Optimizing the ecological network of resource-based cities to enhance the resilience of regional ecological networks.

Mineral extraction in resource-based cities has caused serious damage to the original ecology, resulting in poor regional vegetation growth, reduced carbon sequestration capacity, and reduced ecosystem resilience. Especially in resource-based cities with fragile ecology, the overall anti-interference ability of the environment is relatively worse. Seeking ecological network optimization solutions that can improve vegetation growth conditions on a large scale is an effective way to enhance the resilience of regional ecosystems. This paper introduces carbon sequestration indicators and designs a differential ecological networks (ENs) optimization model (FTCC model) to achieve the goal of improving ecosystem resilience. The model identifies the patches that need to be optimized and their optimization directions based on the differences in ecological function-topology-connectivity-carbon sequestration of the patches. Finally, the resilience of the ecological network before and after optimization was compared, proving that the model is effective. The results show that the sources in the Yulin ENs form three main clusters, with connectivity between clusters relying on only a few patches. The patches in the northeastern and southwest clusters are large but their ecological functions need to be improved. After optimization, 16 new stepping stones were added, 38 new corridors were added, and the ecological function of 39 patches was enhanced. The optimized ecological network resilience was improved in terms of structure, function, and carbon sinks, and carbon sinks increased by 6364.5 tons. This study provides a reference for measures to optimize landscape space and manage ecosystem resilience enhancement in resource-based cities.

Research on the coupling coordination and driving role of urbanization and ecological resilience in the middle and lower reaches of the Yangtze River.

Background: The growth of urbanization in the 20th and 21st centuries has resulted in unprecedented ecological security issues. The imbalance between urban development and internal ecological security is a growing concern. Methods: Based on the urban development process and the characteristics of ecosystem resilience, the corresponding urbanization evaluation system ("scale-structure-benefit") and ecosystem resilience assessment model ("resistance-adaptability-restoring") were constructed to explore the changes in each dimension as well as to analyze the spatial and temporal changes and driving effects of the coupled coordination level of urbanization and ecological resilience using the coupled coordination degree (CCD) model and geographically and temporally weighted regression (GTWR). Results: (1) From 2005 to 2020, urbanization levels increased (from 0.204 to 0.264, respectively), whereas the level of ecological resilience gradually decreased (from 0.435 to 0.421, respectively). The spatial distribution of urbanization is rather steady, with a "high-northeast low-southwest" pattern of regional distribution; however, the spatial distribution pattern of ecological resilience is essentially the reverse. (2) During the study period, there was an improvement in the level of coordination between urbanization and ecological resilience, with an increase from 0.524 to 0.540. However, the main coordination type remained the same, with over 46% being at the basic coordination stage. The relative development type was dominated by the lagging urbanization stage, with the lagging ecological resilience and synchronous development stages accounting for a smaller proportion, and the space was distributed in a block-like cluster. (3) The running results of the GTWR show that the core dimensions of the whole region are scale, benefit, and structure, and the impact of each dimension shows obvious spatial heterogeneity. Cities with different levels of relative development also have different central dimensions. This research will provide support for the coordination of urban development in areas where economic construction and ecological resilience are not coordinated, and will contribute to the sustainable development of urban areas.

An efficient approach addressing the chemical complexity of Jiawei Fangji Huangqi decoction by integrating ultra-high-performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry and intelligent data processing workflows.

A challenge in the quality control of traditional Chinese medicine is the systematic multicomponent characterization of the compound formulae. Jiawei Fangji Huangqi, a modified form of Fangji Huangqi, is a prescription comprising seven herbal medicines. To address the chemical complexity of the Jiawei Fangji Huangqi decoction, we integrated ion mobility-quadrupole time-of-flight high-definition MSE coupled to ultra-high-performance liquid chromatography and intelligent data processing workflows available in the UNIFI software package. Good chromatographic separation was achieved on CORTECS UPLC T3 column within 52 min, and high-accuracy MS2 data were acquired using high-definition MSE in the negative and positive modes. A chemical library of 1250 compounds was created and incorporated into the UNIFI software to enable automatic peak annotation of the high-definition MSE data. We identified or tentatively characterize 430 compounds in the Jiawei Fangji Huangqi decoction. The potential superiority of high-definition MSE over conventional MS data acquisition approaches was revealed in its spectral quality (MS2 ), differentiation of isomers, separation of coeluting compounds, and target mass coverage. The multiple components of the Jiawei Fangji Huangqi decoction were elucidated, offering insight into its improved pharmacological action compared with that of the Fangji Huangqi formula.

Alginate-based edible coating with oregano essential oil/ß-cyclodextrin inclusion complex for chicken breast preservation.

A sodium alginate (SA) edible coating containing oregano essential oil (OEO)/ß-cyclodextrin (ß-CD) inclusion complexes (SA/OEO-MP coating) was developed to extend the shelf life of fresh chicken breast during refrigeration storage. First, OEO was inserted into the hydrophobic interior of ß-CD to form an inclusion complex (OEO-MP) that maintained its excellent antioxidant and antibacterial activities. The formed OEO-MP was characterized using fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). In addition, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) results demonstrated that ß-CD could improve the thermal stability of OEO. The encapsulation efficiency reached 71.6 %, and OEO was released continuously from the OEO-MP. The lipid oxidation, total viable count (TVC) and sensory properties of chicken breasts were regularly monitored when OEO-MP was incorporated into the SA coating for chicken breast preservation. Compared with the uncoated group, the SA/OEO-MP-coated groups showed significantly reduced increases in pH, thiobarbituric acid reactive substances (TBARS), total volatile base nitrogen (TVB-N), and TVC, especially in the SA/OEO-MP1 group. In summary, the SA/OEO-MP coating could preserve the chicken breast by reducing lipid oxidation and inhibiting the proliferation of microorganisms. It would be developed as a prospective edible packaging for chicken preservation.

Artificial intelligence can accurately distinguish IgA nephropathy from diabetic nephropathy under Masson staining and becomes an important assistant for renal pathologists.

Introduction: Hyperplasia of the mesangial area is common in IgA nephropathy (IgAN) and diabetic nephropathy (DN), and it is often difficult to distinguish them by light microscopy alone, especially in the absence of clinical data. At present, artificial intelligence (AI) is widely used in pathological diagnosis, but mainly in tumor pathology. The application of AI in renal pathological is still in its infancy. Methods: Patients diagnosed as IgAN or DN by renal biopsy in First Affiliated Hospital of Zhejiang Chinese Medicine University from September 1, 2020 to April 30, 2022 were selected as the training set, and patients who diagnosed from May 1, 2022 to June 30, 2022 were selected as the test set. We focused on the glomerulus and captured the field of the glomerulus in Masson staining WSI at 200x magnification, all in 1,000 × 1,000 pixels JPEG format. We augmented the data from training set through minor affine transformation, and then randomly split the training set into training and adjustment data according to 8:2. The training data and the Yolov5 6.1 algorithm were used to train the AI model with constant adjustment of parameters according to the adjusted data. Finally, we obtained the optimal model, tested this model with test set and compared it with renal pathologists. Results: AI can accurately detect the glomeruli. The overall accuracy of AI glomerulus detection was 98.67% and the omission rate was only 1.30%. No Intact glomerulus was missed. The overall accuracy of AI reached 73.24%, among which the accuracy of IgAN reached 77.27% and DN reached 69.59%. The AUC of IgAN was 0.733 and that of DN was 0.627. In addition, compared with renal pathologists, AI can distinguish IgAN from DN more quickly and accurately, and has higher consistency. Discussion: We constructed an AI model based on Masson staining images of renal tissue to distinguish IgAN from DN. This model has also been successfully deployed in the work of renal pathologists to assist them in their daily diagnosis and teaching work.

The efficacy of acupuncture combined with other therapies in post stroke cognitive impairment: A network meta-analysis.

BACKGROUND: The network meta-analysis was used to evaluate the efficacy of acupuncture combined with other therapies in the treatment of post stroke cognitive impairment (PSCI). METHODS: The China National Knowledge Infrastructure, Wanfang DATA, Vip Chinese Periodic Service Platform, PUBMED, Cochrane Library, Web of Science, and Embase were searched for randomized controlled trials (RCTs) published before March 18, 2023. Two researchers independently reviewed articles and extracted data, and then qualified papers were included in the study. STATA 14.0 was used for network meta-analysis. RESULTS: A total of 29 articles including 2241 patients were included in this study. The treatment of the intervention group includes acupuncture combined with traditional Chinese medicine prescriptions (TCMP), acupuncture combined with hyperbaric oxygen (HBO), acupuncture combined with repetitive transcranial magnetic stimulation (rTMS), acupuncture combined with cognitive rehabilitation (CR), acupuncture combined with donepezil. The intervention of the control group includes acupuncture, HBO, rTMS, CR, TCMP, and donepezil. In terms of improving the score of Minimum Mental State Examination (MMSE), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the score of Montreal Cognitive Assessment (MoCA), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the total effective rate of clinical treatment, acupuncture combined with rTMS was most likely to be the best treatment (P < .05). CONCLUSION: Acupuncture combined with TCMP may be the best treatment method among all of the above treatments for PSCI.

The response of ecosystem vulnerability to climate change and human activities in the Poyang lake city group, China.

Ecosystem vulnerability is an ecological response of the environment to external damage. Studying the influencing factors and spatiotemporal changes of ecosystem vulnerability is helpful to maintain ecological balance. At present, studies on ecosystem vulnerability are relatively homogeneous and rarely integrate climate change and human activities. Based on a habitat-function framework, this study analyzed the response of ecosystem vulnerability on climate change and human activities in the Poyang Lake City Group (PLCG) in 2010, 2015 and 2020. The spatial agglomeration of ecosystem vulnerability has been analyzed by using GeoDa model. The interaction of factors on ecosystem vulnerability have been analyzed by using geographical detector. It can be seen that the ecosystem vulnerability of the PLCG have increased from 2010 to 2020. The impacts of climate change to the ecosystem vulnerability have showed a positive correlation. Meanwhile, the key factors leading to the change of ecological vulnerability are still human activities. This methodology demonstrates a high level of robustness when applied to other research domains. This research is conducive to maintaining the integrity of the ecosystem, realizing the development of man and nature, and promoting the sound and rapid development of economic society.

Ferulic acid as a therapeutic agent in depression: Evidence from preclinical studies.

Depression is a common but severe mood disorder with a very high prevalence across the general population. Depression is of global concern and poses a threat to human physical and mental health. Ferulic acid (FA) is a natural active ingredient that has antioxidative, anti-inflammatory, and free radical scavenging properties. Furthermore, studies have shown that FA can exert antidepressant effects through a variety of mechanisms. The aim of the review was to comprehensively elucidate the mechanisms in FA that alleviate depression using animal models. The in vivo (animal) studies on the mechanism of FA treatment of depression were searched in PubMed, Chinese National Knowledge Infrastructure, Baidu academic, and Wan fang databases. Thereafter, the literature conclusions were summarized accordingly. Ferulic acid was found to significantly improve the depressive-like behaviors of animal models, suggesting that FA is a potential natural product in the treatment of depression. The mechanisms are achieved by enhancing monoamine oxidase A (MOA) activity, inhibiting microglia activation and inflammatory factor release, anti-oxidative stress, promoting hippocampal nerve regeneration, increasing brain-derived neurotrophic factor secretion, regulating gut microbiome, and activating protein kinase B/collapsin response mediator protein 2 (AKT/CRMP2) signaling pathway. Ferulic acid produces significant antidepressant effects in animal depression models through various mechanisms, suggesting its potential value as a treatment of depression. However, clinical research trials involving FA are required further to provide a solid foundation for its clinical application.

Analysis of Noise-Detection Characteristics of Electric Field Coupling in Quartz Flexible Accelerometer.

The internal electric field coupling noise of a quartz flexible accelerometer (QFA) restricts the improvement of the measurement accuracy of the accelerometer. In this paper, the internal electric field coupling mechanism of a QFA is studied, an electric field coupling detection noise model of the accelerometer is established, the distributed capacitance among the components of the QFA is simulated, the structure of the detection noise transfer system of different carrier modulation differential capacitance detection circuits is analyzed, and the influence of each transfer chain on the detection noise is discussed. The simulation results of electric field coupling detection noise show that the average value of detection noise can reach 41.7 µg, which is close to the effective resolution of the QFA, 50 µg. This confirms that electric field coupling detection noise is a non-negligible factor affecting the measurement accuracy of the accelerometer. A method of adding a high-pass filter to the front of the phase-shifting circuit is presented to suppress the noise of electric field coupling detection. This method attenuates the average value of the detected noise by about 78 dB, and reduces the average value of the detected noise to less than 0.1 µg, which provides a new approach and direction for effectively breaking through the performance of the QFA.

Preparation of a Tetra-Imidazolium Salt and Sensing for p-Dinitrobenzene.

A macrocyclic tetra-imidazolium salt (2) based on quinoxaline was prepared and characterized. The recognition of 2 to nitro compounds was investigated by fluorescence spectroscopy, 1H NMR titrations, MS, IR spectroscopy, and UV/vis spectroscopy. The results displayed that 2 was able to effectively differentiate p-dinitrobenzene from other nitro compounds via the fluorescence method.

Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China.

It is significant to investigate the coupling and coordination between agricultural carbon emission efficiency (ACEE) and food security and to achieve peak carbon dioxide emissions and carbon neutrality in agriculture as early as possible while ensuring national food security. The Super-SBM (slack-based model) and the comprehensive index method were used to measure the ACEE and food security level in Henan province from 2010 to 2020. The coupling coordination degree (CCD) and the relative state of ACEE and food security were analyzed using the coupling coordination degree model (CCDM) and the relative development degree model (RDDM). In addition, the interaction between ACEE and food security and the spatial-temporal heterogeneity were analyzed by combining with the geographically and temporally weighted regression (GTWR) model. The results showed that: Firstly, the overall level of ACEE was high, and the spatial heterogeneity of ACEE was significant. The spatial pattern of food security is relatively stable, with high levels in the south and low levels in the north. Secondly, The CCD between ACEE and food security in Henan province generally shows a decreasing trend. In the spatial dimension, the CCD between ACEE and food security in Henan province exhibits a spatial divergence characteristic of low in the center and high in the north and south, with significant regional variations. Finally, there is spatial and temporal heterogeneity between ACEE and food security. The regression coefficients differ significantly among different cities, the regression coefficients do not show a consistent positive or negative correlation, and the regression coefficients are distributed both positively and negatively. This study serves as a guide for achieving the goal of double carbon in agriculture and ensuring food security.

Activation Enhancement and Grain Size Improvement for Poly-Si Channel Vertical Transistor by Laser Thermal Annealing in 3D NAND Flash.

The bit density is generally increased by stacking more layers in 3D NAND Flash. Lowering dopant activation of select transistors results from complex integrated processes. To improve channel dopant activation, the test structure of vertical channel transistors was used to investigate the influence of laser thermal annealing on dopant activation. The activation of channel doping by different thermal annealing methods was compared. The laser thermal annealing enhanced the channel activation rate by at least 23% more than limited temperature rapid thermal annealing. We then comprehensively explore the laser thermal annealing energy density on the influence of Poly-Si grain size and device performance. A clear correlation between grain size mean and grain size sigma, large grain size mean and sigma with large laser thermal annealing energy density. Large laser thermal annealing energy density leads to tightening threshold voltage and subthreshold swing distribution since Poly-Si grain size regrows for better grain size distribution with local grains optimization. As an enabler for next-generation technologies, laser thermal annealing will be highly applied in 3D NAND Flash for better device performance with stacking more layers, and opening new opportunities of novel 3D architectures in the semiconductor industry.

A multidimensional chromatography/high-resolution mass spectrometry approach for the in-depth metabolites characterization of two Astragalus species.

To address the chemical complexity is indispensable in a number of research fields. Herb metabolome is typically composed by more than one class of structure analogs produced via different biosynthetic pathways. Multidimensional chromatography (MDC), due to the greatly enhanced separation space, offers the potential solution to comprehensive characterization of herbal metabolites. Here, we presented a strategy, by integrating MDC and quadrupole time-of-flight mass spectrometry (QTOF-MS), to accomplish the in-depth herbal metabolites characterization. Using the metabolome of two Astragalus species (A. membranaceus var. mongholicus,AMM; A. membranaceus, AM) as the case, an off-line three-dimensional liquid chromatography (3D-LC) system was established: hydrophilic interaction chromatography using an XAmide column as the first dimension (1D) for fractionating the total extract, on-line reversed-phase × reversed-phase liquid chromatography separately configuring a CSH Fluoro-Phenyl column and a Cosmocore C18 column as the second dimension (2D) and the third dimension (3D) of chromatography to enable the explicit separation of three well fractionated samples. Moreover, the negative-mode collision-induced dissociation by QTOF-MS under the optimized condition could provide diversified fragments that were useful for the structural elucidation of AMM and AM. An in-house library (composed by 247 known compounds) and comparison with 43 reference standards were utilized to assist more reliable characterization. We could characterize 513 compounds from two Astragalus species (344 from AMM and 323 from AM), including 236 flavonoids, 150 triterpenoids, 18 organic acids, and 109 others. Conclusively, the established MDC approach gained excellent performance favoring the analogs-oriented in-depth characterization of herbal metabolites, but received uncompromising analytical efficiency.

Classification of rice leaf blast severity using hyperspectral imaging.

Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity.

A Novel Capacitorless 1T DRAM with Embedded Oxide Layer.

A novel vertical dual surrounding gate transistor with embedded oxide layer is proposed for capacitorless single transistor DRAM (1T DRAM). The embedded oxide layer is innovatively used to improve the retention time by reducing the recombination rate of stored holes and sensing electrons. Based on TCAD simulations, the new structure is predicted to not only have the characteristics of fast access, random read and integration of 4F2 cell, but also to realize good retention and deep scaling. At the same time, the new structure has the potential of scaling compared with the conventional capacitorless 1T DRAM.

Construct a classification decision tree model to select the optimal equation for estimating glomerular filtration rate and estimate it more accurately.

Chronic kidney disease (CKD) has become a worldwide public health problem and accurate assessment of renal function in CKD patients is important for the treatment. Although the glomerular filtration rate (GFR) can accurately evaluate the renal function, the procedure of measurement is complicated. Therefore, endogenous markers are often chosen to estimate GFR indirectly. However, the accuracy of the equations for estimating GFR is not optimistic. To estimate GFR more precisely, we constructed a classification decision tree model to select the most befitting GFR estimation equation for CKD patients. By searching the HIS system of the First Affiliated Hospital of Zhejiang Chinese Medicine University for all CKD patients who visited the hospital from December 1, 2018 to December 1, 2021 and underwent Gate's method of 99mTc-DTPA renal dynamic imaging to detect GFR, we eventually collected 518 eligible subjects, who were randomly divided into a training set (70%, 362) and a test set (30%, 156). Then, we used the training set data to build a classification decision tree model that would choose the most accurate equation from the four equations of BIS-2, CKD-EPI(CysC), CKD-EPI(Cr-CysC) and Ruijin, and the equation was selected by the model to estimate GFR. Next, we utilized the test set data to verify our tree model, and compared the GFR estimated by the tree model with other 13 equations. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Bland-Altman plot were used to evaluate the accuracy of the estimates by different methods. A classification decision tree model, including BSA, BMI, 24-hour Urine protein quantity, diabetic nephropathy, age and RASi, was eventually retrieved. In the test set, the RMSE and MAE of GFR estimated by the classification decision tree model were 12.2 and 8.5 respectively, which were lower than other GFR estimation equations. According to Bland-Altman plot of patients in the test set, the eGFR was calculated based on this model and had the smallest degree of variation. We applied the classification decision tree model to select an appropriate GFR estimation equation for CKD patients, and the final GFR estimation was based on the model selection results, which provided us with greater accuracy in GFR estimation.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

Genome-wide analysis of the heat shock transcription factor gene family in Sorbus pohuashanensis (Hance) Hedl identifies potential candidates for resistance to abiotic stresses.

Heat shock transcription factors (Hsfs) are essential regulators of plant responses to abiotic stresses, growth, and development. However, all the Hsf family members have not been identified in Sorbus pohuashanensis. Therefore, the aim of this study was to identify the Hsf family members in S. pohuashanensis and examine their expression under abiotic stress conditions through the integration of gene structure, phylogenetic relationships, chromosome location, and expression patterns. Bioinformatics-based methods, identified 33 Hsfs in S. pohuashanensis. Phylogenetic analysis of Hsfs from S. pohuashanensis and other species revealed that they were more closely related to apples and white pears, followed by Populus trichocarpa, and most distantly related to Arabidopsis. Moreover, the Hsfs were clustered into three major groups: A, B, and C. Gene structure and conserved motif analysis revealed a high degree of conservation among members of the same class. Collinearity analysis revealed that segmental duplication played an essential role in increasing the size of the SpHsfs gene family in S. pohuashanensis. Additionally, several cis-acting elements associated with growth and development, hormone response, and stress were found in the promoter region of SpHsfs genes. Furthermore, expression analysis in various tissues of S. pohuashanensis showed that the genes were closely associated with heat, drought, salt stress, growth, and developmental processes. Overall, these results provide valuable information on the evolutionary relationships of the Hsf gene family. These genes stand as strong functional candidates for further studies on the resistance of S. pohuashanensis to abiotic stresses.

An ion mobility-enabled and high-efficiency hybrid scan approach in combination with ultra-high performance liquid chromatography enabling the comprehensive characterization of the multicomponents from Carthamus tinctorius.

Liquid chromatography/mass spectrometry (LC/MS) is extensively applied for the untargeted/targeted analyses of the herbal components, utilizing data-dependent acquisition (DDA) or data-independent acquisition (DIA) to record the fragmentation information useful for the structural elucidation. A new trend recently has emerged by integrating DDA and DIA to render the hybrid scan, which, unfortunately, has rarely been reported. Herein, by using the Vion™ ion-mobility quadrupole time-of-flight mass spectrometer, a hybrid scan strategy (HDMSE-HDDDA) was presented and validated by the untargeted characterization of the multicomponents from Carthamus tinctorius (safflower), in combination with reversed-phase ultra-high performance liquid chromatography (RP-UHPLC). Good chromatographic separation was achieved on an HSS T3 column within 26 min, while HDMSE-MS/MS was used to acquire the collision-induced dissociation MS2 data in the negative mode. Automatic workflows (e.g., data correction, precursors/product ions matching, and peak annotation) were well established on UNIFI™ (incorporating an in-house library recording 261 known compounds) to process the obtained MS2 data. Compared with single DDA or DIA, the hybrid approach of HDMSE-HDDDA better balanced between the coverage and reliability, led to high-definition MS spectra, offered useful collision cross section (CCS) information, and showed satisfactory identification performance comparable to MSE. A total of 141 components (involving 41 quinochalcones, 66 flavanols/flavones, 11 flavanones, 6 organic acids, 1 polyacetylene, and 16 others) were characterized from safflower. Moreover, CCS prediction could assist isomers characterization, to some extent. Conclusively, this hybrid scan approach enables a dimension-enhanced MS data acquisition strategy providing the complementary structural information, which more suits the chemical characterization of complex samples.

Genome sequence and transcriptome of Sorbus pohuashanensis provide insights into population evolution and leaf sunburn response.

Sorbus pohuashanensis (Hance) Hedl. is a potential horticulture and medicinal plant, but its genomic and genetic backgrounds remain unknown. Here, we sequence and assemble the S. pohuashanensis reference genome using PacBio long reads. Based on the new reference genome, we resequence a core collection of 22 Sorbus spp. samples, which are divided into 2 groups (G1 and G2) based on phylogenetic and PCA analyses. These phylogenetic clusters are highly consistent with their classification based on leaf shape. Natural hybridization between the G1 and G2 groups is evidenced by a sample (R21) with a highly heterozygous genotype. Nucleotide diversity (π) analysis shows that G1 has a higher diversity than G2 and that G2 originated from G1. During the evolution process, the gene families involved in photosynthesis pathways expanded and the gene families involved in energy consumption contracted. RNA-seq data suggests that flavonoid biosynthesis and heat-shock protein (HSP)-heat-shock factor (HSF) pathways play important roles in protection against sunburn. This study provides new insights into the evolution of Sorbus spp. genomes. In addition, the genomic resources, and the identified genetic variations, especially those related to stress resistance, will help future efforts to produce and breed Sorbus spp.