Dirigent isoflavene-forming PsPTS2: 3D structure, stereochemical, and kinetic characterization comparison with pterocarpan-forming PsPTS1 homolog in pea.

Pea phytoalexins (-)-maackiain and (+)-pisatin have opposite C6a/C11a configurations, but biosynthetically how this occurs is unknown. Pea dirigent-protein (DP) PsPTS2 generates 7,2'-dihydroxy-4',5'-methylenedioxyisoflav-3-ene (DMDIF), and stereoselectivity toward four possible 7,2'-dihydroxy-4',5'-methylenedioxyisoflavan-4-ol (DMDI) stereoisomers was investigated. Stereoisomer configurations were determined using NMR spectroscopy, electronic circular dichroism, and molecular orbital analyses. PsPTS2 efficiently converted cis-(3R,4R)-DMDI into DMDIF 20-fold faster than the trans-(3R,4S)-isomer. The 4R-configured substrate's near ß-axial OH orientation significantly enhanced its leaving group abilities in generating A-ring mono-quinone methide (QM), whereas 4S-isomer's α-equatorial-OH was a poorer leaving group. Docking simulations indicated that the 4R-configured ß-axial OH was closest to Asp51, whereas 4S-isomer's α-equatorial OH was further away. Neither cis-(3S,4S)- nor trans-(3S,4R)-DMDIs were substrates, even with the former having C3/C4 stereochemistry as in (+)-pisatin. PsPTS2 used cis-(3R,4R)-7,2'-dihydroxy-4'-methoxyisoflavan-4-ol [cis-(3R,4R)-DMI] and C3/C4 stereoisomers to give 2',7-dihydroxy-4'-methoxyisoflav-3-ene (DMIF). DP homologs may exist in licorice (Glycyrrhiza pallidiflora) and tree legume Bolusanthus speciosus, as DMIF occurs in both species. PsPTS1 utilized cis-(3R,4R)-DMDI to give (-)-maackiain 2200-fold more efficiently than with cis-(3R,4R)-DMI to give (-)-medicarpin. PsPTS1 also slowly converted trans-(3S,4R)-DMDI into (+)-maackiain, reflecting the better 4R configured OH leaving group. PsPTS2 and PsPTS1 provisionally provide the means to enable differing C6a and C11a configurations in (+)-pisatin and (-)-maackiain, via identical DP-engendered mono-QM bound intermediate generation, which PsPTS2 either re-aromatizes to give DMDIF or PsPTS1 intramolecularly cyclizes to afford (-)-maackiain. Substrate docking simulations using PsPTS2 and PsPTS1 indicate cis-(3R,4R)-DMDI binds in the anti-configuration in PsPTS2 to afford DMDIF, and the syn-configuration in PsPTS1 to give maackiain.

Sampling complex topology structures for spiking neural networks.

Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics.

BACKGROUND: Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis. METHODS: We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed. RESULTS: We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways. CONCLUSION: We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

Anthropogenic heat variation during the COVID-19 pandemic control measures in four Chinese megacities.

Anthropogenic heat (AH) is an important input for the urban thermal environment. While reduction in AH during the Coronavirus disease 2019 (COVID-19) pandemic may have weakened urban heat islands (UHI), quantitative assessments on this are lacking. Here, a new AH estimation method based on a remote sensing surface energy balance (RS-SEB) without hysteresis from heat storage was proposed to clarify the effects of COVID-19 control measures on AH. To weaken the impact of shadows, a simple and novel calibration method was developed to estimate the SEB in multiple regions and periods. To overcome the hysteresis of AH caused by heat storage, RS-SEB was combined with an inventory-based model and thermal stability analysis framework. The resulting AH was consistent with the latest global AH dataset and had a much higher spatial resolution, providing objective and refined features of human activities during the pandemic. Our study of four Chinese megacities (Wuhan, Shanghai, Beijing, and Guangzhou) indicated that COVID-19 control measures severely restricted human activities and notably reduced AH. The reduction was up to 50% in Wuhan during the lockdown in February 2020 and gradually decreased after the lockdown was eased in April 2020, similar to that in Shanghai during the Level 1 pandemic response. In contrast, AH was less reduced in Guangzhou during the same period and increased in Beijing owing to extended central heating use in winter. AH decreased more in urban centers and the change in AH varied in terms of urban land use between cities and periods. Although UHI changes during the COVID-19 pandemic cannot be entirely attributed to AH changes, the considerable reduction in AH is an important feature accompanying the weakening of the UHI.

Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities.

Anthropogenic heat (AH), an essential urban heat source, is often overlooked or simplified in research on the multiple temporal and spatial driving mechanisms of the urban heat island (UHI), and case studies investigating the impacts of different AH connotations are scarce. This study estimated the AH in seven typical Chinese cities based on a remote sensing surface energy balance model (AHseb) and an energy consumption inventory-machine learning model (AHinv). The intensity of the surface UHI was extracted using land surface temperatures, and then the linear mixed-effects model and geographic detectors were used to analyze the driving effect of AH on the UHI. Despite the similar shapes of the spatial profile curves, the AH derived from the two models differed in both temporal and spatial characteristics, which was more typical in winter and in urban centers, and AHinv had a more notable central spread feature than AHseb. The AH driving effects on UHI were notably influenced by spatial and temporal heterogeneity, particularly in regions with distinct background climates. However, after controlling for the random effects of the background climate, AH still exhibited a considerable enhancing effect on the UHI. AHseb outperformed AHinv in terms of linear positive correlation and interpretation rate for UHI. Meanwhile, interactions with other potential factors enhanced AH driving effects. Consequently, UHI mitigation must be tailored to the local context by integrating multiple drivers, and for the heating effects of AH, it is necessary to develop specific mitigation measures by limiting the conversion of AHinv to AHseb in addition to reducing the heat production. The findings offer guidance for analyzing and optimizing urban thermal climates with a focus on AH or energy consumption control.

Dirigent protein subfamily function and structure in terrestrial plant phenol metabolism.

Aquatic plant transition to land, and subsequent terrestrial plant species diversification, was accompanied by the emergence and massive elaboration of plant phenol chemo-diversity. Concomitantly, dirigent protein (DP) and dirigent-like protein subfamilies, derived from large multigene families, emerged and became extensively diversified. DP biochemical functions as gateway entry points into new and diverse plant phenol skeletal types then markedly expanded. DPs have at least eight non-uniformly distributed subfamilies, with different DP subfamily members of known biochemical/physiological function now implicated as gateway entries to lignan, lignin, aromatic diterpenoid, pterocarpan and isoflavene pathways. While some other DP subfamily members have jacalin domains, both these and indeed the majority of DPs throughout the plant kingdom await discovery of their biochemical roles. Methods and approaches were developed to discover DP biochemical function as gateway entry points to distinct plant phenol skeletal types in land plants. Various DP 3D X-ray structural determinations enabled structure-based comparative sequence analysis and modeling to understand similarities and differences among the different DP subfamilies. We consider that the core DP ß-barrel fold and associated characteristics are likely common to all DPs, with several residues conserved and nearly invariant. There is also considerable variation in residue composition and topography of the putative substrate binding pockets, as well as substantial differences in several loops, such as the ß1-ß2 loop. All DPs likely bind and stabilize quinone methide intermediates, while guiding distinctive regio- and/or stereo-chemical entry into Nature's chemo-diverse land plant phenol metabolic classes.

Annotated genome sequence of a fast-growing diploid clone of red alder (Alnus rubra Bong.).

Red alder (Alnus rubra Bong.) is an ecologically significant and important fast-growing commercial tree species native to western coastal and riparian regions of North America, having highly desirable wood, pigment, and medicinal properties. We have sequenced the genome of a rapidly growing clone. The assembly is nearly complete, containing the full complement of expected genes. This supports our objectives of identifying and studying genes and pathways involved in nitrogen-fixing symbiosis and those related to secondary metabolites that underlie red alder's many interesting defense, pigmentation, and wood quality traits. We established that this clone is most likely diploid and identified a set of SNPs that will have utility in future breeding and selection endeavors, as well as in ongoing population studies. We have added a well-characterized genome to others from the order Fagales. In particular, it improves significantly upon the only other published alder genome sequence, that of Alnus glutinosa. Our work initiated a detailed comparative analysis of members of the order Fagales and established some similarities with previous reports in this clade, suggesting a biased retention of certain gene functions in the vestiges of an ancient genome duplication when compared with more recent tandem duplications.

DMU-Net: A Dual-Stream Multi-Scale U-Net Network Using Multi-Dimensional Spatial Information for Urban Building Extraction.

Automatically extracting urban buildings from remote sensing images has essential application value, such as urban planning and management. Gaofen-7 (GF-7) provides multi-perspective and multispectral satellite images, which can obtain three-dimensional spatial information. Previous studies on building extraction often ignored information outside the red-green-blue (RGB) bands. To utilize the multi-dimensional spatial information of GF-7, we propose a dual-stream multi-scale network (DMU-Net) for urban building extraction. DMU-Net is based on U-Net, and the encoder is designed as the dual-stream CNN structure, which inputs RGB images, near-infrared (NIR), and normalized digital surface model (nDSM) fusion images, respectively. In addition, the improved FPN (IFPN) structure is integrated into the decoder. It enables DMU-Net to fuse different band features and multi-scale features of images effectively. This new method is tested with the study area within the Fourth Ring Road in Beijing, and the conclusions are as follows: (1) Our network achieves an overall accuracy (OA) of 96.16% and an intersection-over-union (IoU) of 84.49% for the GF-7 self-annotated building dataset, outperforms other state-of-the-art (SOTA) models. (2) Three-dimensional information significantly improved the accuracy of building extraction. Compared with RGB and RGB + NIR, the IoU increased by 7.61% and 3.19% after using nDSM data, respectively. (3) DMU-Net is superior to SMU-Net, DU-Net, and IEU-Net. The IoU is improved by 0.74%, 0.55%, and 1.65%, respectively, indicating the superiority of the dual-stream CNN structure and the IFPN structure.

SPIDE: A purely spike-based method for training feedback spiking neural networks.

Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised SNN training methods, such as conversion from artificial neural networks or direct training with surrogate gradients, require complex computation rather than spike-based operations of spiking neurons during training. In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs. Specifically, we introduce ternary spiking neuron couples and prove that implicit differentiation can be solved by spikes based on this design, so the whole training procedure, including both forward and backward passes, is made as event-driven spike computation, and weights are updated locally with two-stage average firing rates. Then we propose to modify the reset membrane potential to reduce the approximation error of spikes. With these key components, we can train SNNs with flexible structures in a small number of time steps and with firing sparsity during training, and the theoretical estimation of energy costs demonstrates the potential for high efficiency. Meanwhile, experiments show that even with these constraints, our trained models can still achieve competitive results on MNIST, CIFAR-10, CIFAR-100, and CIFAR10-DVS.

Xiaoqinglong Decoction Enhances Autophagy to Antagonist Airway Inflammation Induced by Cold in Asthmatic Rats.

Asthma is a common chronic respiratory disease characterized by wheezing and shortness of breath. Its risk factors include genetic and acquired factors. The acquired factors are closely related to the environment, especially cold conditions. Autophagy plays a regulatory role in asthma. Therefore, we hypothesized that asthma can be controlled by drug intervention at the autophagy level under cold conditions. The Xiaoqinglong decoction (XQLT) was freeze-dried. The compounds in the freeze-dried powder were identified and quantified using reference standards via the high-performance liquid chromatography method. Ovalbumin (OVA)-sensitized rats were subjected to cold stimulation. The effect of cold stimulation on autophagy levels was determined, and it was confirmed that cold stimulation affected autophagy. The effects and mechanisms of XQLT in an asthmatic rat model (OVA-sensitized rats stimulated with cold) were explored. The concentrations of paeoniflorin, liquiritin, trans-cinnamic acid, glycyrrhizic acid, 6-gingerol, schisandrol A, and asarinin in XQLT freeze-dried powder were 14.45, 3.85, 1.03, 3.93, 0.59, 0.24, and 0.091 mg/g, respectively. Cold stimulation is an important cause of asthma. The inflammatory factors in bronchoalveolar lavage fluid and serum were increased in the model group, accompanied by a decline in autophagy level. The treatment with XQLT increased the expression of autophagy genes and decreased the expression of inflammatory factors. Histological studies showed that XQLT improved inflammatory infiltration and collagen fiber deposition in the lungs of rats. XQLT intervention increased autophagy in asthmatic rats. Autophagy plays a role in phagocytosis and reduces the accumulation of abnormal metabolites in the body to reduce airway inflammation and promote asthma recovery.

Training much deeper spiking neural networks with a small number of time-steps.

Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN conversion method, which is the most effective SNN training method, has successfully converted moderately deep ANNs to SNNs with satisfactory performance. However, this method requires a large number of time-steps, which hurts the energy efficiency of SNNs. How to effectively covert a very deep ANN (e.g., more than 100 layers) to an SNN with a small number of time-steps remains a difficult task. To tackle this challenge, this paper makes the first attempt to propose a novel error analysis framework that takes both the "quantization error" and the "deviation error" into account, which comes from the discretization of SNN dynamicsthe neuron's coding scheme and the inconstant input currents at intermediate layers, respectively. Particularly, our theories reveal that the "deviation error" depends on both the spike threshold and the input variance. Based on our theoretical analysis, we further propose the Threshold Tuning and Residual Block Restructuring (TTRBR) method that can convert very deep ANNs (>100 layers) to SNNs with negligible accuracy degradation while requiring only a small number of time-steps. With very deep networks, our TTRBR method achieves state-of-the-art (SOTA) performance on the CIFAR-10, CIFAR-100, and ImageNet classification tasks.

Almost perfect sequence modulated multiplexing ion mobility spectrometry.

RATIONALE: Multiplexing ion mobility spectrometry with multiple ion injection pulses was used to achieve a high duty cycle and thus improve the signal-to-noise (S/N) ratio while maintaining high resolving power compared with the traditional single-pulse signal averaging method. Historically, an ion mobility spectrum was reconstructed by various multiplexing methods including Fourier transform ion mobility spectrometry (FT-IMS), Hadamard transform ion mobility spectrometry (HT-IMS), and linear frequency modulation correlation ion mobility spectrometry (LFM-CIMS) sequence or Barker code. METHODS: To achieve an artifact-free multiplexing ion mobility spectrum, an almost perfect sequence (APS) with correlation technique was proposed to modulate the Bradbury-Nielson ion gate and was compared with FT-IMS, HT-IMS, LFM-IMS, and the traditional single-pulse signal averaging method. RESULTS: Experimental results showed that there are no artifact peaks in the APS-IMS spectra except an inverted mirror peak, and the S/N ratio was improved 5-8 times with a repetition time of 40-60 ms, corresponding to the improvement in the duty cycle. With the same duty cycle and similar acquisition time, APS-IMS showed a higher S/N ratio than HT-IMS for its unique autocorrelation response. CONCLUSIONS: The APS-IMS technique offered a higher duty cycle and relatively shorter modulation period compared with reported multiplexing methods and is suitable to track rapidly changing signals without losing information and adding extra transformation artifact peaks.

Landscape and vegetation traits of urban green space can predict local surface temperature.

Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient Boost Regression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region.

Improved anthropogenic heat flux model for fine spatiotemporal information in Southeast China.

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m-2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m-2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

Fourier Deconvolution Ion Mobility Spectrometry.

Multiplexing the ion packet injection with advanced signal processing is an effective method to improve both the ion throughput and signal-to-noise ratio for ion mobility spectrometry. Generally used multiplexing methods include Hadamard transform ion mobility spectrometry (HT-IMS), Fourier transform ion mobility spectrometry (FT-IMS), and correlation ion mobility spectrometry (C-IMS). However, HT-IMS sometimes suffer from false peaks and further processing is needed, FT-IMS generally requires longer spectra acquisition time than the traditional signal averaging method, and C-IMS also demonstrated drawbacks such as spectra baseline distortions when using traditional on-off binary gating switches. To improve the performance of multiplexing ion mobility spectrometry, this study investigates the Fourier deconvolution to increase the resolving power and signal-to-noise ratio at the same time. This approach modulates the ion gate with a linear square wave chirp sequence and synchronizes the data acquisition and ion gate modulation and then reconstructs the ion mobility spectra based on convolution theorems. The equivalent ion injection period is decreased to microseconds scale with the signal-to-noise ratio improved by up to 13 times on average, and the resolving power is improved by up to 50% compared with traditional signal averaging methods without hardware modifications.

Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China.

Industrial parks emit large amounts of anthropogenic heat and aggravate the urban heat island effect, which has become a severe environmental problem worldwide. Few studies explored if the warming effect generated by concentrated industrial facilities (i.e., steel plants in this study) produces an intra-heat island effect in urban built-up areas. Sufficient evidence of an industrial heat island (IHI) effect is lacking, and new quantitative methods are urgently needed to address these issues. Therefore, we proposed a new scheme to quantify the warming effect of large, heat-emitting urban objects versus complex surroundings, and the IHI effect was accordingly defined at a finer scale. This study separated the industrial park from other artificial lands and comprehensively estimated the IHI effects' spatiotemporal variation. The IHI intensities were measured based on varied natural and urbanized references, which provided new evidence for the existence of the IHI effect over space and seasons. The land surface temperature (LST) profiles delineated the downward trend in LST variation from inside to surroundings in the IHI cases on both spatial and temporal scales. The time-series analysis revealed that the IHI effects demonstrated more significant disparities regarding the LSTs between the industrial parks and their surrounding backgrounds during warm seasons than in cold seasons. And a more severe IHI effect was observed in spring and summer, and the weakest IHI intensity occurred in winter. Moreover, the IHI intensity is positively associated to the anthropogenic heat, indicating that the industrial activities contribute to the increased LSTs of the industrial park to a great extent. The rationale of the IHI effect can broaden insight for understanding how urban industrial heat sources influence the regional thermal environment, especially at a finer scale.

De novo sequencing and native mass spectrometry revealed hetero-association of dirigent protein homologs and potential interacting proteins in Forsythia × intermedia.

The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families, presumably with different functions. Some known DPs require other essential enzymes/proteins (e.g. oxidases) for their functions. However, the lack of a fully sequenced genome for Forsythia × intermedia made it difficult to profile other components co-purified with the (+)-pinoresinol forming DP. Herein, we used an integrated bottom-up, top-down, and native mass spectrometry (MS) approach to de novo sequence the extracted proteins via adaptation of our initial report of DP solubilization and purification. Using publicly available transcriptome and genomic data from closely related species, we identified 14 proteins that were putatively associated with either DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for potential protein-protein interactions, none were found to form stable protein complexes with DPs in native MS under the specific experimental conditions we have explored. Interestingly, two new DP homologs were found and they formed hetero-trimers. Molecular dynamics simulations suggested that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarities. Nevertheless, our integrated mass spectrometry method development helped prepare for future investigations directed to the discovery of novel proteins and protein-protein interactions. These advantages can be highly beneficial for plant and microbial research where fully sequenced genomes may not be readily available.

SIRT5-Related Desuccinylation Modification Contributes to Quercetin-Induced Protection against Heart Failure and High-Glucose-Prompted Cardiomyocytes Injured through Regulation of Mitochondrial Quality Surveillance.

Myocardial fibrosis represents the primary pathological change associated with diabetic cardiomyopathy and heart failure, and it leads to decreased myocardial compliance with impaired cardiac diastolic and systolic function. Quercetin, an active ingredient in various medicinal plants, exerts therapeutic effects against cardiovascular diseases. Here, we investigate whether SIRT5- and IDH2-related desuccinylation is involved in the underlying mechanism of myocardial fibrosis in heart failure while exploring related therapeutic drugs for mitochondrial quality surveillance. Mouse models of myocardial fibrosis and heart failure, established by transverse aortic constriction (TAC), were administered with quercetin (50 mg/kg) daily for 4 weeks. HL-1 cells were pretreated with quercetin and treated with high glucose (30 mM) in vitro. Cardiac function, western blotting, quantitative PCR, enzyme-linked immunosorbent assay, and immunofluorescence analysis were employed to analyze mitochondrial quality surveillance, oxidative stress, and inflammatory response in myocardial cells, whereas IDH2 succinylation levels were detected using immunoprecipitation. Myocardial fibrosis and heart failure incidence increased after TAC, with abnormal cardiac ejection function. Following high-glucose treatment, HL-1 cell activity was inhibited, causing excess production of reactive oxygen species and inhibition of mitochondrial respiratory complex I/III activity and mitochondrial antioxidant enzyme activity, as well as increased oxidative stress and inflammatory response, imbalanced mitochondrial quality surveillance and homeostasis, and increased apoptosis. Quercetin inhibited myocardial fibrosis and improved cardiac function by increasing mitochondrial energy metabolism and regulating mitochondrial fusion/fission and mitochondrial biosynthesis while inhibiting the inflammatory response and oxidative stress injury. Additionally, TAC inhibited SIRT5 expression at the mitochondrial level and increased IDH2 succinylation. However, quercetin promoted the desuccinylation of IDH2 by increasing SIRT5 expression. Moreover, treatment with si-SIRT5 abolished the protective effect of quercetin on cell viability. Hence, quercetin may promote the desuccinylation of IDH2 through SIRT5, maintain mitochondrial homeostasis, protect mouse cardiomyocytes under inflammatory conditions, and improve myocardial fibrosis, thereby reducing the incidence of heart failure.

Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data.

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

Effect of Baicalin on Transcriptome Changes in Piglet Vascular Endothelial Cells Induced by a Combination of Glaesserella parasuis and Lipopolysaccharide.

Glaesserella parasuis causes porcine Glässer's disease and lipopolysaccharide (LPS) induces acute inflammation and pathological damage. Baicalin has antioxidant, antimicrobial, and anti-inflammatory functions. Long noncoding RNAs (lncRNAs) play key regulatory functions during bacterial infection. However, the role of lncRNAs in the vascular dysfunction induced by a combination of G. parasuis and LPS during systemic inflammation and the effect of baicalin on lncRNA expression induced in porcine aortic vascular endothelial cells (PAVECs) by a combination of G. parasuis and LPS have not been investigated. In this study, we investigated the changes in lncRNA and mRNA expression induced in PAVECs by G. parasuis, LPS, or a combination of G. parasuis and LPS, and the action of baicalin on lncRNA expression induced in PAVECs by the combination of G. parasuis and LPS. Our results showed 133 lncRNAs and 602 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS, whereas 107 lncRNAs and 936 genes were differentially expressed when PAVECs were stimulated with the combination of G. parasuis and LPS after pretreatment with baicalin. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the dominant signaling pathways triggered by the combination of G. parasuis and LPS were the tumor necrosis factor signaling pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism. Protein-protein interaction network analysis showed the differentially expressed target genes of the differentially expressed lncRNAs (DELs) were related to each other. A coexpression analysis indicated the expression levels of the DELs were co-regulated with those of their differentially expressed target genes. This is the first study to systematically compare the changes in lncRNAs and mRNAs in PAVECs stimulated with a combination of G. parasuis and LPS. Our data clarified the mechanisms underlying the vascular inflammation and damage triggered by G. parasuis and LPS, and it may provide novel targets for the treatment of LPS-induced systemic inflammation.