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Steroidal molecularly imprinted polymers (MIPs) and non-imprinted polymer (NIP) were prepared by precipitation polymerization method using cholesterol, deoxycholic acid, and β-sitosterol as the virtual templates, acrylic acid (AA) as the functional monomer, azobisisobutyronitrile (AIBN) as the initiator, and ethylene glycol dimethacrylate (EGDMA) as the cross-linking agent. The morphology and structure of the polymers were characterized using scanning electron microscopy (SEM), X-ray spectroscopy (XRD), Fourier-transform infrared spectroscopy (FT-IR), and Brunauer-Emmett-Teller (BET) specific surface area analysis. The adsorption performance for steroidal substances was also investigated. The results showed that the steroidal MIPs were uniformly-sized and well-dispersed spherical nanoparticles with porous surface. Adsorption performance results showed that MIPs had significantly stronger adsorption capacities for 5α-cholestane compared to NIP. Among the three MIPs, deoxycholic acid and β-sitosterol MIPs demonstrated stronger adsorption capacities for 5α-cholestane than that of cholesterol. The adsorption kinetics studies showed that the adsorption process of MIPs for 5α-cholestane was in accordance with the pseudo-second-order kinetic model, which was mainly controlled by chemisorption. The isothermal adsorption of both MIPs and NIP conformed to the Langmuir isothermal adsorption model and the Scatchard model, indicating that MIPs exhibited specific selective adsorption for 5α-cholestane and that the adsorption process was monolayer adsorption, with a maximum adsorption capacity of 0.735 mg/g. The study suggests that MIPs prepared with cholesterol, deoxycholic acid, and β-sitosterol as virtual templates have high molecular recognition and selectivity for 5α-cholestane.

In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model’s ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively.

Conservation tillage, a crucial method for protecting soil fertility, depends heavily on maintaining adequate straw coverage. The current method of straw coverage detection relies primarily on manual measurement, which is both time-consuming and laborious. This paper introduces a novel straw coverage detection approach based on an improved mask regional convolutional neural network (Mask R-CNN) algorithm. Several images of wheat straw-covered fields were taken, and the dataset was augmented using techniques like image inversion, contrast enhancement, Gaussian noise addition, and translation after cropping the original images. These fields use a crop rotation cycle of wheat and corn. Subsequently, the straw images were annotated using the Labelme annotation tool to obtain the available straw instance segmentation dataset. The Mask R-CNN algorithm was improved by refining the mask generation network structure through a multi-feature fusion strategy, which interweaves features from both the encoder and the mask generation network, enhancing the model’s ability to capture detailed and shape information of the straw. Lastly, using the mask information output by the improved Mask R-CNN algorithm, the straw coverage was calculated by counting the proportion of pixels within each segmented region. In the results, compared to the original Mask R-CNN algorithm, our improved Mask R-CNN algorithm achieved an average improvement of 7.8% in segmentation accuracy, indicating that the improved Mask R-CNN algorithm offers superior segmentation performance. Thus, the new algorithm can achieve straw coverage detection with higher accuracy and can provide a reference for other agricultural applications.

Returning straw to the field is very important of for the conservation tillage to increase land fertility. It is vital to detect the unevenness of the straw covering to evaluate the performance of no-tillage planter, especially for the ones with returning full amount of straw. In this study, two kinds of RGB-D(Red, Green, Blue-Depth) sensors (RealSense D435i and Kinect v2) were applied to estimate the straw mulching unevenness by detecting the depth of straw coverage. Firstly, the overall structure and working principle of no-tillage planter with returning the full amount of straw was introduced. Secondly, field images were captured with the two kinds of RGB-D sensors after no tillage planter operation. Thirdly, straw covering unevenness computing was carried on a system developed by Matlab. Finally, the correlation analysis was conducted to test for the relationship between the straw covering unevenness by manual and deep sensors, with R (correlation coefficient) of 0.93, RMSE(Root Mean Square Error) of 4.59% and MAPE(Mean of Absolute Percentage Error) of 3.86% with D435i sensor, and with R of 0.915, RMSE of 6.53% and MAPE of 13.85% with Kinect V2, which showed both kinds of RGB-D sensors can acquire the unevenness of straw covering efficiently. The finding can provide a potential way to detect the unevenness of straw coverage and data support for operation evaluation and improvement of no-tillage planter.

Reliable precipitation forecasting is essential for effective water management and timely warning of natural disasters such as floods and droughts. However, precipitation is a nonlinear water vapor cycle with certain spatial and temporal dependence, and stable prediction accuracy cannot be obtained by using a single model. Therefore, this paper proposes a novelty prediction model based on original feature extraction and an improved multi-objective swarm intelligence optimization algorithm, and it carries out multi-step prediction tests for two sites in the arid/semi-arid region (Qilian Mountain-Hexi Corridor). Finally, through the 19 comparison models, 5 evaluation indexes and 3 model performance tests, it is confirmed that the precipitation combined forecasting model constructed in this study is a reliable prediction system with optimal parameters. And it can provide favorable technical support for weather forecasting.

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

The main objective of this study was to evaluate the quality and select the best varieties from 85 olive varieties in the Liangshan region, China, from the perspective of oil content, fatty acid, and triacylglycerol compositions. Although 85 varieties of olive oil showed no difference in the type of fatty acid composition and the distribution of triglycerides, they varied greatly in the oil content, the relative proportion of fatty acids, and triglycerides. Principal component analysis (PCA) and cluster and heatmap analysis clearly divided the 85 samples into three groups. Group A included 36 varieties and was characterized by high oleic acid, mono-unsaturated fatty acids (MUFA), eicosenoic acid, OOO (glyceryl trioleate), and OOL (1-oleic acid-2-oleic acid-3-linoleic acid glycerides) contents, but rare palmitoleic acid, SFA (saturated fatty acid), and POP (1-palmitic acid-2-oleic acid-3-palmitic acid glycerides) levels. Group B contained 9 varieties of olive, with the main characteristics of high oleic acid, linolenic acid, MUFA, MUFA/PUFA, SFA, and OLnO (1-oleic acid-2-linolenic acid-3-oleic acid glycerides) content, but low in linoleic acid, PUFA (polyunsaturated fatty acid), PLL levels and ratio of omega-6 to omega-3 polyunsaturated fatty acids. Group C comprised 40 varieties, identified by high linoleic acid and PUFA levels but low ratios of oleic acid, MUFA, OOO, MUFA/PUFA, and the ratio of omega-6 to omega-3 polyunsaturated fatty acids. Combined with the oil content (FW) (%) of 85 varieties, we suggested four distinguished varieties in Group A, namely Lechín, Coratina, Koroneiki, and Arbosana; three distinguished varieties in Group B, namely Picual, Ezhi, Cornicabra; two distinguished varieties in Group C namely Frantoio and Arbequina as the objects of large-scale cultivation by Chinese cultivators. More importantly, the obtained data also demonstrated that some locally bred specific varieties, such as Zhongze-3 and Yuntai, had interesting unsaturated fatty acids and had cultivation and popularization value in China.

The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, incubation, and disease under the stress of root rot. Two types of chili pepper seeds (Manshanhong and Shanjiao No. 4) were cultured until they had grown two to three pairs of true leaves. Subsequently, robust young plants were infected with Fusarium root rot fungi by the root-irrigation technique. The effective wavelength for discriminating between distinct stages was determined using the successive projections algorithm (SPA) after capturing hyperspectral images. The optimal index related to root rot between each normalized difference spectral index (NDSI) was obtained using the Pearson correlation coefficient. The early detection of root rot illness can be modeled using spectral information at effective wavelengths and in NDSI, together with the application of partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LSSVM), and back-propagation (BP) neural network technology. The SPA-BP model demonstrates outstanding predictive capabilities compared with other models, with a classification accuracy of 92.3% for the prediction set. However, employing SPA to acquire an excessive number of efficient wave-lengths is not advantageous for immediate detection in practical field scenarios. In contrast, the NDSI (R445, R433)-BP model uses only two wavelengths of spectral information, but the prediction accuracy can reach 89.7%, which is more suitable for rapid detection of root rot. This thesis can provide theoretical support for the early detection of chili root rot and technical support for the design of a portable root rot detector.

It is meaningful to understand the conversion pathways of nitrogen during the hydrothermal liquefaction process of microalgae to reveal the related reaction mechanisms and develop effective methods to prevent N from ending in biocrude, which eventually increases the quality of biocrude. Extending from our previous works that mainly focused on two high-protein (>50 wt%) microalgae (Chlorella sp. and Spirulina sp.), Nannochloropsis sp., which has a high lipid content (>70 wt%), was used as the feedstock for this project using the same methodology. The high lipid content in Na. induced less nitrogen during the oil phase and as a result, reduced the heteroatom content while also improving the quality of biocrude. It is worth noting that another investigation was conducted on the model compounds with different types of amino acids to specify the effects of the types of amino acids in the proteins in microalgae on the N pathway and their distribution in the products (aqueous phase, oil, solid, and gas). It was found that the basic amino acid in microalgae caused the formation of more N-heterocyclic compounds in the biocrude. The mass flow based on the mass balance was demonstrated to further refine the map showing the predicted reaction pathway of nitrogen from the previous version.

Sludge retention time (SRT) regulation is one of the essential management techniques for refined control of the main-sidestream treatment process under the low ammonia density. It is indispensable to understand the effect of SRTs changes on the Nitrifier kinetics to obtain the functional separation of the Nitrifier and the refined control of the nitrification process. In this study, Nitrifier was cultured with conditions of 35 ± 0.5 °C, pH 7.5 ± 0.2, DO 5.0 ± 0.5 mg-O/L, and SRTs were controlled for 40 d, 20 d, 10 d, and 5 d. The net growth rate (), decay rate (), specific growth rate (), the yield of the Nitrifier (), temperature parameter (), and inhibition coefficient () have been measured and extended with the SRT decreases. Instead, the half-saturation coefficient () decreased. In addition, the limited value of pH inhibition occurs (), and the pH of keeping 5% maximum reaction rate () was in a relatively stable state. The trade of kinetics may be induced by the change of species structure of Nitrifier. The Nitrosomonas proportion was increased, and the Nitrospira was contrary with the SRT decreasing. It is a match for the functional separation of Nitrifier when SRTs was 20 d at ambient temperature under the low ammonia density. The kinetics of ammonia-oxidizing organisms (AOO) and nitrite-oxidizing organisms (NOO) in Nitrifier under different SRT conditions should be measured respectively to the refined control of the partial nitrification process in future study. HIGHLIGHT The Nitrifier typical kinetics were affected notably by SRTs changes.; The species structure of the Nitrifier was recognized beneath distinctive SRTs.; The change of Nitrifier kinetics with SRTs was estimated by the species structure changes.;

Abstract Human leukocyte antigen (HLA), also known as human major histocompatibility complex (MHC), is encoded by the HLA gene complex, and is currently known to have the highest gene density and the most polymorphisms among human chromosomal areas. HLA is divided into class I antigens, class II antigens, and class III antigens according to distribution and function. Classical HLA class I antigens include HLA‐A, HLA‐B, and HLA‐C; HLA class II antigens include HLA‐DP, HLA‐DQ, and HLA‐DR; nonclassical HLA class I and II molecules include HLA‐F, E, H, X, DN, DO, and DM; and others, such as complement, are class III antigens. HLA is closely related to the body's immune response, regulation, and surveillance and is of great significance in the study of autoimmune diseases, tumor immunity, organ transplantation, and reproductive immunity. HLA is an important research topic that bridges immunology and clinical diseases. With the development of research methods and technologies, there will be more discoveries and broader prospects.

Vortioxetine is currently used as the first-line therapy drug for major depressive disorder (MDD). In the present study, we aimed to develop and fully validate an ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification of vortioxetine and its major metabolite Lu AA34443 in plasma and to investigate the effects of dronedarone and amiodarone on vortioxetine metabolism in rats. After protein precipitation with acetonitrile, the separation of vortioxetine, Lu AA34443 and duloxetine (internal standard, IS) were finished on an Acquity BEH C18 (2.1 mm × 50 mm, 1.7 μm) column and their detections were conducted by a Xevo TQ-S triple quadrupole tandem mass spectrometer in the positive ion mode. The assay displayed excellent linearity in the range of 0.5–50 ng/mL for vortioxetine, and 5–1000 ng/mL for Lu AA34443. The results of this method exhibited that the precision, accuracy, matrix effect, recovery, and stability of vortioxetine and Lu AA34443 met all requirements for the quantitation in plasma samples. The validated assay was further successfully employed to study the effects of dronedarone (80 mg/kg) and amiodarone (60 mg/kg) on vortioxetine metabolism in rats. The results showed that dronedarone and amiodarone could increase the concentration of vortioxetine and have inhibitory effect on vortioxetine metabolism. Thus, vortioxetine dose interruption or reduction may be considered.

The pH detection in the alkaline range is particularly important in many fields such as leather processing, waste water treatment, paper industry, and metal mining and finishing. Compared with traditional analysis methods such as colorimetric sensors and electrochemical sensors, the fluorescence and colorimetric probes for pH measurements have attracted much more attention due to their advantages of high sensitivity, excellent selectivity, noninvasiveness, low cost, fast response time, the possibility of continuously measurement, etc. However, there are few fluorescent probes fiting for alkaline pH monitoring. Acturally, the design and synthesis of them were more significant for new probes producing. In this study, the design, synthesis, and practical application of two novel fluorescent pH probes for alkaline pH assay were discussed. Both of the two probes were derived from perylene tetra-(alkoxycarbonyl). The red or blue shift of the absorption/fluorescence spectrum was caused by the introduction of electron donor amino or oxygen ring in the bay region. Due to electronic separation of the OH group from the electron-withdrawing core, the probes have high pKa values and cover the pH range from 8 to 12. They exist in either fluorescent acid form or non-fluorescent basic form. It was investigated that the amino substituent at bay region had a higher pKa value than O-heterocyclic annulated perylene, which showed that the adjustable pKa value could be achieved by the modification of electron withdrawing groups. The probes would have a wide use for testing strips measurements and monitoring pH changes in concrete.

Litchi is often harvested by clamping and cutting the branches, which are small and can easily be damaged by the picking robot. Therefore, the detection of litchi branches is particularly significant. In this article, an fully convolutional neural network-based semantic segmentation algorithm is proposed to semantically segment the litchi branches. First, the DeepLabV3+ semantic segmentation model is combined with the Xception depth separable convolution feature. Second, transfer learning and data enhancement are used to accelerate the convergence and improve the robustness of the model. Third, a coding and a decoding structure are adopted to reduce the number of network parameters. The decoding structure uses upsampling and the shallow features to fuse, and the same weight is assigned to ensure that the shallow feature semantics and the deep feature semantics are evenly distributed. Fourth, using atrous spatial pyramid pooling, we can better extract the semantic pixel position information without increasing the number of weight parameters. Finally, different sizes of hole convolution are used to ensure the prediction accuracy of small targets. Experiment results demonstrated that the DeepLabV3+ model using the Xception_65 feature extraction network obtained the best results, achieving a mean intersection over union (MIoU) of 0.765, which is 0.144 higher than the MIoU of 0.621 of the original DeepLabV3+ model. Meanwhile, the DeepLabV3+ model using the Xception_65 network has greater robustness, far exceeding the PSPNet_101 and ICNet in detection accuracy. The aforementioned results indicated that the proposed model produced better detection results. It can provide powerful technical support for the gripper picking robot to find fruit branches and provide a new solution for the problem of aim detection and recognition in agricultural automation.

It is a challenging problem to detect the apple in natural environment using traditional object recognition algorithms due to occlusion, fluctuating illumination and complex backgrounds. Deep learning methods for object detection make impressive progress, which can automatically extract the number, pixel position, size and other features of apples from the images. In this paper, four deep learning recognition models, Faster RCNN based on AlexNet, Faster RCNN based on ResNet101, YOLOv3 based on DarkNet53 and improved YOLOv3 were employed to carry out recognition experiments on red and green apple under three illumination and two image sharpness conditions, with the transfer learning to accelerate the training process. The results showed that improved YOLOv3 model had the best recognition effect among the four detection models. F1 value of red apple recognition was 95.0%, 94.6% and 94.1% for normal, insufficient and excessive illumination, respectively, and F1 value of green apple recognition was 94.9%, 94.0% and 91.1%. There were F1 value of 92.8% and 92.1% for red and green apple recognition in blurred images, respectively. Moreover, improved YOLOv3 algorithm still had the better performance for occlusion, spot, overlap and incomplete apples, with a recognition recall rate higher than 88.5%. It can be concluded that improved YOLOv3 algorithm can provide a more efficient way for apple detection in natural environment.

Strawberry is one of the popular fruits with numerous nutrients. The ripeness of this fruits was estimated using the hyperspectral imaging (HSI) system in field and laboratory conditions in this study. Strawberry at early ripe and ripe stages were collected HSI data, covered wavelength ranges from 370 to 1015 nm. Spectral feature wavelengths were selected using the sequential feature selection (SFS) algorithm. Two wavelengths selected for field (530 and 604 nm) and laboratory (528 and 715 nm) samples, respectively. Then, reliability of such spectral features was validated based on support vector machine (SVM) classifier. Performance of SVM classification models had good results with receiver operating characteristic values for samples under both field and laboratory conditions higher than 0.95. Meanwhile, the spatial feature images were extracted from the spectral feature wavelength and the first three principal components for laboratory samples. Pretrained AlexNet convolutional neural network (CNN) was used to classify the early ripe and ripe strawberry samples, which obtained the accuracy of 98.6% for test dataset. The above results indicated real-time HSI system was promising for estimating strawberry ripeness under field and laboratory conditions, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Background: U To analyze the effect of metformin hydrochloride combined with insulin pump for gestational diabetes mellitus (GDM). Methods: Overall, 216 patients with GDM in Zhangqiu Maternity and Child Care Hospital, Jinan, China from Aug 2018 to Dec 2020 were enrolled and randomized into research and control groups. Patients in the control group were treated with insulin pump, while those in the research group were treated with metformin hydrochloride combined with insulin pump. The clinical efficacy, blood glucose levels, serum Betatrophin, C reactive protein (CRP), Cystatin C (Cys-C), homocysteine (Hcy), adiponectin, tumor necrosis factor (TNF-α), interleukin-6 (IL-6) content, incidence of adverse pregnancy outcomes and incidence of adverse newborns of patients in the two groups were compared. Results: After treatment, the total clinical efficiency of the research group was 84.26%, significantly higher than that of the control group (68.52%). The levels of FPG, 2hPG, HbAlc, serum Betatrophin, CRP, CysC, Hcy, adiponectin factors, TNF-α, and IL-6 in the research group were lower than those in the control group, with statistically significant differences (P

Aplastic anemia results from bone marrow failure caused by an autoimmune abnormality, but the pathogenesis of severe aplastic anemia (SAA) is not well characterized. To identify potential metabolic markers of SAA and to further elucidate the pathogenetic mechanisms of SAA, we performed a metabolomic study of plasma samples and characterized the intestinal microbiota of patients with SAA and healthy controls. Patients with SAA had more Enterobacteriales and Lactobacillales, but fewer Bacteroidales, Clostridiales, and Erysipelotrichales than healthy controls. At the species level, the abundances of Escherichia coli and others including Clostridium citroniae were higher, whereas those of Prevotella copri, Roseburia faecis, and Ruminococcus bromii were lower. Eight metabolites showed significantly different plasma concentrations in the SAA and healthy control groups. Coumaric acid, L-phenylalanine, and sulfate were present at higher concentrations in the SAA group; whereas L-glutamic γ-semialdehyde, theobromine, 3a, 7a-dihydroxy-5b-cholestane, γ-δ-dioxovaleric acid, and (12Z)-9, 10-dihydroxyoctadec-12-enoic acid were present at lower concentrations. In conclusion, patients with SAA show abnormalities in both their plasma metabolomes and intestinal microbial compositions. These differences might reflect the molecular mechanisms involved in the defective immunity that characterizes SAA.

Summary: How cells adapt to oncogenic transformation-associated cellular stress and become fully transformed is still unknown. Here we identified a novel GGCT-regulated glutathione (GSH)-reactive oxygen species (ROS) metabolic pathway in oncogenic stress alleviation. We identified GGCT as a target of oncogenic Ras and that it is required for oncogenic Ras-induced primary mouse cell proliferation and transformation and in vivo lung cancer formation in the LSL-Kras G12D mouse model. However, GGCT deficiency is compatible with normal mouse development, suggesting that GGCT can be a cancer-specific therapeutic target. Genetically amplified GGCT locus further supports the oncogenic driving function of GGCT. In summary, our study not only identifies an oncogenic function of GGCT but also identifies a novel regulator of GSH metabolism, with implications for further understanding of oncogenic stress and cancer treatment. : Biological Sciences; Cell Biology; Cancer Subject Areas: Biological Sciences, Cell Biology, Cancer

Vortioxetine, as a new therapeutic drug of major depressive disorder (MDD), was approved for MDD in the USA. The significance of our study in this paper was to explore the inhibitory effect of propafenone on the metabolism of vortioxetine through the in vivo and in vitro experiments. In the vitro experiments, we added a series of concentrations of propafenone into an incubation system as the inhibitors and calculated the half-maximal inhibitory concentration (IC50) of propafenone on vortioxetine metabolism in human liver microsomes (HLMs) and rat liver microsomes (RLMs). Twelve male Sprague-Dawley (SD) rats were included in the vivo experiments. We randomly divided them into Group A (control group) and Group B (90 mg/kg propafenone). 30 min later, a single oral dose of 4.0 mg/kg vortioxetine was administrated to each rat. Then, we used ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to determine the concentrations of vortioxetine and its metabolite Lu AA34443. From our results, it indicated that propafenone inhibited the metabolic rate of vortioxetine in the vitro studies with the IC50 of 0.48 μM and 16.5 μM for HLMs and RLMs, respectively. And, propafenone could competitively inhibit vortioxetine in both HLMs and RLMs for the inhibitory mechanisms. Moreover, a single oral dose of 90 mg/kg propafenone obviously enchanced the exposure of vortioxetine in rats, but not Lu AA34443. Combined with the vitro and vivo data, propafenone showed the inhibitory effect on vortioxetine metabolism. Thus, more attention to the safety of vortioxetine in clinic should be paid when taking it with propafenone in combination for the therapy.

Exosomes are membrane-bound extracellular vesicles that are produced in the endosomal compartment of most eukaryotic cells. Containing proteins, RNA, and DNA, exosomes mediate intercellular communication between different cell types by transferring their contents and thus are involved in numerous physiological and pathological processes. T cells are an indispensable part of adaptive immunity, and the functions of T cell-derived exosomes have been widely studied. In the more than three decades since the discovery of exosomes, several studies have revealed that T cell-derived exosomes play a novel role in cell-to-cell signaling, especially in inflammatory responses, autoimmunity, and infectious diseases. In this review, we will summarize the function of T cell-derived exosomes and their therapeutic potential.

This study is aimed at investigating the effects of shikonin, a pyruvate kinase M2 (PKM2) inhibitor, on the functions of myeloid dendritic cells (mDCs) in a mouse model of severe aplastic anemia (AA) generated by total body irradiation and lymphocyte infusion. Flow cytometry and qPCR were used to determine the proportions of PKM2+ mDCs and other immune indicators in the AA mice. Glucose consumption level, pyruvate generation level, and ATP content were used to determine the level of glycolytic metabolism in the mDCs. The survival rates of AA mice were evaluated after the administration of shikonin or the immunosuppressive agent cyclosporin A. The AA mice displayed pancytopenia, decreased CD4+/CD8+ cell ratio, increased perforin and granzyme levels in CD8+ cells, increased costimulatory CD80 and CD86 expressions, and inadequate regulatory T cell number. In vivo animal experiments showed that the shikonin-mediated inhibition of the PKM2 expression in mice was associated with high survival rates. In addition, the administration of cyclosporin A or shikonin decreased the expression of cytotoxic molecules and costimulatory CD80 and CD86 on CD8+ cells. Taken together, the results of this study indicated that shikonin could inhibit the activation and proliferation of mDCs as well as the activation of downstream cytotoxic T cells by reducing the PKM2 level in mDCs.

The role of most plant leucine-rich repeat (LRR) receptors in innate immunity is unknown. Here, the authors develop virus-based constructs to silence LRR receptor-like genes in the Nicotiana benthamiana genome and identify Response to XEG1 that specifically recognizes the glycoside hydrolase 12 protein XEG1.

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry's competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350-2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

Standardization aspects of IoT were discussed. After identified the definition of IoT in China, challenges of IoT were put forward. Somebody considered those challenges mainly came from the lack of standards, according to the definition of IoT, different technologies and standards must be included in IoT. It was pointed out that instead of standardization, abstract methods should be the most effective way to make the inclusion. As an example, the sensor and actuator platform built by NUPT was briefly introduced.

In order to solve the poor control accuracy problem of the traditional synchronous control algorithm for a double-cylinder forging hydraulic press, a synchronous control algorithm for double-cylinder forging hydraulic press based on a fuzzy neural network was proposed. According to the flow equation of valve and hydraulic cylinder, the balance equation and force balance equation of forging hydraulic cylinder are established by using the theory of electro-hydraulic servo systems, and the cylinder-controlled transfer function of forging hydraulic cylinder is deduced. By properly simplifying the transfer function, the mathematical model of synchronous control of double cylinder forging hydraulic press is established. According to the implementation process of traditional fuzzy neural networks, the properties of compensation operation are introduced. The traditional fuzzy neural network is optimized, and the optimized neural network is used to realize the synchronous control of the double cylinder forging hydraulic press. The experimental results show that the amplitude curve of the algorithm is very close to the expected amplitude curve, the error amplitude is only 0.3 mm, and the average control time is about 140 s, which fully shows that the algorithm has high accuracy and a good control effect.

This paper presents a concise overview of ash deposition in combustion or co-firing of biomass (woody biomass, agricultural residues, peat, etc.) with other fuels for power/heat generation. In this article, the following five research aspects on biomass combustion ash deposition are reviewed and discussed: influence of biomass fuel characteristics, deposit-related challenges, ash deposition monitoring and analysis of ash deposits, mechanisms and chemistry of fly ash deposition, and key technologies for reducing ash deposition and corrosion in biomass-involved combustion.

An efficient and simple method for preparing bonded metallic powder coating is in high demand in the paint manufacturing and application industries. The bonding purpose is to keep the mass percentage of metallic pigment consistent between the original and recycled coating powder, which aims at solving the problem of recyclability. One possible method capable of realizing this goal is using the binder to cohere metallic pigment with base particles through a cold bonding method. Through this approach, the pre-curing and high-reject-rate problems generally present in thermal bonding can be completely eliminated. In this paper, polyacrylic acid (PAA) and polyvinyl alcohol (PVA) are applied as binders for the bonding process. At various dosages of liquid binder and D.I. water, bonded samples with different bonding effect were prepared. Finally, a good bonding quality with the lowest variance between the mass concentrations of Al flakes in the original powder (before spray) and deposited powder (after spray) 2.94% with PAA as a binder and 0.46% with PVA as a binder was achieved. These results manifest that the cold bonding method is a green and simple approach for preparing the metallic powder coating.

This study is a report on modifications and pilot testing of a measurement tool to predict Pap testing based on the Interaction Model of Client Health Behavior. Women have been found to avoid routine cervical cancer screening (CCS) due to personal influencing factors. For this study, the Pap Smear Belief Questionnaire (PSBQ) was modified by adding items and evaluated through content validity index (CVI = .89) and cognitive interviews (think-aloud protocol), resulting in a 31-item instrument with four subscales (Benefits, Barriers, Vulnerability, Experiences). Pilot testing the modified PSBQ was conducted at a local agency. Fifty-three women completed and returned the PSBQ, which had good internal consistency, with a Cronbach’s alpha coefficient of .81 (Benefits α = .51; Barriers α = .85; Vulnerability α = .78; Experiences α = .87). Test–retest reliability of the 31-item PSBQ was also good (.878). Univariate logistic regression indicated that the only significant predictor of seeking/avoiding routine screening by women’s background characteristics was social influence ( p = .04). Subscales found to be significant predictors in Pap testing were Barriers ( p = .003) and Gynecological Exam Experiences ( p = .04). The refined 31-item PSBQ has good psychometric data supporting its use in predicting CCS. However, this research should be replicated in a larger sample enabling further validity and reliability testing of the PSBQ. Understanding women’s attitudes toward CCS will help health care providers, who deal with gynecological health, develop more effective screening programs and interventions facilitating better experiences for women contributing to an increase in routine screening.