Estimation of soil water and organic matter content in medium and low yield fields of Ningxia Yellow River Irrigation area based on hyperspectral information.

Accurately obtaining soil water and organic matter content is of great significance for improving soil qua-lity in croplands with medium to low yield. We explored the estimation effect of fractional order differentiation (FOD) combined with different spectral indices on soil water and organic matter content in medium and low yield croplands of Ningxia Yellow River Irrigation Area. After root mean square transformation of field measured hyperspectral reflectance, we used 0-2 FOD (with a step length of 0.25) to construct difference index (DI), ratio index (RI), product index (PI), sum index (SI), generalized difference index (GDI), and nitrogen planar domain index (NPDI) and to select the optimal spectral index based on the correlation coefficients between six spectral indices with soil water and organic matter contents. We constructed a model for estimating soil water and organic matter content based on partial least squares regression (PLSR) and support vector machine (SVM). The results showed that the correlation between soil water and organic matter content and spectral information was effectively improved after FOD transformation compared with the original spectrum, with maximum increases of 0.1785 and 0.1713, respectively. The soil water content sensitive bands were mainly in the range of 400-630 and 1350-1940 nm, while the sensitive bands of organic matter content were mainly at 460-850, 1530-1910, and 2060-2310 nm. The accuracy of SVM model was significantly higher than that of PLSR, and the soil water content estimation model based on 1.75-order NPDI-SVM reached the highest precision, with a validation determination coefficient (Rp2) of 0.970, root mean square error (RMSE) of 1.615, and relative percent deviation (RPD) of 4.211. The organic matter content estimation model based on 0.5 order DI-SVM had the best performance, with Rp2, RMSE and RPD of 0.983, 0.701 and 5.307, respectively. Our results could provide data and technological support for soil water and nutrient monitoring, quality improvement, and graphics creating in similar area with medium to low yield fields.

Inversion of soil water and salt information based on UAV hyperspectral remote sensing and machine lear-ning.

Accurate diagnosis of water and salt information in saline agricultural lands is crucial for long-term soil quality improvement and arable land conservation. In this study, we extracted field-scale vegetation canopy spectral information by UAV hyperspectral information, transforming the reflectance (R) to standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative of reflectance (FDR) and second derivative of reflectance (SDR). We determined the optimal spectral transformation forms of soil water content (SWC), soil pH, and soil salt content (SSC) by the maximum absolute correlation coefficient (MACC), and extracted the feature bands by competitive adaptive reweighted sampling (CARS). We constructed an inversion model of soil water and salt information by partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that R, FDR and MSC were the best spectral transformation types for soil water content, soil pH, and soil salt content, and the corresponding MACC were 0.730, 0.472 and 0.654, respectively. The CARS algorithm effectively eliminated the irrelevant variables, optimally selecting 16-17 feature bands from 150 spectral bands. Both soil water content and soil pH performed best with XGBoost model, achieving determination coefficient of validation (Rp2) 0.927 and 0.743, and the relative percentage difference (RPD) amounted to 3.93 and 2.45. For soil salt content, the RF model emerged as the best inversion method with Rp2 and RPD of 0.427 and 1.64, respectively. The study could provide a reference solution for the integrated remote sensing monitoring of soil water and salt information in space and sky, serving as a scientific guide for the amelioration and sustainable management of saline lands.

Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma.

Background: This study aimed to examine glycolysis/gluconeogenesis-related genes in hepatocellular carcinoma (HCC) and evaluate their potential roles in HCC progression and immunotherapy response. Methods: Data analyzed in this study were collected from GSE14520, GSE76427, GSE174570, The Cancer Genome Atlas (TCGA), PXD006512, and GSE149614 datasets, metabolic pathways were collected from MSigDB database. Differentially expressed genes (DEGs) were identified between HCC and controls. Differentially expressed glycolysis/gluconeogenesis-related genes (candidate genes) were obtained and consensus clustering was performed based on the expression of candidate genes. Bioinformatics analysis was used to evaluate candidate genes and screen prognostic genes. Finally, the key results were tested in HCC patients. Results: Thirteen differentially expressed glycolysis/gluconeogenesis-related genes were validated in additional datasets. Consensus clustering analysis identified two distinct patient clusters (C1 and C2) with different prognoses and immune microenvironments. Immune score and tumor purity were significantly higher in C1 than in C2, and CD4+ memory activated T cell, Tfh, Tregs, and macrophage M0 were higher infiltrated in HCC and C1 group. The study also identified five intersecting DEGs from candidate genes in TCGA, GSE14520, and GSE141198 as prognostic genes, which had a protective role in HCC patient prognosis. Compared with the control group, the prognostic genes all showed decreased expression in HCC patients in RT-qPCR and Western blot analyses. Flow cytometry verified the abnormal infiltration level of immune cells in HCC patients. Conclusion: Results showed that glycolysis/gluconeogenesis-related genes were associated with patient prognosis, immune microenvironment, and response to immunotherapy in HCC. It suggests that the model based on five prognostic genes may valuable for predicting the prognosis and immunotherapy response of HCC patients.

YTHDF3as a prognostic predictive biomarker of thyroid cancer and its correlation with immune infiltration.

PURPOSE: Thyroid cancer (TC) is one of the most common endocrine malignancies, and its morbidity continues to rise. N6-methyladenosine (m6A) RNA methylation, an epigenetic modification, is an important regulator of gene expression in TC. Therefore, it's worth finding the characteristics and predictive value of the m6A RNA methylation regulators in thyroid cancer (TC). METHOD: RNA-seq data of TC was downloaded from the Cancer Genome Atlas (TCGA) database to screen out the differential expressed regulators. The absolute contraction selection operator (Lasso) Cox regression was used to construct the risk model of m6A methylation regulators. The predictive value of the risk scoring model was evaluated by Kaplan Meier (K-M) analysis and receiver operating characteristic (ROC) curves. The underlying mechanism of m6A methylation regulators in TC was predicted by gene set enrichment analysis (GSEA). Further validation was performed by using immunohistochemistry (IHC) and q-PCR. The correlation between risk-related gene and immune infiltration was evaluated by Tumour Immune Estimation Resource (TIMER). RESULTS: IGF2BP2, YTHDF1 and YTHDF3 were screened out as strong independent prognostic factors of TC. Then a risk score model was established to further screen the predictors. Finally, according to the results of overall survival (OS) and clinical characteristics of TC, YTHDF3 was screened out as a potential predictor. Meanwhile, IHC and qPCR confirmed that YTHDF3 was expressed differential in TC. The expression of YTHDF3 was positively associated with the infiltration level of CD4+ T cells and macrophages. It was strongly correlated with a variety of immune markers in TC. CONCLUSION: We confirmed that YTHDF3 can be used as a potential prognostic biomarker of TC. It not only plays a decisive role in the initiation and development of TC, but also provides a new perspective for understanding the modification of m6A RNA in TC.

Hyperspectral inversion of soil water and salt information based on fractional order derivative technology.

Accurate and efficient acquisition of soil water and salt information is a prerequisite for the improvement and sustainable utilization of saline lands. With the ground field hyperspectral reflectance and the measured soil water-salt content as data sources, we used the fractional order differentiation (FOD) technique to process hyperspectral data (with a step length of 0.25). The optimal FOD order was explored at the correlation level of spectral data and soil water-salt information. We constructed two-dimensional spectral index, support vector machine regression (SVR) and geographically weighted regression (GWR). The inverse model of soil water-salt content was finally evaluated. The results showed that FOD technique could reduce the hyperspectral noise and explore the potential spectral information to a certain extent, improve the correlation between spectrum and characteristics, with the highest correlation coefficients of 0.98, 1.35 and 0.33. The combination of characteristic bands screened by FOD and two-dimensional spectral index were more sensitive to characteristics than one-dimensional bands, with the optimal responses of order 1.5, 1.0 and 0.75. The optimal combinations of bands for maximum absolute correction coefficient of SMC were 570, 1000, 1010, 1020, 1330 and 2140 nm, pH were 550, 1000, 1380 and 2180 nm, and salt content were 600, 990, 1600 and 1710 nm, respectively. Compared with the original spectral reflectance, the validation coefficients of determination (Rp2) of the optimal order estimation models for SMC, pH, and salinity were improved by 1.87, 0.94 and 0.56, respectively. The overall GWR accuracy in the proposed model was better than SVR, where the GWR optimal order estimation models Rp2 were 0.866, 0.904 and 0.647, and the relative per-centage difference were 3.54, 4.25 and 1.86, respectively. The overall spatial distribution of soil water and salt content in the study area was characterized by low in the west and high in the east, with more serious soil alkalinization problems in the northwest and less severe in the northeast. The results would provide scientific basis for the hyperspectral inversion of soil water and salt in the Yellow River Irrigation Area and a new strategy for the implementation and management of precision agriculture in saline soil areas.

Estimation of soil organic matter content in Yinchuan Plain based on fractional derivative combined with spectral indices.

Soil organic matter (SOM) is a crucial indicator of soil fertility. Field hyperspectral reflectance and laboratory SOM data of soil samples from the Yinchuan Plain were used to explore the performance of models based on fractional derivative combined with different spectral indices. Following reciprocal and logarithmic transformation, the reflectance data were processed using fractional derivative from 0 to 2 orders (interval 0.20). Then, the difference index (DI), ratio index (RI), brightness index (BI), normalized difference index (NDI), renormalized difference index (RDI), and generalized difference index (GDI) were constructed. The two-dimensional correlation between the six indices and SOM content were analyzed. The optimal spectral indices were selected to establish SOM estimation models with principal component regression (PCR), partial least square regression (PLSR), back propagation neural network (BPNN), support vector machine (SVM), and geographically weighted regression (GWR). Results showed that the maximum absolute correlation coefficient (MACC) values between DI, RI, NDI, BI, GDI, RDI, and SOM contents increased firstly and then decreased, with the highest values observed at 1.0, 0.6, 1.4, and 1.6 orders. The 0.2-2.0 order RDI under fractional derivative variation could be used for subsequent model construction, in which the optimal combinations of bands for MACC values were mainly concentrated at 400-600 nm and 1300-1700 nm. Among the different models based on the single spectral index RDI, the model based on SVM achieved the highest estimation accuracy, whose modeling determination coefficient, verification determination coefficient and relative percentage difference reached 0.86, 0.87 and 2.32. Our results would provide a scientific reference for quick and accurate SOM assessment and mapping in areas with relatively low SOM content.

Study and Classification of Porosity Stress Sensitivity in Shale Gas Reservoirs Based on Experiments and Optimized Support Vector Machine Algorithm for the Silurian Longmaxi Shale in the Southern Sichuan Basin, China.

To understand the characteristics of variation in porosity and permeability, the physical properties of the shale reservoir under different stress conditions play an important role in guiding shale gas production. With the shale of the Wufeng-Longmaxi Formation in the south of the Sichuan Basin as the research object, stress-dependent porosity and permeability test, high-pressure mercury injection, and scanning electron microscope test were performed in this study to thoroughly analyze the variation in physical properties of different shale lithofacies with effective stress. Besides, the stress sensitivity of different lithofacies reservoirs was evaluated by using parameters such as pore compressibility coefficient (PCC) and porosity sensitivity exponent (PSE), while the optimized support vector machine (SVM) algorithm was adopted to predict the coefficient of reservoir porosity sensitivity. According to the research results, the porosity and permeability of shale reservoirs decline as a negative exponential function. When the effective stress falls below 15 MPa, the damage rate of permeability/porosity increases rapidly with the rise of effective stress. By contrast, the permeability curvature of the shale reservoirs plunges with the rise of effective stress. It was discovered that a higher siliceous content results in a higher permeability curvature of shale, indicating the greater stress sensitivity of the reservoir. The ratio of matrix porosity to microfracture porosity determines the PSE, which is relatively low, and low aspect ratio pores contribute to high porosity compressibility and stress sensitivity. Young's modulus shows a negative correlation with pore compressibility and a positive correlation with Poisson's ratio. High clay minerals have a large number of low aspect ratio pores and a low elastic modulus, which leads to both high PCC and low PSE. Based on the principal component analysis, a multiclassification SVM model was established to predict the PSE, revealing that the accuracy of the sigmoid, radial basis function (RBF), and linear kernel function is consistently above 70%. According to error analysis, the accuracy can exceed 80% with the RBF kernel function and appropriate penalty factor. The research results serve to advance the research on the parameters related to overburden pressure, porosity, and permeability. Moreover, the optimized SVM algorithm is applied to make a classification prediction, which provides a reference for shale reservoir exploration and development both in theory and practice.

[Clinical phenotype and genetic features of 16p11.2 microdeletion-related epilepsy in children]. / 16p11.2å¾®ç¼ºå¤±ç›¸å ³å„¿ç«¥ç™«ç—«çš„ä¸´åºŠè¡¨åž‹ä¸Žé—ä¼ å­¦ç‰¹å¾åˆ†æž.

OBJECTIVES: To study the clinical phenotype and genetic features of 16p11.2 microdeletion-related epilepsy in children. METHODS: The medical data of 200 children with epilepsy who underwent a genetic analysis of epilepsy by the whole exon sequencing technology were collected retrospectively, of whom 9 children with epilepsy had 16p11.2 microdeletion. The clinical phenotype and genetic features of the 9 children with 16p11.2 microdeletion were analyzed. RESULTS: The detection rate of 16p11.2 microdeletion was 4.5% (9/200). The 9 children with 16p11.2 microdeletion were 3-10 months old. They experienced focal motor seizures with consciousness disturbance, and some of the seizures developed into generalized tonic-clonic seizures. The interictal electroencephalogram showed focal or multifocal epileptiform discharge, and all 9 children responded well to antiepileptic drugs. The 9 children had a 16p11.2 deletion fragment size of 398-906 kb, and the number of deleted genes was 23-33 which were all pathogenic mutations. The mutation was of maternal origin in 2 children, of paternal origin in 1 child, and de novo in the other children. CONCLUSIONS: 16p11.2 microdeletion can be detected in some children with epilepsy. Most of the 16p11.2 microdeletion is de novo mutation and large gene fragment deletion. The onset of 16p11.2 microdeletion-related epilepsy in children is mostly within 1 year of life, and the epilepsy is drug-responsive.

How curriculum delivery translates into entrepreneurial skills: The mediating role of knowledge of information and communication technology.

This research examines how curriculum delivery predicts entrepreneurial skills, with knowledge of information and communication technology (ICT) as a mediator. Curriculum delivery with the multiple dimensions of objectives, contents, teaching strategies, and feedback and assessment was used in this study, and a quantitative research design was adopted. A questionnaire survey was used to collect data from 482 students at six universities in Lahore, Pakistan, and the partial-least-squares structural equation model in SmartPLS 3.2 was used for data analysis. The results show that all dimensions of curriculum delivery (i) do not influence entrepreneurial skills and (ii) positively influence the knowledge of ICT. Also, in the indirect relationships, all dimensions of curriculum delivery (i.e., objectives, contents, teaching strategies, and feedback and assessment) are associated positively with ICT knowledge. Therefore, ICT knowledge plays a mediating role between curriculum delivery and entrepreneurial skills. The results also show that curriculum delivery for educational entrepreneurs is not working effectively and efficiently in Pakistani universities, and it is concluded that curriculum delivery and ICT knowledge boost entrepreneurial skills. Finally, the conclusions, limitations, and practical implications of this study are presented in detail.

[Effects of different spectra types on the accuracy and correction of soil salt content inversion in Yinchuan Plain, China]. / ä¸åŒå ‰è°±ç±»åž‹å¯¹é“¶å·å¹³åŽŸåœŸå£¤å«ç›é‡åæ¼”ç²¾åº¦çš„å½±å“ä¸Žæ ¡æ­£.

Soil salinization is one of key drivers for the degradation of soil quality and yield in arable land. To accurately and quickly evaluate soil salt content in Yinchuan Plain, field and indoor hyperspectral data were processed with first order differential (FDR) transformation, then the feature bands were identified by stepwise regression (SR). Partial least squares regression (PLSR) and support vector machines (SVM) were used to build models, which were verified to figure out the optimal hyperspectral type for the study area. Moreover, segmented and global corrections were performed to process poor hyperspectral, aiming to improve the accuracy of soil salt content inversion. The results showed that the accuracy of soil salt content inversion model based on field hyperspectral data was 58.9% higher than that of the indoor hyperspectral data. The accuracy of the inversion was improved through the segmented and global correction of the indoor hyperspectral. We found that the segmented correction is more accurate for the PLSR model (Rc2=0.790, Rp2=0.633, RPD=1.64) and the global correction is more accurate for the SVM model (Rc2=0.927, Rp2=0.947, RPD=3.87). The SVM models' inversion accuracy was higher than that of PLSR, with the field hyperspectral model fitted the best, followed by the indoor hyperspectral processed with the global correction and the indoor hyperspectral processed with the segmented correction, while the indoor hyperspectral the worst. Our results suggest that field hyperspectral data could contribute to the quantitative inversion of soil salt content in Yinchuan Plain. The corrected indoor hyperspectral could significantly enhance the inversion accuracy of soil salt content, which could guarantee food security and ecological quality development.

Amniotic fluid derived stem cells promote skin regeneration and alleviate scar formation through exosomal miRNA-146a-5p via targeting CXCR4.

BACKGROUND AND OBJECTIVES: Regenerative medicine is promising in wound healing. Exosomes derived from human amniotic fluid derived stem cells (hAFS) have become an important area of research for many diseases as a key paracrine factor, but its effects in wound healing remains unknown. In this study, we investigated the possible role and possible mechanisms of hAFS in skin wound healing. METHODS: hAFS were isolated from human amniotic fluid via routine amniocentesis. The mice were randomly divided into 2 groups: control group and hAFS group treated with 1.25 × 106  hAFS cells. Immunohistochemistry staining was performed for histological analysis and qRT-PCR for the assessment of gene levels. Luciferase Reporter Assay was performed for the verification of target gene. RESULTS: Our results demonstrated that hAFS accelerated wound closure. hAFS alleviated scar formation via promoting ECM remodeling, upregulating molecular of immune response, enhancing anti-fibrotic activity, and decreasing the secretion of inflammation-associated cytokines through exosomal miRNA-146a-5p via targeting CXCR4. CONCLUSIONS: Taken together, hAFS was a promising cell source for wound healing. The findings in this study provide vital references and pave the way for future research.

Impact of Self-Concept, Self-Imagination, and Self-Efficacy on English Language Learning Outcomes Among Blended Learning Students During COVID-19.

The purpose of the present study was to explore the direct influence of self-concept and self-imagination on English language learning outcomes (ELLO). Furthermore, this study examined the mediating role of self-efficacy in the relationship between self-concept, self-imagination, and ELLO. A survey questionnaire of 21 items was used in this study. We distributed the questionnaire through QR code and collected the data from 2,517 participants who enrolled in blended learning courses at the undergraduate level in Chinese universities. The relationship among the variables was measured through SmartPLS-SEM 3.3.3 (partial least squares structural equation modeling). The outcomes of the present study indicated a direct, positive, and significant connection of self-concept, self-imagination, and self-efficacy with ELLO. Looking at indirect influences, self-concept and self-imagination, positive and significant, influence ELLO through self-efficacy. Thus, self-efficacy was indicated to play a mediating role between self-concept, self-imagination and ELLO. We can conclude that self-concept, self-imagination, and self-efficacy are the main predictors of ELLO in blended learning courses during the pandemic. Additionally, self-concept and self-imagination along with the intervening role of self-efficacy, play a more effective role in improving ELLO. Moreover, this study provided some useful, practical implications, and future research directions.

Clinical and electroencephalogram characteristics and treatment outcomes in children with benign epilepsy and centrotemporal spikes.

BACKGROUND: Epilepsy is a syndrome characterized by transient, rigid, paroxysmal, and repetitive central nervous system dysfunction. Prevention, control, and improvement of cognitive and behavioral dysfunction are of great significance for improving the patients' intellectual development and quality of life. Electroencephalograms (EEG) can predict an accelerated decline in cognitive function. AIM: To determine the clinical and EEG characteristics and treatment results of benign epilepsy in spiking children. METHODS: A total of 106 cases of benign epilepsy in children with myocardial spines treated at our hospital from January 2017 to January 2020 were selected. Differences in clinical data and EGG characteristics between treatment-effective/-ineffective patients were analyzed, and children's intellectual development before and after treatment evaluated using the Gesell Development Diagnostic Scale. RESULTS: EEG showed that the discharge proportion in the awake and sleep periods was 66.04%, and the peak/peak discharge was mainly single-sided, accounting for 81.13%, while the discharge generalization accounted for 31.13%. There was no significant difference in any of these variables between sexes and ages (P > 0.05). The proportion of patients with early onset (< 5 years old) and seizure frequency > 3 times/half a year was 40.00% and 60.00%, respectively; the incidence rate and seizure frequency in the younger age group (< 5 years old) were significantly higher than those in the treatment-effective group (P < 0.05), while the discharge index was significantly lower than that in the treatment-effective group (P < 0.05). The discharge index was negatively correlated with fine motor skill and language development (r = -0.274 and -0.247, respectively; P < 0.05), but not with the rest (P > 0.05). Logistic regression analysis showed that low age onset (< 5 years old) and seizure frequency were the factors affecting ineffective-treatment of benign epilepsy in children (odds ratio = 11.304 and 5.784, respectively; P < 0.05). The discharge index of the responsive group after treatment was significantly lower than that of the unresponsive group (P < 0.05). However, there was no significant difference between groups after treatment in gross and fine motor skills, adaptability, language, and personal social development (P > 0.05). CONCLUSION: The EEG of children with benign epilepsy due to spinal wave in central time zone has characteristic changes, and the therapeutic effect is influenced by age of onset and attack frequency.

[Distribution Characteristics of Antibiotics and Antibiotic Resistance Genes in Manure and Surrounding Soil of Cattle Farms in Ningxia].

Livestock breeding intensively uses veterinary antibiotics in concentrated feeding operations to improve growth and control disease. Consequently, livestock and poultry manure is an important repository of antibiotics and antibiotic resistance genes (ARGs). To understanding the distribution of antibiotics and ARGs in manure and surrounding soils of cattle farms in Ningxia, cattle manure from five breeding periods (lactation, calving, growing, pre-fattening, and post-fattening periods) and comparative soil samples were collected from the largest beef-breeding area in Ningxia. The compositions of ARGs in the samples were analyzed by UPLC-MS/MS and HT-qPCR. The results showed that:① Tetracycline, quinolone, and sulfonamide were the dominant antibiotics in cattle manure. The content of antibiotics in the manure varied greatly between different breeding periods. High amounts of antibiotics were detected during the pre-fattening and lactation periods, and the lowest amounts were detected during the calving period. ② Quinolone and tetracycline were the dominant antibiotics in the soils, and the detection rate and content of quinolone were highest. The cattle farms did not affect the distribution of antibiotics in the surrounding soils. The content of quinolone and tetracycline in the soils with cattle manure application were significantly higher than control and surrounding soils. ③ We detected 79-142 ARGs in cattle manure, with aminoglycosides the most common form. The number and relative abundance of ARGs were highest during the growing period and lowest during the calving period. The dominant ARGs were tetQ, ermF, and tetO-01 across all the breeding periods. ④ There were 35-79 ARGs in the tested soils, and multidrug and aminoglycoside ARGs were dominant. The cattle farms did not affect the number and relative abundance of ARGs in the surrounding soils; however, manure application significantly affected the number and relative abundance of ARGs. ⑤ Sulfonamide and chloramphenicol ARGs are at risk of mobilization and horizontal transport. A correlation analysis showed that the relative abundances of aminoglycoside and tetracycline in cattle manure were significantly positively correlated with their contents. ⑥ Aminoglycoside, sulfonamide, chloramphenicol, and mobile genetic elements (MGEs) in manure were significantly or extremely significantly positively correlated with aminoglycosides and sulfonamides in the soils, whereas macrolides were negatively correlated with vancomycin. These results provide baseline data to inform controls on the variety and dosages of feed and veterinary drugs in cattle farms and the application of organic fertilizers in agriculture.

Integrin αVß1 regulates procollagen I production through a non-canonical transforming growth factor ß signaling pathway in human hepatic stellate cells.

Hepatic stellate cells (HSCs) are thought to play key roles in the development of liver fibrosis. Extensive evidence has established the concept that αV integrins are involved in the activation of latent transforming growth factor ß (TGF-ß), a master regulator of the fibrotic signaling cascade. Based on mRNA and protein expression profiling data, we found that αVß1 integrin is the most abundant member of the αV integrin family in either quiescent or TGF-ß1-activated primary human HSCs. Unexpectedly, either a selective αVß1 inhibitor, Compound 8 (C8), or a pan-αV integrin inhibitor, GSK3008348, decreased TGF-ß1-activated procollagen I production in primary human HSCs, in which the role of ß1 integrin was confirmed by ITGB1 siRNA. In contrast with an Activin receptor-like kinase 5 (Alk5) inhibitor, C8 and GSK3008348 failed to inhibit TGF-ß1 induced SMAD3 and SMAD2 phosphorylation, but inhibited TGF-ß-induced phosphorylation of ERK1/2 and STAT3, suggesting that αVß1 integrin is involved in non-canonical TGF-ß signaling pathways. Consistently, ITGB1 siRNA significantly decreased phosphorylation of ERK1/2. Furthermore, a selective inhibitor of MEK1/2 blocked TGF-ß1 induced phosphorylation of ERK1/2 and decreased TGF-ß1 induced procollagen I production, while a specific inhibitor of STAT3 had no effect on TGF-ß1 induced procollagen I production. Taken together, current data indicate that αVß1 integrin can regulate TGF-ß signaling independent of its reported role in activating latent TGF-ß. Our data further support that αVß1 inhibition is a promising therapeutic target for the treatment of liver fibrosis.

Estimation of soil Na + content based on measured hyperspectral and Sentinel-2B data in northern Ningxia, China.

To explore the ability of different sensors to estimate soil Na+ content, we got the mea-sured soil spectra and Sentinel-2B image spectra of the typical soil samples from the northern area of Ningxia. We filtered the sensitive parameters from the spectra data by means of stepwise regression (SR) and principal component regression analysis (PCA). We established the models to estimate soil Na+ content based on the measured spectra and image data using partial least square regression (PLSR), support vector machine (SVM) and back propagation neural network model (BPNN). The results showed that, except for Band9, there was significant correlation between the resampling data and the image data. The estimation accuracy of models based on SR-screening was generally higher than the PCA (excluding SVM model). The PCA-SVM model was the best image estimation model for soil Na+ content, with a prediction accuracy of 0.792. The SR-BPNN model was the best measured estimation model, with a prediction accuracy of 0.908. The estimating accuracy of the SR-PLSR image-spectra-based model increased from 0.481 to 0.798 after calibrated by the resampled measured spectrum model, which effectively enhanced the accuracy in estimating the soil Na+ content at large scale. We successfully made the spatial transformation of soil Na+ content from point to surface. Our results provided a scientific reference for Sentinel-2B image to monitor Na+ content in salinized soil.

Therapeutic Potential of Exosomes Derived From circRNA_0002113 Lacking Mesenchymal Stem Cells in Myocardial Infarction.

Exosomes are participated in the pathogenesis of cardiovascular diseases and can be secreted by mesenchymal stem cells (MSCs). However, the effects of circRNA, delivered by exosomes derived from MSCs, on myocardial injury remain unclear. Hence, this study aims to explore the therapeutic potential of exosomes derived from circRNA_0002113 lacking MSCs in the treatment of myocardial injury in vitro and in vivo. Our results reveal that exosomes derived from circRNA_0002113 lacking MSCs decreased cell apoptosis in anoxia-reoxygenation (A/R) model cells, and reduced myocardial injury by inhibiting nuclear translocation of RUNX1 in vitro and in vivo. Moreover, miR-188-3p, which targets RUNX1 in cardiomyocytes was also found to interact with circRNA_0002113. In conclusion, exosomes derived from circRNA_0002113 lacking MSCs could suppress myocardial infarction by sponging miR-188-3p to regulate RUNX1 nuclear translocation. The circRNA_0002113/miR-188-3p/RUNX1 axis mediated alleviation of apoptosis serves as a novel strategy to treat myocardial I/R injury.

Circulating expression of Hsa_circRNA_102893 contributes to early gestational diabetes mellitus detection.

Due to a poor availability of reliable biomarkers, detecting gestational diabetes mellitus (GDM) in early pregnancy remains a challenge. Novel biomarkers like Circular RNAs (circRNAs) may be a promising diagnostic tool. The aim of this study was (a) to identify circRNAs deregulated in GDM and (b) evaluate the potential of circRNAs in detecting GDM. The circRNAs expression profiling in 6 paired women (with and without GDM) was measured by microarray. The levels of five most relevant circRNAs were validated in 12 paired participants by qRT-PCR. To verify the reproducibility of qRT-PCR, significantly differential expressed circRNA levels were confirmed in 18 paired participants. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value. The areas under ROC curves of hsa_circRNA_102893 were 0.806 (95% CI 0.594-0.937) and 0.741 (0.568-0.872) in training set and test set, respectively. Circulating circRNAs reflect the presence of GDM. Hsa_circRNA_102893 may be a potential novel and stable noninvasive biomarker for detecting GDM in early pregnancy.

Primary cilia-dependent signaling is involved in regulating mesenchymal stem cell proliferation and pluripotency maintenance.

Using a large-scale quantitative mesenchymal stem cells (MSCs) membrane proteomics analysis, we identified a large group of ciliary proteins in the MSCs membrane fraction, which prompted us to examine the cilia, intricate organelles that were originally discovered approximately 100 years ago. Here we characterize their primary structure and function in MSCs. We first characterized the primary cilia on undifferentiated human MSCs by immunostaining and verified these observation with scanning and 3D electronic microscopy. To investigate the function of the primary cilia of the MSCs we induced loss of function by means of siRNA knockdown (targeted to two known ciliary proteins: IFT172 and KIF3A). After either of these two proteins was knocked down by the respective siRNA, the MSCs showed fewer and shortened primary cilia. The MSCs proliferation assays showed increased cell proliferative activity under confluent conditions after the siRNA knockdown of IFT172 or KIF3A; among these MSCs, the proportion in S phase was increased in the IFT172 siRNA knockdown group. The expression of stem cell markers on the MSCs, namely, Oct4, Nanog and Sox2, were downregulated after the siRNA-induced knockdown of IFT172 or KIF3A, and the gene expression upregulation of ectoderm lineage markers was notable. Furthermore, manipulation of the primary cilia-dependent Shh pathway, using the Shh activator SAG (smoothened agonist), upregulated the gene expression of pluripotency markers, including Nanog and Oct4, and transcriptional target genes in the Shh pathway. This study confirms that MSCs have primary cilia and provides evidence that primary cilia-dependent signaling pathways play functional roles in MSCs proliferation and stemness maintenance.

Spherical Superstructure of Boron Nitride Nanosheets Derived from Boron-Containing Metal-Organic Frameworks.

The assembly of two-dimensional (2D) nanosheets into three-dimensional (3D) well-organized superstructures is one of the key topics in materials chemistry and physics, due to their potential applications in various fields. Herein, starting from the crystalline metal-organic framework (MOF) particles, a spherical superstructure consisting of metal-organic framework nanosheets (SS-MOFNSs) is synthesized via a simple solvothermal transformation process. After pyrolysis and nitrogenization in ammonia, the SS-MOFNSs are further transformed into the spherical superstructure consisting of boron nitride nanosheets (SS-BNNSs), which preserve the original spherical superstructure morphology. Taking advantage of this unique superstructure, the resulting SS-BNNSs exhibit excellent catalytic activity for selective oxidative dehydrogenation of propane to produce propylene and ethylene. The results of this work provide a novel synthetic strategy to fabricate 3D spherical superstructures consisting of 2D nanosheets for high-performance applications in catalysis, energy storage, as well as other related fields.