[Determination of twelve halobenzoquinones in drinking water by solid phase extraction-ultra-performance liquid chromatography-tandem mass spectrometry].

OBJECTIVE: To establish a method for twelve halobenzoquinones(HBQs) in drinking water by solid phase extraction-ultra-performance liquid chromatography coupled with electrospray-tandem mass spectrometry(SPE-UPLC-MS/MS). METHODS: The drinking water was acidified with formic acid and concentrated by Bond Elut Plexa solid phase extraction column. The sample solution was separated using Waters ACQUITY HSS T3 column(100 mm×2.1 mm, 1.8 µm) with gradient elution using methanol-water containing 0.1% formic acid as mobile phase. The target compouds were detected in negtive electrospray ionization(ESI~-) and multiple reaction monitoring. RESULTS: The concentration of twelve HBQs showed good linearity in the range 5.0-150.0 ng/mL, respectively, with the correlation coefficients greater than 0.999. The limits of detection(LOD) of twelve HBQs were lower than 2.0 ng/mL, and the limits of quantification(LOQ) for twelve HBQs were lower than 5.0 ng/mL, respectively. The recoveries of three levels in the matrix were 70.0%-84.0%. The matrix effffect was 0.08-0.64. CONCLUSION: The SPE-UPLC-MS/MS method has high sensitivity, good accuracy and fast analysis speed for the detection of halobenzoquinones in drinking water.

Pro-Inflammatory Diet as a Risk Factor for Stomach Cancer: Findings from a Multicenter Study in Central and Western China.

Purpose: We conducted a multicenter cross-sectional study in central and western China to explore the association between inflammatory diet and stomach cancer odds. Patients and Methods: Participants from five hospitals in the central and western regions were collected. All participants completed the questionnaire we provided before the gastroscopy examination, which includes inquiries about risk factors for stomach cancer and food frequency. All participants underwent gastroscopy, and a mucosal biopsy was confirmed pathologically. Pathological findings were classified as chronic gastritis group, precancerous lesions group and stomach cancer group. Dietary Inflammatory Index (DII) scores were calculated based on the frequency of food occurrences in the questionnaire, and finally SPSS was used to calculate the correlation between variables. Results: A total of 1162 patients were included in this study, including 668 cases of chronic gastritis, 411 cases of precancerous lesions, and 83 cases of cancer. A single factor analysis was conducted to examine the risk factors of stomach cancer, revealing a significant association between a pro-inflammatory diet and the stomach cancer odds (p value < 0.05). The results of binary classification analysis further confirmed that a pro-inflammatory diet is a risk factor for stomach cancer 【odds ratio (OR) =7.400)】. Moreover, correlation analysis demonstrated a positive correlation between the severity of gastric mucosal diseases and an inflammatory diet (including anti-inflammatory and pro-inflammatory diets) (rs=0.274, p-value < 0.001). Conclusion: Pro-Inflammatory diet is a risk factor for stomach cancer, and may accelerate the progression of stomach mucosal disease.

[Determination of beauvercin and enniatins in rice flour and wheat flour by solid phase extraction-ultra performance liquid chromatography-tandem mass spectrometry].

OBJECTIVE: To develop a method for the determination of beauvercin(BEA), enniatin A(ENNA), enniatin A1(ENNA1), enniatin B(ENNB) and enniatin B1(ENNB1) in rice flour and wheat flour by ultra performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS). METHODS: Samples were extracted by acetonitrile-water, purified by Oasis Prime HLB solid-phase extraction column. The sample solution was separated by waters BEH C_(18) column(2.1 mm×100 mm, 1.8 µm). The detection was performed in the electrospray positive ionization(ESI+) under multiple reaction monitoring(MRM) mode. The internal standard method and the matrix-matched calibrations were used for quantification. RESULTS: The linear relationships of BEA and 4 kinds of enniatins(ENNs) were good in the range of 0.1-50.0 ng/mL(r>0.999). The average recoveries of BEA and ENNs in rice flour and wheat flour were 96.4%-105.4% and 99.1%-109.2%, with the relative standard deviations(RSD) of 1.01%-7.42% and 1.09%-9.69%(n=6). The detection limits(LOD) of BEA and ENNs were 0.03 µg/kg. The quantitative limits(LOQ) of BEA and ENNs were 0.1µg/kg. The matrix induced suppression or enhancement effect were 72.7%-99.3% and 60.8%-100.4%, respectively. The levels of emerging BEA and ENNs in wheat flour were higher than rice flour. The detection rate of enniatin B was highest in wheat flour and rice flour, the contents were 0.03-9.57 µg/kg and 0.03-0.56 µg/kg, the positive percentage were 98.5% and 36.4%. CONCLUSION: The method is quick, easy, accurate and sensitive, which is suitable for the determination of BEA and 4 kinds of ENNs in rice flour and wheat flour.

Metabolomics-based study on the changes of endogenous metabolites during adventitious bud formation from leaf margin of Bryophyllum pinnatum (Lam.) Oken.

Bryophyllum pinnatum (Lam.) Oken is an ornamental and ethno-medicine plant, which can grow a circle of adventitious bud around the leaf margin. The dynamic change of metabolites during the development of B. pinnatum remains poorly understood. Here, leaves from B. pinnatum at four developmental stages were sampled based on morphological characteristics. A non-targeted metabolomics approach was used to evaluate the changes of endogenous metabolites during adventitious bud formation in B. pinnatum. The results showed that differential metabolites were mainly enriched in sphingolipid metabolism, flavone and flavonol biosynthesis, phenylalanine metabolism, and tricarboxylic acid cycle pathway. The metabolites assigned to amino acids, flavonoids, sphingolipids, and the plant hormone jasmonic acid decreased from period Ⅰ to Ⅱ, and then increased from period Ⅲ to Ⅳ with the emergence of adventitious bud (period Ⅲ). While the metabolites related to the tricarboxylic acid cycle showed a trend of first increasing and then decreasing during the four observation periods. Depending on the metabolite changes, leaves may provide conditions similar to in vitro culture for adventitious bud to occur, thus enabling adventitious bud to grow at the leaf edge. Our results provide a basis for illustrating the regulatory mechanisms of adventitious bud in B. pinnatum.

Chemical composition and therapeutic mechanism of Xuanbai Chengqi Decoction in the treatment of COVID-19 by network pharmacology, molecular docking and molecular dynamic analysis.

Xuanbai Chengqi Decoction (XBCQD), a classic traditional Chinese medicine, has been widely used to treat COVID-19 in China with remarkable curative effect. However, the chemical composition and potential therapeutic mechanism is still unknown. Here, we used multiple open-source databases and literature mining to select compounds and potential targets for XBCQD. The COVID-19 related targets were collected from GeneCards and NCBI gene databases. After identifying putative targets of XBCQD for the treatment of COVID-19, PPI network was constructed by STRING database. The hub targets were extracted by Cytoscape 3.7.2 and MCODE analysis was carried out to extract modules in the PPI network. R 3.6.3 was used for GO enrichment and KEGG pathway analysis. The effective compounds were obtained via network pharmacology and bioinformatics analysis. Drug-likeness analysis and ADMET assessments were performed to select core compounds. Moreover, interactions between core compounds and hub targets were investigated through molecular docking, molecular dynamic (MD) simulations and MM-PBSA calculations. As a result, we collected 638 targets from 61 compounds of XBCQD and 845 COVID-19 related targets, of which 79 were putative targets. Based on the bioinformatics analysis, 10 core compounds and 34 hub targets of XBCQD for the treatment of COVID-19 were successfully screened. The enrichment analysis of GO and KEGG indicated that XBCQD mainly exerted therapeutic effects on COVID-19 by regulating signal pathways related to viral infection and inflammatory response. Meanwhile, the results of molecular docking showed that there was a stable binding between the core compounds and hub targets. Moreover, MD simulations and MM-PBSA analyses revealed that these compounds exhibited stable conformations and interacted well with hub targets during the simulations. In conclusion, our research comprehensively explained the multi-component, multi-target, and multi-pathway intervention mechanism of XBCQD in the treatment of COVID-19, which provided evidence and new insights for further research.

Identifying Diagnostic and Prognostic Differentially Expressed Genes of Gastric Cancer Based on Bioinformatics Analyses of RNA-seq Data.

Background: The abnormal expression of genes in serum may be associated with early diagnosis of patients with malignant tumors. This study was designed to screen for significantly differentially expressed genes (DEGs) that may be associated with gastric cancer using bioinformatic methods. Methods: RNA-seq data from gastric cancers were downloaded from the TCGA and GEO databases, and 1903 secretory genes were downloaded from the HPA database. The diagnostic secretory RNAs of gastric cancer were screened using least absolute shrinkage and selection operator regression analysis. Univariate Cox regression analysis was used to evaluate the prognostic significance of the results. Biological functions were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Then, 640 cases of gastric cancer and paracancerous tissues were collected, and immunohistochemistry (IHC) was used to detect the expression of COL4A1. Results: In total, 25 upregulated differentially expressed genes (DEGs) were identified, which were secreted mainly in the blood and cell matrices. Six secretory genes (OLFM4, CEMIP, APOC1, CST1, COL4A1, and CD55) with diagnostic significance were identified, and the enrichment scores of these six genes were significantly associated with tumor stage. In addition, we found that increased COL4A1 expression might be associated with a poor prognosis in patients with gastric cancer. Based on GO and KEGG analyses, we found COL4A1-related DEGs were mainly enriched in connective tissue development, collagen fibrous tissue-related processes, extracellular structure, extracellular matrix (ECM) tissue, and related to the ECM receptor-related pathway, focal adhesion, and PI3K-Akt signaling pathway. Moreover, the results of immunohistochemical analyses showed that the COL4A1 protein level in gastric cancers was also higher than in the matched paracancerous tissues. Conclusions: In this study, we found six upregulated secretory genes, including OLFM4, CEMIP, APOC1, CST1, COL4A1, and CD55 which we hypothesized to be significant DEGs for the diagnosis of gastric cancer. Our data also suggest that COL4A1 may play an important role in the diagnosis and prognosis of gastric cancer.

Network pharmacology of iridoid glycosides from Eucommia ulmoides Oliver against osteoporosis.

Eucommia ulmoides Oliver is one of the commonly used traditional Chinese medicines for the treatment of osteoporosis, and iridoid glycosides are considered to be its active ingredients against osteoporosis. This study aims to clarify the chemical components and molecular mechanism of iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis by integrating network pharmacology and molecular simulations. The active iridoid glycosides and their potential targets were retrieved from text mining as well as Swiss Target Prediction, TargetNet database, and STITCH databases. At the same time, DisGeNET, GeneCards, and Therapeutic Target Database were used to search for the targets associated with osteoporosis. A protein-protein interaction network was built to analyze the interactions between targets. Then, DAVID bioinformatics resources and R 3.6.3 project were used to carry out Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Moreover, interactions between active compounds and potential targets were investigated through molecular docking, molecular dynamic simulation, and binding free energy analysis. The results showed that a total of 12 iridoid glycosides were identified as the active iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis. Among them, aucubin, reptoside, geniposide and ajugoside were the core compounds. The enrichment analysis suggested iridoid glycosides of Eucommia ulmoides Oliver prevented osteoporosis mainly through PI3K-Akt signaling pathway, MAPK signaling pathway and Estrogen signaling pathway. Molecular docking results indicated that the 12 iridoid glycosides had good binding ability with 25 hub target proteins, which played a critical role in the treatment of osteoporosis. Molecular dynamic and molecular mechanics Poisson-Boltzmann surface area results revealed these compounds showed stable binding to the active sites of the target proteins during the simulations. In conclusion, our research demonstrated that iridoid glycosides of Eucommia ulmoides Oliver in the treatment of osteoporosis involved a multi-component, multi-target and multi-pathway mechanism, which provided new suggestions and theoretical support for treating osteoporosis.

Exploring the potential therapeutic effect of Eucommia ulmoides-Dipsaci Radix herbal pair on osteoporosis based on network pharmacology and molecular docking technology.

Eucommia ulmoides-Dipsaci Radix (EU-DR) is a commonly used herbal pair for the treatment of osteoporosis (OP) in China. The purpose of this study was to investigate the potential mechanism of EU-DR on OP through network pharmacology and molecular docking approaches. Combining data from multiple open-source databases and literature mining, the active compounds and potential targets of EU-DR were screened out. The OP related targets were identified from the interactive web tool GEO2R. The shared targets were obtained by intersecting the targets of EU-DR and OP. The protein-protein interaction (PPI) network was constructed via the STRING database and Cytoscape 3.7.2 software. GO and KEGG enrichment analysis were conducted using R 3.6.3 software with adjusted p-value < 0.05. Sybyl-x 2.1.1 and Autodock Vina 1.1.2 software were used to cross validate the affinity between active compounds and target proteins. Our results showed that a total of 50 active compounds were screened, corresponding to 895 EU-DR targets, 2202 OP targets and 144 shared targets. The flavonoids in EU-DR played an important role in anti-OP. The enrichment analysis of GO and KEGG suggested EU-DR exerted a therapeutic effect on OP mainly by regulating the osteoclast differentiation related signaling pathway. Meanwhile, molecular docking results showed that most active compounds in EU-DR had strong binding efficiency to the target proteins. In conclusion, this study elaborated the multi-component, multi-target, and multi-pathway interaction mechanism of the EU-DR herbal pair in the treatment of OP for the first time, which also provided a pharmacological basis for treating OP.

SOSTDC1 acts as a tumor inhibitor in acute myeloid leukemia by downregulating the Wnt/ß-catenin pathway.

Sclerostin domain-containing 1 (SOSTDC1) has been documented as a key tumor-associated protein that is differentially expressed in multiple malignancies. However, the function of SOSTDC1 in acute myeloid leukemia (AML) is unexplored. The goal of this work was to assess the possible role of SOSTDC1 in AML. Our data showed decreased SOSTDC1 level in bone marrow from AML patients, and patients with low levels of SOSTDC1 had a reduced survival rate. SOSTC1 upregulation restrained the proliferative ability and promoted the apoptotic rate of AML cells. SOSTDC1 suppressed the activation of the Wnt/ß-catenin pathway in AML cells. Reactivation of the Wnt/ß-catenin pathway reversed SOSTDC1-mediated antitumor effects. SOSTDC1 upregulation weakened the tumorigenicity of AML cells in vivo. Collectively, our work demonstrates that SOSTDC1 has a tumor-inhibiting role in AML via downregulation of the Wnt/ß-catenin pathway. This work underscores a key function for the SOSTDC1/Wnt/ß-catenin pathway in AML.

The Protective Effect of E. faecium on S. typhimurium Infection Induced Damage to Intestinal Mucosa.

Intensive farming is prone to induce large-scale outbreaks of infectious diseases, with increasing use of antibiotics, which deviate from the demand of organic farming. The high mortality rate of chickens infected with Salmonella caused huge economic losses; therefore, the promising safe prevention and treatment measures of Salmonella are in urgent need, such as probiotics. Probiotics are becoming an ideal alternative treatment option besides antibiotics, but the effective chicken probiotic strains with clear protective mechanism against Salmonella remain unclear. In this study, we found Enterococcus faecium YQH2 was effective in preventing Salmonella typhimurium infection in chickens. Salmonella typhimurium induced the loss of body weight, and liver and intestinal morphology damage. The inflammatory factor levels increased and intestinal proliferation inhibited. However, after treatment with Enterococcus faecium YQH2, broilers grew normally, the pathological changes of liver and intestine were reduced, and the colonization of Salmonella in the intestine was improved. Not only that, the length of villi and the depth of crypts were relatively normal, and the levels of inflammatory factors such as IL-1ß, TNF-α, and IL-8 were reduced. The number of PCNA cells of Enterococcus faecium YQH2 returned to normal under the action of Salmonella typhimurium infection, which was conducive to the normal proliferation of intestinal epithelial cells. The protective effect of Enterococcus faecium YQH2 may be due to the attribution to the activation of hypoxia and then induced the proliferation of intestinal stem cells to repair the damage of intestinal mucosa under Salmonella typhimurium infection. This study demonstrated that Enterococcus faecium YQH2 was effective in preventing Salmonella typhimurium infection, which could be further used in the chicken health breeding.

Art Teachers' Attitudes Toward Online Learning: An Empirical Study Using Self Determination Theory.

The pandemic in 2020 made online learning the widely used modality of teaching in several countries and it has also entered the spotlight of educational research. However, online learning has always been a challenge for disciplines (engineering, biology, and art) that require hands-on practice. For art teaching or training, online learning has many advantages and disadvantages. How art teachers embrace and adapt their teaching for online delivery remains an unanswered question. This research examines 892 art teachers' attitudes toward online learning, using learning environment, need satisfaction, mental engagement, and behavior as predictors. Structural equation modeling was used to explore the relationship between these four dimensions during these teachers' participation in an online learning program. The results reveal significant correlations between the learning environment, need satisfaction, mental engagement, and behavior. Moreover, this study reveals the group characteristics of art teachers, which can actually be supported by online learning programs. These findings provide insights into how art teachers view and use online learning, and thus can shed lights on their professional development.

High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning.

BACKGROUND: Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step. RESULTS: We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset. CONCLUSION: The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.

FAM84B acts as a tumor promoter in human glioma via affecting the Akt/GSK-3ß/ß-catenin pathway.

Family with sequence similarity 84, member B (FAM84B) has recently emerged as an oncoprotein in multiple types of cancer. However, whether FAM84B modulates the progression of glioma has not been determined. The goals of this work were to assess the possible relationship between FAM84B and glioma. Our data revealed high FAM84B level in glioma specimens and exhibited that the overexpression of FAM84B was correlated with a low survival rate in glioma patients. Cellular functional assays showed that silencing of FAM84B prohibited the proliferation and invasion, and induced the apoptosis of glioma cells. Further results determined that the knockdown of FAM84B remarkably decreased the levels of phosphorylated Akt and glycogen synthase kinase (GSK)-3ß, and active ß-catenin. Inhibition of Akt abolished the FAM84B-mediated promotion effects on Wnt/ß-catenin pathway. The subcutaneous xenograft assay confirmed that the silencing of FAM84B significantly prohibited the tumorigenicity of glioma cells in vivo. Collectively, the findings from this work demonstrate that the downregulation of FAM84B exhibits a cancer-suppressive role in human glioma through the regulation of Akt/GSK-3ß/ß-catenin pathway.

[Nutritional composition in different processed wheats from Shaanxi Province].

OBJECTIVE: To study the content of nutritive ingredients of 11 kinds of different processed wheats planted in Shaanxi Province, and assess their nutritional value. METHODS: The macronutrients, moisture, ash and vitamins in 11 different wheat were determined. The index of nutritional quality(INQ) method was used to evaluate the different nutrients in 11 kinds of wheat and the fuzzy membership function method was used to evaluate the nutritional value comprehensively. RESULTS: The contents of water and carbohydrate in 11 kinds of whole wheat flour were lower than those of special flour and wheat core flour. The contents of ash, fat, protein and total dietary fiber were significantly higher than those of special flour and wheat core flour. The wheat flour contained high levels of vitamin B_1, certain vitamin B_2 and trace amount of ß-carotene. Under the parameters selected in this article, the comprehensive evaluation shows that Jinmai 54 had the highest nutritive value among 11 kinds of wheat, while Zhoumai 26 had the lowest nutritive value. CONCLUSION: 11 kinds of wheat is rich in protein, vitamins and other nutrients, peeling can cause a large loss of vitamin B and vitamin E in wheat flour. The comprehensive nutritional value of whole wheat flour is higher than the special flour and wheat core powder.

A smart diagnostic tool based on deep kernel learning for on-site determination of phosphate, calcium, and magnesium concentration in a hydroponic system.

Calcium, phosphate, and magnesium are essential nutrients for plant growth. The in situ determination of these nutrients is an important task for monitoring them in a closed hydroponic system where the nutrient elements need to be individually quantified based on ion-selective electrode (ISE) sensing. The accuracy issue of calcium ISEs due to interference, drift, and ionic strength, and the unavailability of phosphate and magnesium ISEs makes the development of these ion detecting tools hard to set up in a hydroponic system. This study modeled and evaluated a smart tool for recognising three ions (calcium, phosphate, and magnesium) based on the automatic multivariate standard addition method (AMSAM) and deep kernel learning (DKL) model. The purpose was to improve the accuracy of calcium ISEs, determining phosphate through cobalt electrochemistry, and soft sensing of magnesium ions. The model provided better performance in on-site detecting and measuring those ions in a lettuce hydroponic system achieving root mean square errors (RMSEs) of 12.5, 12.1, and 7.5 mg L-1 with coefficients of variation (CVs) below 5.0%, 7.0%, and 10% for determining Ca2+, H2PO4 -, and Mg2+ in the range of 150-250, 100-200, and 20-70 mg L-1 respectively. Furthermore, the DKL was implemented for the first time in the third platform (LabVIEW) and deployed to determine three ions in a real on-site hydroponic system. The open architecture of the SDT allowed posting the measured results on a cloud computer. This would help growers monitor their plants' nutrients conveniently. The informative data about the three mentioned ions that have no commercial sensors so far, could be adapted to the other components to develop a fully automated fertigation system for hydroponic production.

LncRNA SNHG1 contributes to the regulation of acute myeloid leukemia cell growth by modulating miR-489-3p/SOX12/Wnt/ß-catenin signaling.

The long noncoding RNA (lncRNA) small nucleolar RNA host gene 1 (SNHG1) is a critical regulator for the development and progression of multiple tumors. Yet, the role of SNHG1 in acute myeloid leukemia (AML) is unknown. The present study demonstrated that SNHG1 expression was upregulated in AML. SNHG1 silencing markedly repressed AML cell growth, whereas SNHG1 overexpression had the opposite effect. MicroRNA-489-3p (miR-489-3p) was identified as a SNHG1-targeting miRNA. SNHG1 knockdown increased miR-489-3p expression. Low expression of miR-489-3p was correlated with high expression of SNHG1 in AML tissues. miR-489-3p overexpression restricted AML cell growth, and SRY-related high-mobility-group box 12 (SOX12) was identified as a miR-489-3p-targeting gene. SNHG1 inhibition or miR-489-3p overexpression inactivated Wnt/ß-catenin signaling through downregulation of SOX12. SOX12 overexpression partially reversed the SNHG1 knockdown- or miR-489-3p overexpression-mediated effects. Taken together, these data indicate that suppression of SNHG1 downregulates AML cell growth by inactivating SOX12/Wnt/ß-catenin signaling via upregulating miR-489-3p.

Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy.

Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 × 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.

Functions of Small Organic Compounds that Mimic the HNK-1 Glycan.

Because of the importance of the HNK-1 carbohydrate for preferential motor reinnervation after injury of the femoral nerve in mammals, we screened NIH Clinical Collection 1 and 2 Libraries and a Natural Product library comprising small organic compounds for identification of pharmacologically useful reagents. The reason for this attempt was to obviate the difficult chemical synthesis of the HNK-1 carbohydrate and its isolation from natural sources, with the hope to render such compounds clinically useful. We identified six compounds that enhanced neurite outgrowth from cultured spinal motor neurons at nM concentrations and increased their neurite diameter, but not their neurite branch points. Axons of dorsal root ganglion neurons did not respond to these compounds, a feature that is in agreement with their biological role after injury. We refer to the positive functions of some of these compounds in animal models of injury and delineate the intracellular signaling responses elicited by application of compounds to cultured murine central nervous system neurons. Altogether, these results point to the potential of the HNK-1 carbohydrate mimetics in clinically-oriented settings.

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution.

Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including NO3-, NH4+, K+, Ca2+, Na+, Cl-, H2PO4-, and Mg2+. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM-feature enrichment (FE)-DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 mg·L-1 with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5-275 mg·L-1 and 10-80 mg·L-1 had RMSEs of 29.6 and 8.7 mg·L-1 respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.

Flubendazole elicits anti-cancer effects via targeting EVA1A-modulated autophagy and apoptosis in Triple-negative Breast Cancer.

Background: Triple-negative breast cancer (TNBC) is one of the most prevalent neoplastic diseases worldwide, but efficacious treatments for this pathological condition are still challenging. The lack of an effective targeted therapy also leads to a poor prognosis for patients affected by TNBC. In the present study, we repurposed the distinctive inhibitory effects of flubendazole, a traditional anthelmintic drug, towards the putative modulation of proliferation and migration of TNBC in vitro and in vivo. Methods: According to a series of experimental approaches, including immunofluorescence (IF), immunoblotting (IB), siRNA and GFP-mRFP-LC3 plasmid transfection, respectively, we have found that flubendazole is capable of inducing autophagic cell death and apoptosis, thus exerting some anti-proliferative and anti-migration activity in TNBC cells. The therapeutic effects of flubendazole were evaluated by xenograft mouse models, followed by immunohistochemistry (IHC), IF and IB. Changes in the gene expression profiles of flubendazole-treated TNBC cells were analyzed by RNA sequencing (RNA-seq) and validated by IB. The potential binding mode of flubendazole and EVA1A was predicted by molecular docking and demonstrated by site-directed mutagenesis. Results: We have presently found that flubendazole exhibits a considerable anti-proliferative activity in vitro and in vivo. Mechanistically, the induction of autophagic cell death appears to be pivotal for flubendazole-mediated growth inhibition of TNBC cells, whereas blocking autophagy was able to improve the survival rate and migration ability of flubendazole-treated TNBC cells. Specifically, RNA-seq analysis showed that flubendazole treatment could promote the up-regulation of EVA1A. Flubendazole may regulate autophagy and apoptosis by targeting EVA1A, thus affecting the mechanisms of TNBC proliferation and migration. Furthermore, Thr113 may be the key amino acid residues for the binding of flubendazole to EVA1A. Conclusion: Our results provide novel insights towards the putative anti-cancer efficacy of flubendazole. Furthermore, here we show that flubendazole could serve as a potential therapeutic drug in TNBC. Altogether, this study highlights the possibility of this repurposed autophagic inducer for future cancer treatments.