyses by providing intrinsic chemical information with a high sensitivity. To address the irreproducibility in SERS analysis that hampers reliable DNA detection, we used iodide-modified Ag nanoparticles to obtain highly reproducible SERS signals of single- and double-strand DNA in aqueous solutions close to physiological conditions. The phosphate backbone signal was used as an internal standard to calibrate the absolute signal of each base for a more reliable determination of the DNA structure, which has not been achieved before. Clear identification of DNA with single-base sensitivity and the observation of a hybridization event have been demonstrated.

Identification, characterization, and utilization of single copy genes in 29 angiosperm genomes.

BACKGROUND: Single copy genes are common across angiosperm genomes. With the sufficiently high quality sequenced genomes, the identification of large-scale single copy genes among multiple species is possible. Although some characteristics have been reported, our study provides novel insights into single copy genes. RESULTS: We identified single copy genes across 29 angiosperm genomes. A significant negative correlation was found between the number of duplicate blocks and the number of single copy genes. We found that a considerable number of single copy genes are located in organelles, showing a preference for binding and catalytic activity. The analysis of effective number of codons (Nc) illustrates that single copy genes have a stronger codon bias than non-single copy genes in eudicots. The relative high expression level of single copy genes was partially confirmed by the RNA-seq data, rather than the Codon Adaptation Index (CAI). Unlike in most other species, a strongly negatively correlation occurs between Nc and GC3 among single copy genes in grass genomes. When compared to all non-single copy genes, single copy genes indicate more conservation (as indicated by Ka and Ks values). But our alternative splicing (AS) results reveal that selective constraints are weaker in single copy genes than in low copy family genes (1-10 in-paralogs) and stronger than high copy family genes (>10 in-paralogs). Using concatenated shared single copy genes, we obtained a well-resolved phylogenetic tree. With the addition of intron sequences, the branch support is improved, but striking incongruences are also evident. Therefore, it is noteworthy that inclusion of intron sequences seems more appropriate for the phylogenetic reconstruction at lower taxonomic levels. CONCLUSIONS: Our analysis provides insight into the evolutionary characteristics of single copy genes across 29 angiosperm genomes. The results suggest that there are key differences in evolutionary constraints between single copy genes and non-single copy genes. And to some extent, these evolutionary constraints show some species-specific differences, especially between eudicots and monocots. Our preliminary evidence also suggests that the concatenated shared single copy genes are well suited for use in resolving phylogenetic relationships.