Neuroprotective effect of acetoxypachydiol against oxidative stress through activation of the Keap1-Nrf2/HO-1 pathway.

BACKGROUND: Excessive oxidative stress in the brain is an important pathological factor in neurological diseases. Acetoxypachydiol (APHD) is a lipophilic germacrane-type diterpene extracted as a major component from different species of brown algae within the genus Dictyota. There have been no previous reports on the pharmacological activity of APHD. The present research aims to explore the potential neuroprotective properties of APHD and its underlying mechanisms. METHODS: The possible mechanism of APHD was predicted using a combination of molecular docking and network pharmacological analysis. PC12 cells were induced by H2O2 and oxygen-glucose deprivation/reoxygenation (OGD/R), respectively. Western blot, flow cytometry, immunofluorescence staining, and qRT-PCR were used to investigate the antioxidant activity of APHD. The HO-1 inhibitor ZnPP and Nrf2 gene silencing were employed to confirm the influence of APHD on the signaling cascade involving HO-1, Nrf2, and Keap1 in vitro. RESULTS: APHD exhibited antioxidant activity in both PC12 cells subjected to H2O2 and OGD/R conditions by downregulating the release of LDH, the concentrations of MDA, and ROS, and upregulating SOD, GSH-Px, and GSH concentrations. APHD could potentially initiate the Keap1-Nrf2/HO-1 signaling cascade, according to the findings from network pharmacology evaluation and molecular docking. Furthermore, APHD was observed to increase Nrf2 and HO-1 expression at both mRNA and protein levels, while downregulating the protein concentrations of Keap1. Both Nrf2 silencing and treatment with ZnPP reversed the neuroprotective effects of APHD. CONCLUSIONS: APHD activated antioxidant enzymes and downregulated the levels of LDH, MDA, and ROS in two cell models. The neuroprotective effect is presumably reliant on upregulation of the Keap1-Nrf2/HO-1 pathway. Taken together, APHD from brown algae of the genus Dictyota shows potential as a candidate for novel neuroprotective agents.

A New Lunar Lineament Extraction Method Based on Improved UNet++ and YOLOv5.

Lineament is a unique geological structure. The study of Lunar lineament structure has great significance on understanding its history and evolution of Lunar surface. However, the existing geographic feature extraction methods are not suitable for the extraction of Lunar lineament structure. In this paper, a new lineament extraction method is proposed based on improved-UNet++ and YOLOv5. Firstly, new lineament dataset is created containing lineaments structure based on CCD data from LROC. At same time the residual blocks are replaced with the VGG blocks in the down sample part of the UNet++ with adding the attention block between each layer. Secondly, the improved-UNet++ and YOLO networks are trained to execute the object detection and semantic segmentation of lineament structure respectively. Finally, a polygon-match strategy is proposed to combine the results of object detection and semantic segmentation. The experiment result indicate that this new method has relatively better and more stable performance compared with current mainstream networks and the original UNet++ network in the instance segmentation of lineament structure. Additionally, the polygon-match strategy is able to perform preciser edge detail in the instance segmentation of lineament structure result.

Carbazole and Quinazolinone Derivatives from a Fluoride-Tolerant Streptomyces Strain OUCMDZ-5511.

Six new 9H-carbazole derivatives (1-6) and nine previously reported compounds (7-15) were isolated from a fermented solid medium of the Thailand mangrove-derived Streptomyces strain, OUCMDZ-5511, under fluoride stress. Compounds 2-5, 12, and 15 were exclusively present in the fluoride-supplemented fermentation medium, while compounds 7-9, 13, and 14 were newly discovered natural products. The molecular structures of the compounds were identified by a spectroscopic analysis. The new compound 2 displayed antiquorum sensing activity against Chromobacterium violaceum ATCC 12472 by reducing the violacein production and inhibiting the biofilm formation in a concentration-dependent manner. The study revealed that compound 2 could be a novel potential inhibitor of quorum sensing.

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Supra-Normal Left Ventricular Ejection Fraction as a Prognostic Marker for Long-Term Outcomes in Patients with Acute Coronary Syndrome.

Recently, the supra-normal left ventricular ejection fraction (snLVEF) has been proposed, based on extensive datasets indicating increased all-cause mortality in individuals with an LVEF exceeding 65%. However, the implications of an LVEF > 65% in the context of acute coronary syndrome (ACS) remain underexplored.The aim of the present study was to investigate the correlation between supra-normal left ventricular ejection fraction (snLVEF) and major adverse cardiovascular events (MACE) in patients with ACS.Methods: A total of 874 ACS patients (560 men, mean age 59.5 ± 10.0; 314 women, mean age 61.5 ± 8.9) who underwent their first coronary angiography during the period from March 2013 to October 2015 were divided into 2 groups: normal LVEF (nLVEF) (55% ≤ EF ≤ 65%) and snLVEF (EF > 65%), according to their echocardiography results. The patients were evaluated for MACE after surgery by collecting clinical data and long-term follow-up data. This correlation was further analyzed by Kaplan-Meier analysis and Cox regression analysis.The follow-up data revealed a significantly higher incidence of MACE among snLVEF patients compared to the nLVEF group (15.6% versus 7.4%; P = 0.020). This heightened risk persisted even after adjustment for multiple variables, indicating a strong association between snLVEF and increased MACE risk (HR: 2.346; 95% CI: 1.196-4.602; P = 0.013).SnLVEF was independently associated with poor prognosis after ACS. Enhanced management strategies for snLVEF patients could potentially reduce the incidence of MACE in ACS patients.

Actomyosin and CSI1/POM2 cooperate to deliver cellulose synthase from Golgi to cortical microtubules in Arabidopsis.

As one of the major components of plant cell walls, cellulose is crucial for plant growth and development. Cellulose is synthesized by cellulose synthase (CesA) complexes (CSCs), which are trafficked and delivered from the Golgi apparatus to the plasma membrane. How CesAs are released from Golgi remains largely unclear. In this study, we observed that STELLO (STL) family proteins localized at a group of small CesA-containing compartments called Small CesA compartments (SmaCCs) or microtubule-associated CesA compartments (MASCs). The STL-labeled SmaCCs/MASCs were directly derived from Golgi through a membrane-stretching process: membrane-patches of Golgi attached to cortical microtubules, which led to emergence of membrane-tails that finally ruptured to generate SmaCCs/MASCs associated with the cortical microtubules. While myosin propelled the movement of Golgi along actin filaments to stretch the tails, the CesA-microtubule linker protein, CSI1/POM2 was indispensable for the tight anchor of the membrane-tail ends at cortical microtubules. Together, our data reveal a non-canonical delivery route to the plasma membrane of a major enzyme complex in plant biology.

[A new cinnamic acid ester derivative from Liquidambaris Resina].

Twelve compounds were isolated from Liquidambaris Resina by silica gel column chromatography and thin layer chromatography. Their structures were identified on the basis of spectral data, electron capture detector data, and physicochemical properties as(2'R, 3'R)-2',3'-dihydroxy-hydrocinnamyl-(E)-cinnamate(1),(E)-cinnamyl-(E)-cinnamate(2), cinnamic acid(3), 28-norlup-20(29)-en-3-one-17ß-hydroperoxide(4), erythrodiol(5), 13ß,28-epoxy-30-hydroxyolean-1-en-3-one(6),(3ß)-olean-12-ene-3,23-diol(7), 2α,3α-dihydroxy-olean-12-en-28-oic acid(8), 28-hydroxyolean-12-en-3-one(9), 3-epi-oleanolic acid(10), 3-oxo-oleanolic acid(11), and hederagenin(12). Compound 1 was a new cinnamic acid ester derivative and compounds 2-4,6-8, and 12 were isolated from Liquidambaris Resina for the first time. Compounds 4, 5, 10, and 12 exerted inhibitory effects on the proliferation of human umbilical vein endothelial cells(HUVEC) with the IC_(50) values of(17.43±2.17),(35.32±0.61),(27.50±0.80), and(46.30±0.30) µmol·L~(-1), respectively.

Adjacent Mn site boosts photocatalytic hydrogen evolution of MnXCd1-XS solid solution through a dual-metal-site design.

CdS has emerged as a possible candidate for photocatalytic hydrogen generation. However, further improvement in the performance of the Cd metal site is challenging due to limited optimization space. To solve this limitation, in this work, the Mn-Cd dual-metal photocatalyst was synthesized by a one-step solvothermal method, and the effects of different proportions of bimetals on hydrogen production activity were systematically studied. The ingenious design of the bimetallic sites enhances the carrier separation efficiency and the built-in electric field intensity, which leads to significant improvement in the photocatalytic hydrogen production performance of MCS0.19. Density functional theory (DFT) calculations confirm that the introduction of the Mn element can drive electrons through the Fermi level, resulting in enhanced conductivity of the catalyst. Meanwhile, electron channels are built between Mn and S, which speeds up the rate of electron transfer and is conducive to improving hydrogen production activity. This work provides a technical-methodological entrance to improve the photocatalytic hydrogen production performance of dual-metal S solid solutions and also promises to open a novel approach to creating high-efficiency solid solution photocatalysts.

Research on transformer fault diagnosis based on an IWHO optimized MS1DCNN algorithm and LIF spectrum.

Accurate diagnosis of transformer faults can effectively improve the enduring reliability of power grid operation. Aiming at overcoming the problems of long time consumption and low diagnostic rate in the past diagnosis methods, this article designs a laser-induced fluorescence (LIF) detection system, which can be combined with a multi-scale one-dimensional convolution neural network (MS1DCNN) to diagnose transformer fault categories. The structural parameters of MS1DCNN are optimized using the improved wild horse optimizer (IWHO). Electrical fault oil, thermal fault oil, normal oil and locally damped oil are used as raw materials for the experiment. First, the LIF spectral data of the four kinds of oil samples are obtained, and the spectral data obtained are pretreated by standard normal variate (SNV) and multiple scattering correction (MSC), and the dimensions are reduced by linear discriminant analysis (LDA) and kernel principal component analysis (KPCA). Then the dimensionality reduced data are imported into the MS1DCNN algorithm for learning, and the parameters of MS1DCNN are optimized using the IWHO algorithm. Finally, the experiment shows that the efficiency and precision of LIF technology for raw data extraction are higher than for traditional methods; in comparison with the same type of algorithm, MSC has a better preprocessing effect, KPCA has a better dimensionality reduction effect, MS1DCNN has a better prediction effect, and IWHO has a better optimization effect. Compared to them, the MSC-KPCA-IWHO-MS1DCNN model has the best diagnostic ability, with a mean square error (MSE) of 4.9037 × 10-4, mean absolute error (MAE) of 0.0179, and goodness of fit (R2) of 0.9996. Transformer fault intelligent diagnosis is necessary for the sustained and stable operation of power networks.

Target Recognition of Coal and Gangue Based on Improved YOLOv5s and Spectral Technology.

Aiming at the problems of long detection time and low detection accuracy in the existing coal gangue recognition, this paper proposes a method to collect the multispectral images of coal gangue using spectral technology and match with the improved YOLOv5s (You Only Look Once Version-5s) neural network model to apply it to coal gangue target recognition and detection, which can effectively reduce the detection time and improve the detection accuracy and recognition effect of coal gangue. In order to take the coverage area, center point distance and aspect ratio into account at the same time, the improved YOLOv5s neural network replaces the original GIou Loss loss function with CIou Loss loss function. At the same time, DIou NMS replaces the original NMS, which can effectively detect overlapping targets and small targets. In the experiment, 490 sets of multispectral data were obtained through the multispectral data acquisition system. Using the random forest algorithm and the correlation analysis of bands, the spectral images of the sixth, twelfth and eighteenth bands from twenty-five bands were selected to form a pseudo RGB image. A total of 974 original sample images of coal and gangue were obtained. Through two image noise reduction methods, namely, Gaussian filtering algorithm and non-local average noise reduction, 1948 images of coal gangue were obtained after preprocessing the dataset. This was divided into a training set and test set according to an 8:2 ratio and trained in the original YOLOv5s neural network, improved YOLOv5s neural network and SSD neural network. By identifying and detecting the three neural network models obtained after training, the results can be obtained, the loss value of the improved YOLOv5s neural network model is smaller than the original YOLOv5s neural network and SSD neural network, the recall rate is closer to 1 than the original YOLOv5s neural network and SSD neural network, the detection time is the shortest, the recall rate is 100% and the average detection accuracy of coal and gangue is the highest. The average precision of the training set is increased to 0.995, which shows that the improved YOLOv5s neural network has a better effect on the detection and recognition of coal gangue. The detection accuracy of the improved YOLOv5s neural network model test set is increased from 0.73 to 0.98, and all overlapping targets can also be accurately detected without false detection or missed detection. At the same time, the size of the improved YOLOv5s neural network model after training is reduced by 0.8 MB, which is conducive to hardware transplantation.

Mechanical regulation of cortical microtubules in plant cells.

All living organisms are subjected to mechanical forces at all times. It has been reported that mechanics regulate many key cellular processes, including cell polarity establishment, cell division and gene expression, as a physical signal in both animal and plant development. Plant cells are exposed to several types of mechanical stresses, ranging from turgor-driven tensile stresses, mechanical force modified by heterogeneous growth directions and rates between neighbouring cells, to forces from the environment such as wind and rain, for which they have developed adaptive mechanisms. Increasing evidence has revealed that mechanical stresses markedly influence the alignment of cortical microtubules (CMTs) in plant cells, among other effects. CMTs are able to reorient in response to mechanical stresses at both the single-cell and tissue levels and always align with the maximal tensile stress direction. In this review, we discussed the known and potential molecules and pathways involved in the regulation of CMTs by mechanical stresses. We also summarized the available techniques that have allowed for mechanical perturbation. Finally, we highlighted several key questions remaining to be addressed in this emerging field.

Anti-Neuroinflammatory Meroterpenoids from a Chinese Collection of the Brown Alga Sargassum siliquastrum.

Nine new chromane-type meroterpenoids, including the rare nor-meroterpenoid sargasilol A (1) and the eight meroditerpenoids sargasilols B-I (2-9), were isolated from a China Sea collection of the brown alga Sargassum siliquastrum, together with six known analogues (10-15). The structures of the new chromanes were identified by extensive spectroscopic analysis and by comparison with previously reported data. Compounds 1-3 and 6-15 exhibited inhibition against LPS-induced NO production in BV-2 microglial cells, and 1, with a shorter carbon chain, was the most active one. Compound 1 was established as an anti-neuroinflammatory agent through targeting the IKK/IκB/NF-κB signaling pathway. As such, the chromanes from brown algae could provide promising anti-neuroinflammatory lead compounds for further structural modification.

Paraconulones A-G: Eremophilane Sesquiterpenoids from the Marine-Derived Fungus Paraconiothyrium sporulosum DL-16.

Seven new eremophilane sesquiterpenoids, paraconulones A-G (1-7), along with three previously reported analogues, periconianone D (8), microsphaeropsisin (9), and 4-epi-microsphaeropsisin (10), were obtained from an EtOAc extract of the marine-derived fungus Paraconiothyrium sporulosum DL-16. The structures of these compounds were elucidated by extensive spectroscopic and spectrometric analyses, single-crystal X-ray diffraction, and computational studies. Compounds 1, 2, and 4 are the first examples of dimeric eremophilane sesquiterpenoids coupled through a C-C bond identified from microorganisms. Compounds 2-5, 7, and 10 showed inhibitory effects on lipopolysaccharide-induced NO production in BV2 cells, which were comparable to the positive control curcumin.

Electronic and energy level structural engineering of graphitic carbon nitride nanotubes with B and S co-doping for photocatalytic hydrogen evolution.

The ideal photocatalyst used for photocatalytic water splitting requires strong light absorption, fast charge separation/transfer ability and abundant active sites. Heteroatom doping offers a promising and rational approach to optimize the photocatalytic activity. However, achieving high photocatalytic performance remains challenging if just relying on single-element doping. Herein, Boron (B) and sulfur (S) dopants are simultaneously introduced into graphitic carbon nitride (g-C3N4) nanotubes by supramolecular self-assembly strategy. The developed B and S co-doped g-C3N4 nanotubes (B,S-TCN) exhibited an outstanding photocatalytic performance in the conversion of H2O into H2 (9.321 mmol g-1h-1), and the corresponding external quantum efficiency (EQE) reached 5.3% under the irradiation of λ = 420 nm. It is well evidenced by the closely combined experimental and (density functional theory) DFT calculations: (1) the introduction of B dopants can facilitate H2O adsorption and drive interatomic electron transfer, leading to efficient water splitting reaction. (2) S dopants can stretch the VB position to promote the oxidation ability of g-C3N4, which can accelerate the consumption of holes and thus inhibit the recombination with electrons. (3) the simultaneous introduction of B and S can engineer the electronic and energy level structural of g-C3N4 for optimizing interior charge transfer. Finally, the purpose of maximizing photocatalytic performance is achieved.

Antioxidant and Neuroprotective Xenicane Diterpenes from the Brown Alga Dictyota coriacea.

Five new xenicane diterpenes, including three rare nitrogen-containing derivatives, dictyolactams A (1) and B (2) and 9-demethoxy-9-ethoxyjoalin (3), a rare diterpene with a cyclobutanone moiety, named 4-hydroxyisoacetylcoriacenone (4), and 19-O-acetyldictyodiol (5), were isolated from an East China Sea collection of the brown alga Dictyota coriacea, along with 15 known analogues (6-20). The structures of the new diterpenes were elucidated by spectroscopic analyses and theoretical ECD calculations. All compounds had cytoprotective effects against oxidative stress in neuron-like PC12 cells. The antioxidant mechanism of 18-acetoxy-6,7-epoxy-4-hydroxydictyo-19-al (6) was related to the activation of Nrf2/ARE signaling pathway; it also showed significant neuroprotective effects against cerebral ischemia-reperfusion injury (CIRI) in vivo. This study provided xenicane diterpene as a promising lead scaffold for the development of potent neuroprotective agents against CIRI.

Transformer fault diagnosis research based on LIF technology and IAO optimization of LightGBM.

Transformer fault diagnosis is a necessary operation to ensure the stable operation of a power system. In view of the problems of the low diagnostic rate and long time needed in traditional methods, such as the dissolved gas in oil method, a laser-induced fluorescence (LIF) spectral technology is proposed in this paper, which incorporated an improved aquila optimizer (IAO) and light gradient boosting machine (LightGBM), to predict the types of transformer faults. The original AO was improved using the Nelder Mead (NM) simple search method and opposition-based learning (OBL) mechanism, which could improve the parameter optimization ability of the model. Normal oil, thermal fault oil, local moisture oil, and electrical fault oil were selected as experimental samples. First, the spectral images of the four oil samples were obtained by LIF technology, and the fluorescence spectral curves obtained were preprocessed by multivariate scattering correction (MSC) and normalization (normalize), while kernel-based principle component analysis (KPCA) was used for dimensional reduction. The dimensionality-reduced data were then imported into the LightGBM model for training, and the IAO algorithm was used to optimize the parameters of the LightGBM. Finally, the experiment showed that the LIF technology demonstrated good recognition of the fault types for transformer fault diagnosis; the data purity after MSC preprocessing was higher than that of other processing methods; the prediction effect of the LightGBM model was superior to other prediction models; the LightGBM model optimized by IAO had better convergence, parameter optimization ability, and prediction accuracy than the LightGBM model optimized by the original AO and particle swarm optimization (PSO). Among the models, the MSC-IAO-LightGBM model had the best effect on fault prediction, with the mean square error (MSE) reaching 9.0643 × 10-7, mean absolute error (MAE) reaching 8.7439 × 10-4, and goodness of fit (R2) approaching 1. It can be implemented as a new diagnostic method in transformer fault detection, which is of great significance to ensure the stable and safe operation of power systems.

Engineering surface bromination in carbon nitride for efficient CO2 photoconversion to CH4.

The direct conversion of CO2 into reusable CH4 fuel by solar energy can effectively solve the problems of energy crisis and carbon emissions. However, the challenge of photocatalytic CO2 reduction to produce CH4 is still low conversion efficiency and poor selectivity. Here, surface brominated carbon nitride (named CNBr) is fabricated for stable and efficient photocatalytic CO2 reduction to produce CH4 with a rate of 16.68 µmol h-1 g-1 (70.27 % selectivity). Br atom in CNBr can substitute the N atom in the tri-s-triazine unites, which promotes local charge separation, narrows band gap and deepens the conduction band of CNBr. Benefiting from Br as active sites, CO2 can be enriched on the catalyst surface, and localized photogenerated electrons can activate the adsorbed CO2 to form CH4 through subsequent hydrogenation. Density functional theory results suggest that Br doping can effectively reduce the energy barrier of the rate-limiting step, accelerate the reaction, and induce the formation of *CHO, thereby improving the selectivity of CH4. This work reveals that surface modification can simultaneously increase the activation site of CO2 adsorption activation, enhance light absorption and accelerate charge, laying a solid foundation for the future design of carbon nitride based photocatalyst with high performance.

Dapagliflozin Alleviates Coxsackievirus B3-induced Acute Viral Myocarditis by Regulating the Macrophage Polarization Through Stat3-related Pathways.

Viral myocarditis (VMC), which is most prevalently caused by Coxsackievirus B3 (CVB3) infection, is a serious clinical condition characterized by cardiac inflammation. Dapagliflozin, a kind of sodium glucose co-transporters 2(SGLT-2) inhibitor, exhibited protective effects on plenty of inflammatory diseases, while its effect on viral myocarditis has not been studied. Recently, we found the protective effect of dapagliflozin on VMC. After CVB3 infection, dapagliflozin and STATTIC (a kind of stat3 inhibitor) were given to Balb/c male mice for 8 days, and then the severity of myocarditis was assessed. Our results indicated that dapagliflozin significantly alleviated the severity of viral myocarditis, elevated the survival rate, and ameliorated cardiac function. Besides, dapagliflozin can decrease the level of pro-inflammatory cytokines including IL-1ß, IL-6, and TNF-α. Furthermore, dapagliflozin can inhibit macrophages differentiate to classically activated macrophages (M1) in cardiac tissue and activate the Stat3 signal pathway which is reported to promote polarization of the alternatively activated macrophage (M2). And STATTIC can reverse these changes caused by dapagliflozin. In conclusion, we found that dapagliflozin treatment increased anti-inflammatory macrophage polarization and reduced cardiac injury following VMC via activating Stat3 signal pathway.

A sensitive photoelectrochemical aptasensor for enrofloxacin detection based on plasmon-sensitized bismuth-rich bismuth oxyhalide.

Highly sensitive detection of enrofloxacin (ENR) is crucial for contaminant detection and environmental protection. A sensitive and selective photoelectrochemical (PEC) aptasensor was assembled by Au nanoparticles sensitized Bi24O31Br10 (Au/Bi24O31Br10) composites for detecting ENR. Due to the synergistic effect of bismuth-rich strategy and surface plasmon resonance (SPR) effect, Au/Bi24O31Br10 possessed promoted visible light absorption capacity further enhancing PEC performance and detection sensitivity of the constructed PEC aptasensor. By chemically adsorption effect between the sulfhydryl modified aptamer and Au nanoparticles, the ENR-aptamer was introduced into the PEC sensor to achieve highly selective detection of ENR. The PEC ENR aptasensor based on Au/Bi24O31Br10 composites possessed a broad linear detection scope (0.72-36000 ng L-1), satisfactory limit of detection (0.30 ng L-1, S/N = 3), high selectivity and stability. This work provides a new way for the trace detection of antibiotics in environmental analysis field.

Cytosolic and Nucleosolic Calcium-Regulated Molecular Networks in Response to Long-Term Treatment with Abscisic Acid and Methyl Jasmonate in Arabidopsis thaliana.

Calcium acts as a universal secondary messenger that transfers developmental cues and stress signals for gene expression and adaptive growth. A prior study showed that abiotic stresses induce mutually independent cytosolic Ca2+ ([Ca2+]cyt) and nucleosolic Ca2+ ([Ca2+]nuc) increases in Arabidopsis thaliana root cells. However, gene expression networks deciphering [Ca2+]cyt and [Ca2+]nuc signalling pathways remain elusive. Here, using transgenic A. thaliana to selectively impair abscisic acid (ABA)- or methyl jasmonate (MeJA)-induced [Ca2+]cyt and [Ca2+]nuc increases, we identified [Ca2+]cyt- and [Ca2+]nuc-regulated ABA- or MeJA-responsive genes with a genome oligo-array. Gene co-expression network analysis revealed four Ca2+ signal-decoding genes, CAM1, CIPK8, GAD1, and CPN20, as hub genes co-expressed with Ca2+-regulated hormone-responsive genes and hormone signalling genes. Luciferase complementation imaging assays showed interactions among CAM1, CIPK8, and GAD1; they also showed interactions with several proteins encoded by Ca2+-regulated hormone-responsive genes. Furthermore, CAM1 and CIPK8 were required for MeJA-induced stomatal closure; they were associated with ABA-inhibited seed germination. Quantitative reverse transcription polymerase chain reaction analysis showed the unique expression pattern of [Ca2+]-regulated hormone-responsive genes in cam1, cipk8, and gad1. This comprehensive understanding of distinct Ca2+ and hormonal signalling will allow the application of approaches to uncover novel molecular foundations for responses to developmental and stress signals in plants.