Effect of Yuanbao Maple Tea Powder with High Chlorogenic Acid Content on Bread Quality

Using Yuanbao maple leaves as raw materials, the extraction process of chlorogenic acid in leaves was optimized, and single-factor and orthogonal experiments were carried out on ultrasonic temperature, time, and solid-liquid ratio through ultrasonic extraction. The results showed that the optimal level of the experiment was when the ratio of solid to liquid was 16:1, the concentration of ethanol was 60%, and the ultrasonic time was 15 min, and the extraction amount was 6.86% (mass fraction). Under the optimal extraction process conditions, the dynamic content of chlorogenic acid in the growth cycle of Yuanbaofeng in 2020 was analyzed. The results showed that the content of chlorogenic acid in the leaves of Yuanbaofeng in June was the highest, and the content in September was the least. In order to further explore the effect of Yuanbao maple tea powder on bread quality, different proportions of Yuanbao maple tea powder were added to bread to study its sensory effects on bread. The effects of scores, moisture content, texture, polyphenol content, antioxidant activity and other qualities. The results show that the water holding capacity, elasticity and anti-oxidation of bread are the best when the addition amount of GTB is 0.5%. Less elastic, more difficult to chew, and gradually unstable antioxidant properties

Highly selective fluorescent chemosensor for Zn2+ derived from inorganic-organic hybrid magnetic core/shell Fe3O4@SiO2 nanoparticles

Magnetic nanoparticles with attractive optical properties have been proposed for applications in such areas as separation and magnetic resonance imaging. In this paper, a simple and novel fluorescent sensor of Zn2+ was designed with 3,5-di-tert-butyl-2-hydroxybenzaldehyde [DTH] covalently grafted onto the surface of magnetic core/shell Fe3O4@SiO2 nanoparticles [NPs] (DTH-Fe3O4@SiO2 NPs) using the silanol hydrolysis approach. The DTH-Fe3O4@SiO2 inorganic-organic hybrid material was characterized by transmission electron microscopy, dynamic light scattering, X-ray power diffraction, diffuse reflectance infrared Fourier transform, UV-visible absorption and emission spectrometry. The compound DTH exhibited fluorescence response towards Zn2+ and Mg2+ ions, but the DTH-Fe3O4@SiO2 NPs only effectively recognized Zn2+ ion by significant fluorescent enhancement in the presence of various ions, which is due to the restriction of the N-C rotation of DTH-Fe3O4@SiO2 NPs and the formation of the rigid plane with conjugation when the DTH-Fe3O4@SiO2 is coordinated with Zn2+. Moreover, this DTH-Fe3O4@SiO2 fluorescent chemosensor also displayed superparamagnetic properties, and thus, it can be recycled by magnetic attraction

Multiple stellar populations at less evolved stages: detection of chemical variations among main-sequence dwarfs in NGC 1978

Multiple stellar populations (MPs) with different chemical compositions are not exclusive features of old GCs (older than 10 Gyr). Indeed, recent studies reveal that younger clusters ($\sim$2--6 Gyr-old) in the Magellanic Clouds also exhibit star-to-star chemical variations among evolved stars. However, whether MPs are present among less evolved dwarfs of these intermediate-age clusters is still unclear. In this work, we search for chemical variations among GK-type dwarfs in the $\sim$2 Gyr-old cluster NGC 1978, which is the youngest cluster with MPs. We exploit deep ultraviolet and visual observations from the Hubble Space Telescope to constrain the nitrogen (N) and oxygen (O) variations among MS stars. To do this, we compare appropriate photometric diagrams that are sensitive to N and O with synthetic diagrams of simple stellar populations and MPs. We conclude that the G- and K-type MS stars in NGC\,1978 host MPs. Our statistical analysis shows that the fraction of N-rich stars ranges from $\sim$40\% to $\sim$80\%, depending on the detailed distributions of nitrogen and oxygen.Comment: 16 pages, 10 figures, ApJ accepte

Childhood abuse and association with adult depressive symptoms among people with cardiovascular disease

BackgroundTo study the association between the total/different types of childhood abuse and adult depressive symptoms in people with cardiovascular disease (CVD).MethodsThe subjects were people with CVD who continuously participated in the China Health and Retirement Longitudinal Study (CHARLS) life history survey and the 2018 wave of the CHARLS national baseline Survey. Multi-level logistic regression models were used to analyze the relationship between emotional neglect, physical neglect, physical abuse and adult depressive symptoms.ResultsA total of 4,823 respondents were included in this study. The incidence of childhood abuse (existed emotional neglect, physical neglect or physical abuse) was 43.58% among people over 45 years old with CVD, which was higher than that of the general population (36.62%, p < 0.05). Adjusted model showed that overall childhood abuse was associated with adult depressive symptoms (OR = 1.230, 95%CI:1.094–1.383). Among different types of childhood abuse, only physical abuse was associated with depressive symptoms in adulthood (OR = 1.345, 95%CI:1.184–1.528).ConclusionCompared with that of the general population, the incidence of childhood abuse in CVD population is higher. Physical abuse in childhood increased the risk of depressive symptoms in adulthood. It suggested that the occurrence of depressive symptoms was the result of related factors in the whole life course. In order to prevent the depressive symptoms, childhood abuse also needs to be considered. It is very important to identify and prevent the continuation of childhood abuse in time

Segmentation of Kidney and Renal Tumor in CT Scans Using Convolutional Networks

Accurate segmentation of kidney and renal tumor in CT images is a prerequisite step in surgery planning. However, this task remains a challenge. In this report, we use convolutional networks (ConvNet) to automatically segment kidney and renal tumor. Specifically, we adopt a 2D ConvNet to select a range of slices to be segmented in the inference phase for accelerating segmentation, while a 3D ConvNet is trained to segment regions of interest in the above narrow range. In localization phase, CT images from several publicly available datasets were used for learning localizer. This localizer aims to filter out slices impossible containing kidney and renal tumor, and it was fine-tuned from AlexNet pre-trained on ImageNet. In segmentation phase, a simple U-net with large patch size (160×160×80) was trained to delineate contours of kidney and renal tumor. In the 2019 MICCAI Kidney Tumor Segmentation (KiTS19) Challenge, 5-fold cross-validation was performed on the training set. 168 (80%) CT scans were used for training and remaining 42 (20%) cases were used for validation. The resulting average Dice similarity coefficients are 0.9662 and 0.7905 for kidney and renal tumor, respectively

Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods

RNA N6-methyladenosine (m6A) has emerged as an important epigenetic modification for its role in regulating the stability, structure, processing, and translation of RNA. Instability of m6A homeostasis may result in flaws in stem cell regulation, decrease in fertility, and risk of cancer. To this day, experimental detection and quantification of RNA m6A modification are still time-consuming and labor-intensive. There is only a limited number of epitranscriptome samples in existing databases, and a matched RNA methylation profile is not often available for a biological problem of interests. As gene expression data are usually readily available for most biological problems, it could be appealing if we can estimate the RNA methylation status from gene expression data using in silico methods. In this study, we explored the possibility of computational prediction of RNA methylation status from gene expression data using classification and regression methods based on mouse RNA methylation data collected from 73 experimental conditions. Elastic Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and Random Forests (RF) were constructed for classification. Both SVM and RF achieved the best performance with the mean area under the curve (AUC) = 0.84 across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was conducted on those sites selected by ENLR as predictors to access the biological significance of the model. Three functional annotation terms were found statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and endoplasmic reticulum. All 3 terms were found to be closely related to m6A pathway. For regression analysis, Elastic Net was implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a mean Spearman correlation coefficient = 0.64. Our exploratory study suggested that gene expression data could be used to construct predictors for m6A methylation status with adequate accuracy. Our work showed for the first time that RNA methylation status may be predicted from the matched gene expression data. This finding may facilitate RNA modification research in various biological contexts when a matched RNA methylation profile is not available, especially in the very early stage of the study

DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network

Recent studies have revealed that the RNA N6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum