Research on the application and effect of flipped-classroom combined with TBL teaching model in WeChat-platform-based biochemical teaching under the trend of COVID-19.

BACKGROUND: Biochemistry is a core subject in clinical medical education. The traditional classroom teaching model led by teachers is often limited to the knowledge transfer of teachers and the passive acceptance of students. It lacks interactive and efficient teaching methods and is not enough to meet the learning needs and educational goals of modern students. The combination of WeChat public platform, flipped classroom and TBL teaching model is closer to the needs of real life and workplace, helping students to cultivate comprehensive literacy and the ability to solve practical problems. At the same time, this teaching model has yet to be used in biochemistry courses. OBJECTIVE: To explore the influence of the mixed teaching model of flipped classroom and combining TBL based on WeChat public platform upon undergraduates in biochemistry. METHODS: Using the mixed research method of quasi-experimental research design and descriptive qualitative research, 68 students were selected into the traditional and the blended teaching groups. Among them, the blended teaching group adopts the blended teaching model of flipped classroom combined with TBL based on the WeChat platform to learn biochemical courses. In this study, an independent sample t-test was intended to analyze the differences in final scores, a chi-square test was served to analyze the differences in satisfaction questionnaires, and thematic analysis was used to analyze semi-structured interview data. RESULTS: Compared with the traditional teaching model, the mixed teaching model significantly improved students' final exam scores (P < 0.05). The teaching satisfaction of the mixed teaching group was also higher than that of the traditional teaching group with statistical significance (P < 0.05). The results of the interviews with eight students were summarized into three topics: (1) Stimulating interest in learning; (2) Improving the ability of autonomous learning; (3) Recommendations for improvement. CONCLUSIONS: The combination of the WeChat platform and flipped classroom with TBL has a positive effect on improving medical students' autonomous learning ability and problem-solving ability. The research shows that the teaching mode of flipped classroom combined with TBL based on the WeChat platform is effective and feasible.

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface.

In this study, a hyperspectral imaging system of 866.4-1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky-Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC-NCA-SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC-NCA-SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

Specific Reactivation of Latent HIV-1 by dCas9-SunTag-VP64-mediated Guide RNA Targeting the HIV-1 Promoter.

HIV-1 escapes antiretroviral agents by integrating into the host DNA and forming a latent transcriptionally silent HIV-1 provirus. Transcriptional activation is prerequisite for reactivation and the eradication of latent HIV-1 proviruses. dCas9-SunTag-VP64 transcriptional system has been reported that it can robustly activate the expression of an endogenous gene using a single guide RNA (sgRNA). Here, we systematically investigated the potential of dCas9-SunTag-VP64 with the designed sgRNAs for reactivating latent HIV-1. We found dCas9-SunTag-VP64 with sgRNA 4 or sgRNA 5 targeted from -164 to -146 or -124 to -106 bp upstream of the transcription start sites of HIV-1 could induce high expression of luciferase reporter gene after screening of sgRNAs targeting different regions of the HIV-1 promoter. Further, we confirmed that dCas9-SunTag-VP64 with sgRNA 4 or sgRNA 5 can effectively reactivate latent HIV-1 transcription in several latently infected human T-cell lines. Moreover, we confirmed that the reactivation of latent HIV-1 by dCas9-SunTag-VP64 with the designed sgRNA occurred through specific binding to the HIV-1 LTR promoter without genotoxicity and global T-cell activation. Taken together, our data demonstrated dCas9-SunTag-VP64 system can effectively and specifically reactivate latent HIV-1 transcription, suggesting that this strategy could offer a novel approach to anti-HIV-1 latency.

Construction of a 2D Graphene-Like MoS2/C3N4 Heterojunction with Enhanced Visible-Light Photocatalytic Activity and Photoelectrochemical Activity.

A novel graphene-like MoS2 /C3N4 (GL-MoS2/C3N4) composite photocatalyst has been synthesized by a facile ethylene glycol (EG)-assisted solvothermal method. The structure and morphology of this GL-MoS2/C3N4 photocatalyst have been investigated by a wide range of characterization methods. The results showed that GL-MoS2 was uniformly distributed on the surface of GL-C3N4 forming a heterostructure. The obtained composite exhibited strong absorbing ability in the ultraviolet (UV) and visible regions. When irradiated with visible light, the composite photocatalyst showed high activity superior to those of the respective individual components GL-MoS2 and GL-C3N4 in the degradation of methyl orange. The enhanced photocatalytic activity of the composite may be attributed to the efficient separation of electron-hole pairs as a result of the matching band potentials between GL-MoS2 and GL-C3N4. Furthermore, a photocatalytic mechanism for the composite material has been proposed, and the photocatalytic reaction kinetics has been measured. Moreover, GL-MoS2/C3N4 could serve as a novel sensor for trace amounts of Cu(2+) since it exhibited good selectivity for Cu(2+) detection in water.

Identification of the Citrus Greening Disease Using Spectral and Textural Features Based on Hyperspectral Imaging.

In this paper we discussed the application of spectral and textural features in identifying early stage of the citrus greening disease (Huanglongbing or HLB). A total of 176 hyperspectral images of citrus leaves (60 for healthy, 60 for HLB-infected and 56 for zinc-deficient) were captured by using a near-ground hyperspectral imaging system. Regions of interest (ROI) were extracted manually from the part of pathological changes in the images to calculate the average reflectance spectra of each sample as the sample spectra, ranging from 396 to 1 010 nm. The dimensions of the sample spectra were reduced with the algorithms of principal component analysis (PCA) and successive projection analysis (SPA). Classification models were built with the original spectra and candidate variables, the first four PCs selected by PCA and a set of wavelengths (630.5, 679.4, 749.4 and 899.9 nm) selected by SPA. The results based on a classifier of least square-support vector machine (LS-SVM) showed that the classification models built with the candidate variables selected by PCA and SPA had a better performance, achieving 89.7% and 87.4% in terms of average accuracy. In addition, two groups of textural features, extracted from gray images of the four selected wavelengths based on gray-level histogram and gray-level co-occurrence matrix (GLCM), were also used for the classifier. The first ten features ranked by SPA promoted the average accuracy of classifier significantly, achieving 100%, 93.3% and 92.9% for the three class samples respectively. The results of this study indicated that it would be feasible to identify HLB using the image textural features based on selected wavelengths, and it provided a basis for developing a portable HLB detection system with multispectral imaging techniques.

N-acetyl-L-cysteine protects against cadmium-induced neuronal apoptosis by inhibiting ROS-dependent activation of Akt/mTOR pathway in mouse brain.

AIMS: This study explores the neuroprotective effects and mechanisms of N-acetyl-L-cysteine (NAC) in mice exposed to cadmium (Cd). METHODS: NAC (150 mg/kg) was intraperitoneally administered to mice exposed to Cd (10-50 mg/L) in drinking water for 6 weeks. The changes of cell damage and death, reactive oxygen species (ROS), antioxidant enzymes, as well as Akt/mammalian target of rapamycin (mTOR) signalling pathway in brain neurones were assessed. To verify the role of mTOR activation in Cd-induced neurotoxicity, mice also received a subacute regimen of intraperitoneally administered Cd (1 mg/kg) with/without rapamycin (7.5 mg/kg) for 11 days. RESULTS: Chronic exposure of mice to Cd induced brain damage or neuronal cell death, due to ROS induction. Co-administration of NAC significantly reduced Cd levels in the plasma and brain of the animals. NAC prevented Cd-induced ROS and significantly attenuated Cd-induced brain damage or neuronal cell death. The protective effect of NAC was mediated, at least partially, by elevating the activities of Cu/Zn-superoxide dismutase, catalase and glutathione peroxidase, as well as the level of glutathione in the brain. Furthermore, Cd-induced activation of Akt/mTOR pathway in the brain was also inhibited by NAC. Rapamycin in vitro and in vivo protected against Cd-induced neurotoxicity. CONCLUSIONS: NAC protects against Cd-induced neuronal apoptosis in mouse brain partially by inhibiting ROS-dependent activation of Akt/mTOR pathway. The findings highlight that NAC may be exploited for prevention and treatment of Cd-induced neurodegenerative diseases.

Dilazep synergistically reactivates latent HIV-1 in latently infected cells.

The long-lived latently infected cells persist in spite of prolonged highly active anti-retroviral therapy and present a major barrier to a cure of human immunodeficiency virus type 1 (HIV-1) infection. Elimination of this reservoir requires reactivation of the latent virus. None of the current agents can safely and effectively reactivate latent HIV-1 reservoirs. Dilazep, a nucleoside transport inhibitor, is used to treat ischemic dysfunction. However, little is known about the effect of dilazep in inducing HIV expression in latently infected cells. Using the Jurkat T cell model of HIV-1 latency, we found that dilazep effectively reactivates latent HIV-1 gene expression in a dose manner. We observed that dilazep synergistically reactivated latent HIV-1 transcription with valproic acid. We also found that dilazep activates viral latency without inducing cell surface activation markers CD25 and CD69 activation. In summary, dilazep, alone or in combination with VPA, could be useful in future eradication strategies.

[Detection of citrus greening based on Vis-NIR spectroscopy and spectral feature analysis].

In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes. By the Fisher linear discriminant analysis, three classification models (HLB & healthy, HLB & iron deficiency and HLB & nitrogen deficiency) based on the spectral features yielded more than 90% accuracies, which were better than expected. And at last, we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction. The models trained based on the original reflectance spectra, first derivative and selected spectral features yielded more than 88% average accuracy, and especially the model based on the spectral features yielded more than 94% average accuracies, which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction. The results were compared based on classification tree, k-NN and Bayesian classifiers. Adoption of spectral features as input variables was significantly superior to using the original spectrum, which confirmed the validity of spectral feature selection. Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus greening.

Effect of Subanesthetic Dose of Esketamine on Perioperative Neurocognitive Disorders in Elderly Undergoing Gastrointestinal Surgery: A Randomized Controlled Trial.

Background: Perioperative neurocognitive disorders (PND), including delayed neurocognitive recovery (dNCR) and postoperative cognitive dysfunction (POCD), are common postoperative complications in elderly patients and adversely affect their prognosis. The study was designed to explore the effects of esketamine on postoperative cognitive function in elderly patients who underwent gastrointestinal surgery under general anesthesia and its potential mechanism. Methods: Eighty-four patients aged 65 and above undergoing gastrointestinal surgery were randomly divided into 2 groups: the esketamine group (group S) and the control group (group C). Group S received intravenous sub-anesthetic doses of esketamine (0.15 mg/kg) 5 minutes before the initiation of surgery, while group C received the same volume of saline. A battery of neuropsychological tests was used to assess cognitive function before surgery, 7 days, and 3 months after surgery. The primary outcome was the incidence of dNCR at 7 days postoperatively and POCD at 3 months postoperatively in both groups. The secondary outcome measures included changes in the levels of serum interleukin-6 (IL-6) and calcium-binding protein ß (S100ß) before and 1 day after surgery. Results: The incidence of dNCR in group S was lower than that of group C (18.15% vs 38.24% P=0.033). Contrarily, there was no difference in both groups regarding POCD 3 months postoperatively (6.06% vs 14.37% P=0.247). Plasma IL-6 and S100ß levels were significantly elevated in both groups on postoperative day 1 (p<0.05), but esketamine pretreatment reduced these levels to some extent compared with group C (p<0.05). Conclusion: Sub-anesthetic doses of esketamine might reduce the incidence of dNCR and improve early postoperative cognitive function in elderly patients undergoing gastrointestinal surgery, which might be related to the anti-neuroinflammation effects of esketamine.

Development and Validation of a Delirium Risk Prediction Model for Elderly Patients Undergoing Elective Orthopedic Surgery.

Purpose: This study aimed to develop and validate a post-operative delirium (POD) nomogram in a population of elderly patients undergoing elective orthopedic surgery. Patients and Methods: A predictive model was developed based on a training dataset of 474 elderly patients undergoing elective orthopedic surgery from March 2021 to May 2022. POD was identified using the Confusion Assessment Methods (CAM). The least absolute shrinkage and selection operator (LASSO) method was used to screen risk factors, and prediction models were created by combining the outcomes with logistic regression analysis. We employ bootstrap validation for internal validation to examine the model's repeatability. The results were validated using a prospective study on 153 patients operated on from January 2022 to May 2022 at another institution. Results: The predictors in the POD nomogram included age, the Mini-Mental State Examination(MMSE), sleep disorder, neurological disorders, preoperative serum creatinine (Pre-SCR), and ASA classification. The c-index of the model was 0.928 (95% confidence interval 0.898 ~ 0.957) and the bootstrap validation still achieved a high c-index of 0.912. The c-index of the external validation was 0.921. The calibration curve for the diagnostic probability showed good agreement between prediction by nomogram and actual observation. Conclusion: By combining preoperative and intraoperative clinical risk factors, we created a POD risk nomogram to predict the probability of POD in elderly patients who undergo elective orthopedic surgery. It could be a tool for guiding individualized interventions.

Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology.

The quality of wheat kernels is critical to ensure crop yields. However, in actual breeding work, unsound kernels are scarce compared to healthy kernels. Limited data sets or unbalanced data sets make it difficult for many algorithms to accurately identify kernels in different states. A novel method based on deep convolutional generative adversarial network (DCGAN) and near-infrared hyperspectral imaging technology was proposed to identify unsound wheat kernels in this paper. Three classifiers, convolutional neural network (CNN), support vector machine (SVM) and decision tree (DT) were used. After expanding the samples, the results showed that the accuracy of the test set of the DT model increased from 51.67% to 80.83%, a total increase of 29.16%. And the CNN and SVM models increased by 8.34% and 14.17% respectively. This demonstrated that the DCGAN method had the ability to generate reliable data samples for unbalanced data sets for improving the performance of the classifier. On this basis, the training samples are further expanded for improving the performance of the classifier. The results showed that CNN model gained the most from incremental data, and its accuracy rate had been continuously improved from 79.17% to 96.67%, a total increase of 17.50%. This also demonstrated that the DCGAN method had the ability to expand a limited data set. In general, the joint model based on DCGAN and CNN combined with hyperspectral imaging technology had a good prospect in the identification of unsound kernels.

ABCA7 rs3764650 Polymorphism is Associated with Delayed Neurocognitive Recovery.

Background: Several studies have shown that ATP-binding cassette transporter A7 (ABCA7) gene variation is associated with cognitive impairment. This study was aimed to investigate the relationship between ABCA7 rs3764650 polymorphism and perioperative neurocognitive disorder (pNCD). Methods: A total of 132 elderly patients aged 65 and over who underwent elective non-cardiac surgery were enrolled in the study, while 28 healthy volunteers matching age and sex were recruited as the control group. A battery of neuropsychological tests was conducted 1 day before, 7 days, and 3 months after surgeries. Delayed neurocognitive recovery (dNCR) and postoperative mild or major neurocognitive disorder (POCD) were determined using the Z value method. The venous blood sample of the surgical patients was taken before the operation. Genotyping of rs3764650 was performed using polymerase chain reaction amplification and restriction fragment length polymorphism analysis. Results: The incidences of dNCR and POCD were 29.7% and 16.8% at 7 days and 3 months after surgery, respectively. The G allele frequency and GG frequency of dNCR patients were significantly higher than that of non-dNCR patients (43.3% vs 28.2%, P=0.035; 23.3% vs 4.2%, P=0.013, respectively) at 7 days following surgery. No significant differences in ABCA7 alleles between POCD and non-POCD patients were observed 3 months postoperatively. Conclusion: ABCA7 rs3764650 gene polymorphism is associated with dNCR and GG genotype might be a predisposing factor for postoperative cognitive impairment in Chinese Han elderly populations.

An emission-free controlled potassium pyrosulfate roasting-assisted leaching process for selective lithium recycling from spent Li-ion batteries.

Recycling critical metals from spent Li-ion batteries (LIBs) is important for the overall sustainability of future batteries. This study reports an improved sulfation roasting technology to efficiently recycle Li and Co from spent LiCoO2 LIBs using potassium pyrosulfate as sulfurizing reagent. By sulfation roasting, LiCoO2 was converted into water-soluble lithium potassium sulfate and water-insoluble cobalt oxide. Under optimal conditions, 98.51% Li was leached in water, with a selectivity of 99.86%. More importantly, sulfur can be recirculated thoroughly, and the sulfur atomic efficiency can be significantly enhanced by controlling the amount of potassium pyrosulfate. Li ions from the water leaching process were recovered by chemical precipitation. Furthermore, application of this technology to other spent LIBs, such as LiMn2O4 and LiNi0.5Co0.2Mn0.3O2, verified its effectiveness for selective recovery Li. These findings can provide some inspiration for high efficiency and environmentally friendly recovery metal from spent LIBs.

Dexmedetomidine Directs T Helper Cells toward Th1 Cell Differentiation via the STAT1-T-Bet Pathway.

Dexmedetomidine is an α2 adrenergic receptor agonist that has been reported to modulate the polarization of CD4+ T cells. However, the underlying mechanisms by which dexmedetomidine induces T-helper 1 (Th1) cell differentiation remain poorly understood. The aim of this study was to explore the potential mechanisms through which dexmedetomidine can induce Th1 cell differentiation. Purified CD4+ T cells were stimulated with anti-CD3/anti-CD28 and then treated with dexmedetomidine. Flow cytometry analysis was adopted to measure the concentration of Th1 cells. Enzyme-linked immunosorbent assay (ELISA) and real-time quantitative polymerase chain reaction (qPCR) were performed to detect protein levels and mRNA expression, respectively, of IFN-γ and IL-4. Western blotting was used to determine the phosphorylation of signal transducer and activator of transcription 1 (STAT1) and T-bet expression. The Th1 cell subset and IFN-γ levels were elevated in the dexmedetomidine-induced CD4+ T cells. Dexmedetomidine enhanced the phosphorylation of STAT1 and the expression of T-bet in the CD4+ T cells. Atipamezole (an α2 adrenergic antagonist) and fludarabine (a STAT1 inhibitor) reversed the dexmedetomidine-induced Th1 cell differentiation. These results suggested that dexmedetomidine induced Th1 cell differentiation via the STAT1-T-bet signaling pathway.

Recycling of mullite from high-alumina coal fly ash by a mechanochemical activation method: Effect of particle size and mechanism research.

High-alumina coal fly ash (HAFA) is a special solid waste since its alumina content can reach 40-50 wt%, which is seen as a potential resource for mullite material production. However, obtaining an ideal mullite material from HAFA is difficult because of its low Al2O3/SiO2 mass ratio. In this work, the microstructure characteristics of HAFA were systematically analyzed by combining multiple characterization techniques. It was found that HAFA had a core-shell structure with a mullite/corundum crystal core and a silica-rich amorphous phase shell. The novel mechanochemical activation-desilication process was used to remove amorphous phase from HAFA and elevate the Al2O3/SiO2 mass ratio. In particular, the effect of particle size after mechanical treatment and mechanism of the desilication process were extensively investigated. On decreasing the particle size, a high leaching rate of alumina was achieved during mechanochemical activation, thus generating a hydroxysodalite coating layer as desilication was suppressed, and the amorphous phase was effectively removed. The mineralogical phase of the desilicated HAFA is mainly mullite and corundum, and the Al2O3/SiO2 mass ratio was elevated from 1.29 to 3.02. Mullite refractory obtained from the desilicated HAFA exhibited excellent physical properties. This study provides insights into further high-valued utilization of HAFA.

Sodium Bicarbonate Sub-Diaphragmatic Irrigation Relieves Shoulder Pain After Total Laparoscopic Hysterectomy: A Randomized Controlled Trial.

STUDY OBJECTIVE: To determine whether sub-diaphragmatic irrigation with sodium bicarbonate would relieve post-laparoscopic shoulder pain (PLSP) after total laparoscopic hysterectomy. DESIGN: Randomized double-blinded trial. SETTING: Teaching hospital. PATIENTS: Seventy patients undergoing total laparoscopic hysterectomy (TLH) for benign indications. INTERVENTION: We randomly allocated patients to intervention or control groups where sodium bicarbonate containing flushing liquid or normal saline was irrigated sub-diaphragm before sewing. MEASUREMENT & MAIN RESULTS: The primary outcome was PLSP following surgery measured by a numerical rating scale (NRS) (0 = no pain; 10 = worst pain imaginable). Secondary outcomes were abdominal incisional and visceral pain, analgesic use, and sodium bicarbonate related side effects. The incidence of PLSP in intervention group was significantly lower than that in control group (P < 0.05). Contrarily, incisional and visceral pain was similar in both groups (P = 0.1). The consumption of rescue analgesics in the intervention group was lower than that in the control group. Side effects were comparable in both study groups. CONCLUSION: Sub-diaphragmatic irrigation with sodium bicarbonate could effectively reduce shoulder pain, but not abdominal incisional and visceral pain, in patients undergoing TLH without an increase in side effects. REGISTRATION INFORMATION: Clinical trial registry number: http://www.chictr.org.cn/ (ChiCTR2100041765). REGISTRATION FINDINGS: http://www.chictr.org.cn/showproj.aspx?proj=66721 Link to clinical trial page and data repository: http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=2992.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds.

In recent years, deep learning models have been widely used in the field of hyperspectral imaging. However, the training of deep learning models requires not only a large number of samples, but also the need to set too many hyper-parameters, which is time consuming and laborious for researchers. This study used hyperspectral imaging technology combined with a deep learning model suitable for small-scale sample data sets, deep forests (DF) model, to classify rice seeds with different degrees of frost damage. During the period, three spectral preprocessing methods (Savitzky-Golay first derivative (SG1), standard normal variate (SNV), and multivariate scatter correction (MSC)) were used to process the original spectral data, and three feature extraction algorithms (principal component analysis (PCA), successive projections algorithm (SPA), and neighborhood component analysis (NCA)) were used to extract the characteristic wavelengths. Moreover, DF model and three traditional machine learning models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were built based on different numbers of sample sets. After multivariate data analysis, it showed that the pretreatment effect of MSC was the most excellent, and the characteristic wavelength extracted by NCA algorithm was the most useful. In addition, the performance of DF model was better than these three traditional classifier models, and it still performed well in small-scale sample set data. Therefore, DF model was chosen as the best classification model. The results of this study show that the DF model maintains good classification performance in small-scale sample set data, and it has a good application prospect in hyperspectral imaging technology.

[Study of quantitative analysis of protein in barley using OSC-PLS algorithm].

Spectra of barley containing vast information were obtained with the dispersion spectrograph. The contents of protein in barley were determined by dispersive near infrared (NIR) spectroscopy. Pretreatment method of orthogonal signal correction (OSC) was used to reject uncorrelated variables in the original spectra before building the partial least squares NIR method (OSC-PLS). The results were compared with the regular PLS model. With the OSC-PLS method, the determination coefficient R2 was 0.901. The correlation coefficient of validation set was 0.971 7. The standard deviation (SD) and relative standard deviation (RSD)were 0.545 0 and 4.2% respectively. Applying OSC-PLS resulted in removal of non-correlated variation in spectra and reduced model's complexity with preserved ability and improved interpretative ability of variation in spectra. It means that the OSC-PLS is a fungible model to predict the contents of protein in barley veraciously to meet the demand of fast analysis of agricultural products.

Synthesis of porous carbon via a waste tire leavening strategy for adsorptive desulfurization.

Adsorptive desulfurization is an effective technology for removing harmful sulfur under mild conditions. Carbon materials have many advantages and are often used in adsorption desulfurization research, but until now have been synthesized using complicated methods and have shown limited adsorption capacity. Using an NaHCO3-assisted leavening method, waste tires were in the current work used as raw materials to produce hierarchically porous carbon that exhibits a high specific surface area and abundant oxygen-containing functional groups. In contrast to the sulfur removal by the carbon material prepared using a commonly used method, the as-prepared carbon material shows excellent adsorption performance, and was able to achieve an ultra-deep desulfurization of pentanethiol, specifically removing up to 99.7% of the sulfur from a model fuel with an initial sulfur concentration of 28 ppm. Therefore, we have provided a simple method for synthesizing adsorbents with high adsorption performance, and we expect these adsorbents to be used for industrial adsorptive desulfurization.