Dietary Supplementation with Nervonic Acid Ameliorates Cerebral Ischemia-Reperfusion Injury by Modulating of Gut Microbiota Composition-Fecal Metabolites Interaction.

SCOPE: Cerebral ischemia-reperfusion (IR) injury stands as a prominent global contributor to disability and mortality. Nervonic acid (NA), a bioactive elongated monounsaturated fatty acid, holds pivotal significance in human physiological well-being. This research aims to explore the prophylactic effects and fundamental mechanisms of NA in a rat model of cerebral IR injury. METHODS AND RESULTS: Through the induction of middle cerebral artery occlusion, this study establishes a rat model of cerebral IR injury and comprehensively assesses the pharmacodynamic impacts of NA pretreatment. This evaluation involves behavioral analyses, histopathological examinations, and quantification of serum markers. Detailed mechanisms of nervonic acid's prophylactic effects are revealed through fecal metabolomics and 16S rRNA sequencing analyses. Our findings robustly support nervonic acid's capacity to ameliorate neurological impairments in rats afflicted with cerebral IR injury. Beyond its neurological benefits, NA demonstrates its potential by rectifying metabolic perturbations across diverse pathways, particularly those pertinent to unsaturated fatty acid metabolism. Additionally, NA emerges as a modulator of gut microbiota composition, notably by selectively enhancing vital genera like Lactobacillus. CONCLUSION: These comprehensive findings highlight the potential of incorporating NA as a functional component in dietary interventions aimed at targeting cerebral IR injury.

The violent collapse of vapor bubbles in cryogenic liquids.

Vapor bubbles in cryogenic fluids may collapse violently under subcooled and pressurized conditions. Despite important implications for engineering applications such as cavitation erosion in liquid propellant rocket engines, these intense phenomena are still largely unexplored. In this paper, we systematically investigate the ambient conditions leading to the occurrence of violent collapses in liquid nitrogen and analyze their thermodynamic characteristics. Using Brenner's time ratio χ, the regime of violent collapse is identified in the ambient pressure-temperature parameter space. Complete numerical simulations further refine the prediction and illustrate two classes of collapses. At 1 < χ < 10, the collapse is impacted by significant thermal effects and attains only moderate wall velocity. Only when χ > 10 does the collapse show more inertial features. A mechanism analysis pinpoints a critical time when the surrounding liquid enters supercritical state. The ultimate collapse intensity is shown to be closely associated with the dynamics at this moment. Our study provides a fresh perspective to the treatment of cavitation in cryogenic fluids. The findings can be instrumental in engineering design to mitigate adverse effects arising from intense cavitational activities.

6-Gingerol regulates triglyceride and cholesterol biosynthesis to improve hepatic steatosis in MAFLD by activating the AMPK-SREBPs signaling pathway.

Excessive synthesis of triglycerides and cholesterol accelerates the progression of hepatic steatosis in metabolic-associated fatty liver disease (MAFLD). However, the precise mechanism by which 6-gingerol mitigates hepatic steatosis in MAFLD model mice has yet to be fully understood. The present study observed that 6-gingerol administration exhibited significant protective effects against obesity, insulin resistance, and hepatic steatosis in mice subjected to a high-fat diet (HFD), and mitigated lipid accumulation in HepG2 cells treated with palmitate (PA). Following the hepatic lipidomic analysis, we confirmed that the AMPK-SREBPs signaling pathway as the underlying molecular mechanism by which 6-gingerol inhibited triglyceride and cholesterol biosynthesis, both in vivo and in vitro, through Western blot and immunofluorescence assay. Additionally, the application of an AMPK agonist/inhibitor further validated that 6-gingerol promoted AMPK activation by increasing the phosphorylation level of AMPK in vitro. Notably, the inhibitory effect of 6-gingerol on cholesterol biosynthesis, rather than triglyceride biosynthesis, was significantly diminished after silencing SREBP2 using a lentiviral plasmid shRNA in HepG2 cells. Our study demonstrates that 6-gingerol mitigates hepatic triglyceride and cholesterol biosynthesis to alleviate hepatic steatosis by activating the AMPK-SREBPs signaling pathway, indicating that 6-gingerol may be a potential candidate in the therapy of MAFLD.

An Image Detection Model for Aggressive Behavior of Group Sheep.

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.

Spatio-Temporal-Based Identification of Aggressive Behavior in Group Sheep.

In order to solve the problems of low efficiency and subjectivity of manual observation in the process of group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior in group sheep. In the experiment, we collected videos of the sheep's daily routine and videos of the aggressive behavior of sheep in the sheep pen. Using the open-source software LabelImg, we labeled the data with bounding boxes. Firstly, the YOLOv5 detects all sheep in each frame of the video and outputs the coordinates information. Secondly, we sort the sheep's coordinates using a sheep tracking heuristic proposed in this paper. Finally, the sorted data are fed into an LSTM framework to predict the occurrence of aggression. To optimize the model's parameters, we analyze the confidence, batch size and skipping frame. The best-performing model from our experiments has 93.38% Precision and 91.86% Recall. Additionally, we compare our video streaming-based model with image-based models for detecting aggression in group sheep. In sheep aggression, the video stream detection model can solve the false detection phenomenon caused by head impact feature occlusion of aggressive sheep in the image detection model.

A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model.

In large-scale meat sheep farming, high CO2 concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO2 concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO2 concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO2 mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 µg·m-3, a mean absolute error (MAE) of 51.839 µg·m-3, and a coefficient of determination (R2) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO2 concentration in sheep barns in large-scale meat sheep farming.

Co-regulatory effects of hormone and mRNA-miRNA module on flower bud formation of Camellia oleifera.

Few flower buds in a high-yield year are the main factors restricting the yield of Camellia oleifera in the next year. However, there are no relevant reports on the regulation mechanism of flower bud formation. In this study, hormones, mRNAs, and miRNAs were tested during flower bud formation in MY3 ("Min Yu 3," with stable yield in different years) and QY2 ("Qian Yu 2," with less flower bud formation in a high-yield year) cultivars. The results showed that except for IAA, the hormone contents of GA3, ABA, tZ, JA, and SA in the buds were higher than those in the fruit, and the contents of all hormones in the buds were higher than those in the adjacent tissues. This excluded the effect of hormones produced from the fruit on flower bud formation. The difference in hormones showed that 21-30 April was the critical period for flower bud formation in C. oleifera; the JA content in MY3 was higher than that in QY2, but a lower concentration of GA3 contributed to the formation of the C. oleifera flower bud. JA and GA3 might have different effects on flower bud formation. Comprehensive analysis of the RNA-seq data showed that differentially expressed genes were notably enriched in hormone signal transduction and the circadian system. Flower bud formation in MY3 was induced through the plant hormone receptor TIR1 (transport inhibitor response 1) of the IAA signaling pathway, the miR535-GID1c module of the GA signaling pathway, and the miR395-JAZ module of the JA signaling pathway. In addition, the expression of core clock components GI (GIGANTEA) and CO (CONSTANS) in MY3 increased 2.3-fold and 1.8-fold over that in QY2, respectively, indicating that the circadian system also played a role in promoting flower bud formation in MY3. Finally, the hormone signaling pathway and circadian system transmitted flowering signals to the floral meristem characteristic genes LFY (LEAFY) and AP1 (APETALA 1) via FT (FLOWERING LOCUS T) and SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CO 1) to regulate flower bud formation. These data will provide the basis for understanding the mechanism of flower bud alternate formation and formulating high yield regulation measures for C. oleifera.

Dietary supplementation of Acanthopanax senticosus extract alleviates motor deficits in MPTP-induced Parkinson's disease mice and its underlying mechanism.

Acanthopanax senticosus extract (ASE), a dietary supplement with antifatigue, neuroprotective, and immunomodulatory properties, has been widely used due to its high polyphenol content. Our previous study showed that ASE could be used to treat Parkinson's disease (PD) as it contains multiple monoamine oxidase B inhibitors prescribed in early PD. However, its mechanism remains ambiguous. In this study, we investigated the protective effects of ASE on MPTP-induced PD in mice and explored the underlying mechanisms of action. We found that the administration of ASE significantly improved motor coordination in mice with MPTP-induced PD. As shown by quantitative proteomic analysis, 128 proteins' expression significantly changed in response to ASE administration, most of which were involved with Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway, PI3K/AKT signaling pathway, and insulin receptor signaling pathway. Furthermore, the network analysis results showed that ASE modulates protein networks involved in regulating cellular assembly, lipid metabolism, and morphogenesis, all of which have implications for treating PD. Overall, ASE served as a potential therapeutic because it regulated multiple targets to improve motor deficits, which could lay the strong foundation for developing anti-PD dietary supplements.

Yellowhorn Xso-miR5149-XsGTL1 enhances water-use efficiency and drought tolerance by regulating leaf morphology and stomatal density.

Yellowhorn (Xanthoceras sorbifolium) is a unique edible woody oil tree species in China. Drought stress is the major yield-limiting factor of yellowhorn. MicroRNAs play an important role in regulating the response of woody plants to drought stress. However, the regulatory function of miRNAs in yellowhorn remains unclear. Here, we first constructed coregulatory networks integrated with miRNAs and their target genes. According to GO function and expression pattern analysis, we selected the Xso-miR5149-XsGTL1 module for further study. Xso-miR5149 is a key regulator of leaf morphology and stomatal density by directly mediating the expression of the transcription factor XsGTL1. Downregulation of XsGTL1 in yellowhorn led to increased leaf area and reduced stomatal density. RNA-seq analysis indicated that downregulation of XsGTL1 increased the expression of genes involved in the negative control of stomatal density, leaf morphology, and drought tolerance. After drought stress treatments, the XsGTL1-RNAi yellowhorn plants were less damaged and had higher water-use efficiency than the WT plants, while destruction of Xso-miR5149 or overexpression of XsGTL1 had the opposite effect. Our findings indicated that the Xso-miR5149-XsGTL1 regulatory module plays a critical role in controlling leaf morphology and stomatal density; hence, it's a potential candidate module for engineering enhanced drought tolerance in yellowhorn.

Identification of Camellia oleifera WRKY transcription factor genes and functional characterization of CoWRKY78.

Camellia oleifera Abel is a highly valued woody edible oil tree, which is endemic to China. It has great economic value because C. oleifera seed oil contains a high proportion of polyunsaturated fatty acids. C. oleifera anthracnose caused by Colletotrichum fructicola, poses a serious threat to C. oleifera growth and yield and causes the benefit of the C. oleifera industry to suffer directly. The WRKY transcription factor family members have been widely characterized as vital regulators in plant response to pathogen infection. Until now, the number, type and biological function of C. oleifera WRKY genes are remains unknown. Here, we identified 90 C. oleifera WRKY members, which were distributed across 15 chromosomes. C. oleifera WRKY gene expansion was mainly attributed to segmental duplication. We performed transcriptomic analyses to verify the expression patterns of CoWRKYs between anthracnose-resistant and -susceptible cultivars of C. oleifera. These results demonstrated that multiple candidate CoWRKYs can be induced by anthracnose and provide useful clues for their functional studies. CoWRKY78, an anthracnose-induced WRKY gene, was isolated from C. oleifera. It was significantly down-regulated in anthracnose-resistant cultivars. Overexpression of CoWRKY78 in tobacco markedly reduced resistance to anthracnose than WT plants, as evidenced by more cell death, higher malonaldehyde content and reactive oxygen species (ROS), but lower activities of superoxide dismutase (SOD), peroxidase (POD), as well as phenylalanine ammonia-lyase (PAL). Furthermore, the expression of multiple stress-related genes, which are associated with ROS-homeostasis (NtSOD and NtPOD), pathogen challenge (NtPAL), and pathogen defense (NtPR1, NtNPR1, and NtPDF1.2) were altered in the CoWRKY78-overexpressing plants. These findings increase our understanding of the CoWRKY genes and lay the foundation for the exploration of anthracnose resistance mechanisms and expedite the breeding of anthracnose-resistant C. oleifera cultivars.

A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network.

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

A Metallic Niobium Nitride with Open Nanocavities for Surface-Enhanced Raman Spectroscopy.

The construction of open hot-spot structures that facilitate the entry of analytes is crucial for surface-enhanced Raman spectroscopy. Here, metallic niobium nitride (NbN) three-dimensional (3D) hierarchical networks with open nanocavity structure are first found to exhibit a strong visible-light localized surface plasmon resonance (LSPR) effect and extraordinary surface-enhanced Raman scattering (SERS) performance. The unique nanocavity structure allows easy entry of molecules, promoting the utilization of electromagnetic hot spots. The NbN substrate has a lowest detection limit of 1.0 × 10-12 M and a Raman enhancement factor (EF) of 1.4 × 108 for contaminants. Furthermore, the NbN hierarchical networks possess outstanding environmental durability, high signal reproducibility, and detection universality. The remarkable SERS sensitivity of the NbN substrate can be attributed to the joint effect of LSPR and interfacial charge transport (CT).

Prediction of Liquid Magnetization Series Data in Agriculture Based on Enhanced CGAN.

The magnetized water and fertilizer liquid can produce biological effect of magnetic field on crops, but its residual magnetic field strength is difficult to be expressed quantitatively in real time, and accurate prediction of it is helpful to define the scope of action of liquid magnetization. In this paper, a prediction model for liquid magnetization series data is presented. It consists of conditional generative adversarial network (CGAN) and projected gradient descent (PGD) algorithm. First, the real training dataset is used as the input of PGD attack algorithm to generate antagonistic samples. These samples are added to the training of CGAN as true samples for data enhancement. Second, the training dataset is used as both the generator and discriminator input of CGAN to constrain the model, capture distribution of the real data. Third, a network model with three layers of CNN is built and trained inside CGAN. The input model is constructed by using the structure of two-dimensional convolution model to predict data. Lastly, the performance of the model is evaluated by the error between the final generated predicted value and the real value, and the model is compared with other prediction models. The experimental results show that, with limited data samples, by combining PGD attack with CGAN, the distribution of the real data can be more accurately captured and the data can be generated to meet the actual needs.

Determination of the Soluble Solids Content in Korla Fragrant Pears Based on Visible and Near-Infrared Spectroscopy Combined With Model Analysis and Variable Selection.

The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the application of portable devices is more common. The construction of an accurate and stable prediction model is the key for the successful application of the device. In this study, the visible and near-infrared (Vis/NIR) spectra of Korla fragrant pears were collected by a commercial portable measurement device. Different pretreatment methods were used to preprocess the raw spectra, and the partial least squares (PLS) model was constructed to predict the SSC of pears for the determination of the appropriate pretreatment method. Subsequently, PLS and least squares support vector machine (LS-SVM) models were constructed based on the preprocessed full spectra. A new combination (BOSS-SPA) of bootstrapping soft shrinkage (BOSS) and successive projections algorithm (SPA) was used for variable selection. For comparison, single BOSS and SPA were also used for variable selection. Finally, three types of models, namely, PLS, LS-SVM, and multiple linear regression (MLR), were constructed based on different input variables. Comparing the prediction performance of all models, it showed that the BOSS-SPA-PLS model based on 17 variables obtained the best SSC assessment ability with r p of 0.94 and RMSEP of 0.27 °Brix. The overall result indicated that portable measurement with Vis/NIR spectroscopy can be used for the detection of SSC in Korla fragrant pears.

Source-To-Sink Transport of Sugar and Its Role in Male Reproductive Development.

Sucrose is produced in leaf mesophyll cells via photosynthesis and exported to non-photosynthetic sink tissues through the phloem. The molecular basis of source-to-sink long-distance transport in cereal crop plants is of importance due to its direct influence on grain yield-pollen grains, essential for male fertility, are filled with sugary starch, and rely on long-distance sugar transport from source leaves. Here, we overview sugar partitioning via phloem transport in rice, especially where relevant for male reproductive development. Phloem loading and unloading in source leaves and sink tissues uses a combination of the symplastic, apoplastic, and/or polymer trapping pathways. The symplastic and polymer trapping pathways are passive processes, correlated with source activity and sugar gradients. In contrast, apoplastic phloem loading/unloading involves active processes and several proteins, including SUcrose Transporters (SUTs), Sugars Will Eventually be Exported Transporters (SWEETs), Invertases (INVs), and MonoSaccharide Transporters (MSTs). Numerous transcription factors combine to create a complex network, such as DNA binding with One Finger 11 (DOF11), Carbon Starved Anther (CSA), and CSA2, which regulates sugar metabolism in normal male reproductive development and in response to changes in environmental signals, such as photoperiod.

The regulatory role of CARBON STARVED ANTHER-mediated photoperiod-dependent male fertility in rice.

Environmental signals, especially daylength, play important roles in determining fertility in photoperiod-sensitive genic male sterile (PGMS) lines that are critical to sustain production of high-yielding hybrid rice (Oryza sativa) varieties. However, the mechanisms by which PGMS lines perceive changes in photoperiod and transmit those signals to elicit downstream effects are not well understood. In this study, we compared the transcriptomes from the leaves and anthers of carbon starved anther (csa), a PGMS line, to wild-type (WT) tissues under different photoperiods. Components of circadian clock in the leaves, including Circadian Clock-Associated 1 and Pseudo-Response Regulator (PRR95), played vital roles in sensing the photoperiod signals. Photoperiod signals were weakly transduced to anthers, where gene expression was mainly controlled by the CSA allele. CSA played a critical role in regulating sugar metabolism and cell wall synthesis in anthers under short-day conditions, and transcription of key genes inducing csa-directed sterility was upregulated under long-day (LD) conditions though not to WT levels, revealing a mechanism to explain the partial restoration of fertility in rice under LD conditions. Eight direct targets of CSA regulation were identified, all of which were genes involved in sugar metabolism and transport (cell wall invertases, SWEETs, and monosaccharide transporters) expressed only in reproductive tissues. Several hub genes coordinating the effects of CSA regulation were identified as critical elements determining WT male fertility and further analysis of these and related genes will reveal insights into how CSA coordinates sugar metabolism, cell wall biosynthesis, and photoperiod sensing in rice anther development.

Effect of Nozzle Geometry on the Flow Dynamics and Resistance Inside and Outside the Cone-Straight Nozzle.

The cone-straight nozzle has been commonly utilized in various applications, such as cleaning, cutting, and drilling, and hence investigated extensively with simulations and experiments. However, the internal flow patterns and dynamics, as well as the influence of internal flow on jetting performance, remain unclear. In this study, we carry out both experiments and computational fluid dynamics to understand the effect of different converging angles of the cone-straight nozzle on internal and external flow patterns. Nozzle flows are simulated by a large eddy simulations model and further compared with the experimental flow fields obtained by a particle image velocimetry (PIV) method. Nozzles with different converging angles and throat lengths have been used experimentally. The influence of nozzle converging angle, throat length, and inlet flow speed on flow field, skin friction resistance, and viscous force is discussed. Associated boundary layer transition and separation are investigated comparatively. The flow discharge coefficient and flow core length are measured by the PIV test system with a high-pressure pump. The experimental results show that a specific converging angle and flow speed can cause the boundary layer transition and separation. Skin friction resistance increases first and then decreases with the increase of inlet flow speed when the angle is larger than 20°. The resistance decreases gradually when the angle is lower than 15°. Importantly, the skin friction resistance remains a lower level when the converging angle is 15°, in agreement with the previous research results. The experimental results show that the nozzle with a converging angle of 10° or 15° has a higher discharge coefficient and a better cluster capacity. The nozzle with a throat length of 3 times the outlet diameter has a longer flow core. Considering the nozzle size, the nozzle with a converging angle of 15° and a throat length of 3 times the diameter of the outlet is suggested when the nozzle is used in jetting for obtaining a longer jetting distance.

Plasmonic Rare-Earth Nanosheets as Surface Enhanced Raman Scattering Substrates with High Sensitivity and Stability for Multicomponent Analysis.

Looking for high-performance substrates is an important goal of current surface enhanced Raman scattering (SERS) research. Herein, ultrathin multilayer rhenium (Re) nanosheets as a rare-earth metal substrate are found to have extraordinary SERS performance. These Re nanosheets are prepared through a convenient low-temperature molten salt strategy, and their total thickness is ∼5 nm, including 3-4 layers of ultrathin nanosheets with a thickness of only ∼1 nm. The viscosity of molten salt plays a key role in the formation of these ultrathin layered nanosheets. These nanosheets exhibit a strong and well-defined localized surface plasmon resonance (SPR) effect in the visible light region. The plasmonic Re nanosheets show excellent SERS performance with high sensitivity, chemical stability, and signal repeatability. The lowest detection limit for toxic compounds is 10-12 mol, and the corresponding Raman enhancement factor is 9.1 × 108. A composite enhancement mechanism caused by localized-SPR and charge transport has played an important role in the rare-earth-SERS. High-throughput multiassay analysis is performed on the flexible membrane assembled from the Re nanosheets, which highlights that our system is capable of rapid separation and identification of the samples containing various analytes.

OsFH3 Encodes a Type II Formin Required for Rice Morphogenesis.

The actin cytoskeleton is crucial for plant morphogenesis, and organization of actin filaments (AF) is dynamically regulated by actin-binding proteins. However, the roles of actin-binding proteins, particularly type II formins, in this process remain poorly understood in plants. Here, we report that a type II formin in rice, Oryza sativa formin homolog 3 (OsFH3), acts as a major player to modulate AF dynamics and contributes to rice morphogenesis. osfh3 mutants were semi-dwarf with reduced size of seeds and unchanged responses to light or gravity compared with mutants of osfh5, another type II formin in rice. osfh3 osfh5 mutants were dwarf with more severe developmental defectiveness. Recombinant OsFH3 could nucleate actin, promote AF bundling, and cap the barbed end of AF to prevent elongation and depolymerization, but in the absence of profilin, OsFH3 could inhibit AF elongation. Different from other reported type II formins, OsFH3 could bind, but not bundle, microtubules directly. Furthermore, its N-terminal phosphatase and tensin homolog domain played a key role in modulating OsFH3 localization at intersections of AF and punctate structures of microtubules, which differed from other reported plant formins. Our results, thus, provide insights into the biological function of type II formins in modulating plant morphology by acting on AF dynamics.

Cotton stubble detection based on wavelet decomposition and texture features.

BACKGROUND: At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. METHODS: Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. RESULTS: The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. CONCLUSIONS: The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.