Microwave-assisted rapid and regioselective synthesis of N-(alkoxycarbonylmethyl) nucleobases in water

A facile and eco-friendly approach has been developed for the preparation of N-(ethyoxycarbonylmethyl) nucleobases and N-(iso-propoxycarbonylmethyl) nucleobases, which are important building blocks for Peptide Nucleic Acids (PNA). All the nucleobases are regioselectively alkylated and the desired products are obtained in moderate to high yields under microwave irradiation for 8 min in water as the solvent and in the presence of Et3N as the base

The discrepancy distribution of macrophage subsets in preeclampsia placenta with or without fetal growth restriction from a small cohort

Objectives: To identify the effect of distribution characteristic of macrophages on placental function and angiogenesis in pregnancies with preeclampsia (PE) in presence of fetal growth restriction (FGR) or preeclampsia without FGR. Material and methods: The study tested the hypothesis that there was association between distribution characteristic of macrophage subsets (marked by CD68, CD163, respectively) and placental capillary development, leading to placental dysfunction in PE pregnancies with FGR (n = 36). Changes in placental parameters related with efficiency and angiogenesis and macrophage phenotypes (CD68 and CD163) were evaluated by immunohistochemistry. Pearson correlation analysis was performed to analysis the association between macrophage phenotype and placental function as well the CD34 staining, respectively. Additionally, the localization of CD68 and CD163 was assessed by using immunoflurorescence staining. Results: Pearson correlation analysis had shown the positive association between CD68 expression and microvessel formation and the reverse linear relationship between CD163 staining and placental sufficiency in PE + FGR placenta. The co-localization of CD163 and CD34 may pointed to the compensatory role of CD163 distribution involved in prompting neovascularization. Conclusions: The association between disturbed distribution of macrophages and placental efficiency and angiogenesis were only found in PE with FGR not in PE pregnancies without FGR, underlying the discrepancy role of macrophage subsets depending on the clinical phenotype of PE pregnancies

Direct observation of high temperature superconductivity in one-unit-cell FeSe films

Heterostructure based interface engineering has been proved an effective method for finding new superconducting systems and raising superconductivity transition temperature (TC). In previous work on one unit-cell (UC) thick FeSe films on SrTiO3 (STO) substrate, a superconducting-like energy gap as large as 20 meV, was revealed by in situ scanning tunneling microscopy/spectroscopy (STM/STS). Angle resolved photoemission spectroscopy (ARPES) further revealed a nearly isotropic gap of above 15 meV, which closes at a temperature of ~ 65 K. If this transition is indeed the superconducting transition, then the 1-UC FeSe represents the thinnest high TC superconductor discovered so far. However, up to date direct transport measurement of the 1-UC FeSe films has not been reported, mainly because growth of large scale 1-UC FeSe films is challenging and the 1-UC FeSe films are too thin to survive in atmosphere. In this work, we successfully prepared 1-UC FeSe films on insulating STO substrates with non-superconducting FeTe protection layers. By direct transport and magnetic measurements, we provide definitive evidence for high temperature superconductivity in the 1-UC FeSe films with an onset TC above 40 K and a extremely large critical current density JC ~ 1.7*106 A/cm2 at 2 K. Our work may pave the way to enhancing and tailoring superconductivity by interface engineering

Design and Research of Electron Cyclotron Resonance Heating and Current Dive System on HL-2M Tokamak

A research has been conducted to develop an 8MW electron cyclotron resonance heating and current drive (ECRH/ECCD) system on HL-2M tokamak. The ECRH system compromise eight 1MW gyrotrons, eight evacuated transmission lines and three launchers. The main purpose of the ECRH system was to suppress the neo-classical tearing modes and control the plasma profile. This paper presents an overview of the design and studies performed in this framework. Some primary test results of the critical components have been released in this paper, e.g. polarizers, power monitor and fast steering launchers

A case of primary central nervous system lymphoma mimic neuromyelitis optica

Primary central nervous system lymphoma (PCNSL) is rare. And the symptoms of PCNSL are atypical, it is extremely easy to be misdiagnosed as other diseases. However, early treatment is crucial which is requesting early diagnosis. We report a case of a 47-year-old man who was initially diagnosed as neuromyelitis optica (NMO) on the basis of clinical findings, slightly high Aquaporin4 (AQP4) (1:10) and high signals of magnetic resonance imaging. Though his symptoms progressively improved after steroid pulse treatment, but worse when steroid was decreased to 40 mg per day. We considered the patient should be diagnosed as PCNSL. After the examination of magnetic resonance spectroscopy (MRS) and positron emission tomography (PET), the results indicated PCNSL was most possible. Therefore we gave him stereotactic biopsy of deep of supratentorial, which showed non-Hodgkin malignant B-cell lymphoma

Soil Organic Matter Detection Based on Pyrolysis and Electronic Nose Combined with Multi-Feature Data Fusion Optimization

Soil organic matter (SOM) is one of the main sources of plant nutrition and promotes plant growth and development. The content of SOM varies in different areas of the field. In this study, a method based on pyrolysis and electronic nose combined with multi-feature data fusion optimization was proposed to realize rapid, accurate and low-cost measurement of SOM content. Firstly, an electronic nose was used to collect response data from the soil pyrolysis gas, and the sensor features (10 × 6) were extracted to form the original feature space. Secondly, Pearson correlation coefficient (PCC), one-way analysis of variance (One-Way ANOVA), principal component analysis algorithm (PCA), linear discriminant analysis algorithm (LDA), and genetic algorithm-backpropagation neural network algorithm (GA-BP) were used to realize multi-feature data fusion optimization. Thirdly, the optimized feature space was used to train the PLSR models, and the predictive performance of the models were used as an indicator to evaluate different feature optimization algorithms. The results showed that the PLSR model with GA-BP for feature optimization had the best predictive performance (R2 = 0.90) and could achieve accurate quantitative prediction of SOM content. The dimensionality of the optimized feature space was reduced to 30 and there was no redundancy in the sensor array

Soil Organic Matter Detection Based on Pyrolysis and Electronic Nose Combined with Multi-Feature Data Fusion Optimization

Soil organic matter (SOM) is one of the main sources of plant nutrition and promotes plant growth and development. The content of SOM varies in different areas of the field. In this study, a method based on pyrolysis and electronic nose combined with multi-feature data fusion optimization was proposed to realize rapid, accurate and low-cost measurement of SOM content. Firstly, an electronic nose was used to collect response data from the soil pyrolysis gas, and the sensor features (10 × 6) were extracted to form the original feature space. Secondly, Pearson correlation coefficient (PCC), one-way analysis of variance (One-Way ANOVA), principal component analysis algorithm (PCA), linear discriminant analysis algorithm (LDA), and genetic algorithm-backpropagation neural network algorithm (GA-BP) were used to realize multi-feature data fusion optimization. Thirdly, the optimized feature space was used to train the PLSR models, and the predictive performance of the models were used as an indicator to evaluate different feature optimization algorithms. The results showed that the PLSR model with GA-BP for feature optimization had the best predictive performance (R2 = 0.90) and could achieve accurate quantitative prediction of SOM content. The dimensionality of the optimized feature space was reduced to 30 and there was no redundancy in the sensor array

Multi-Feature Optimization Study of Soil Total Nitrogen Content Detection Based on Thermal Cracking and Artificial Olfactory System

To improve the accuracy of detecting soil total nitrogen (STN) content by an artificial olfactory system, this paper proposes a multi-feature optimization method for soil total nitrogen content based on an artificial olfactory system. Ten different metal–oxide semiconductor gas sensors were selected to form a sensor array to collect soil gas and generate response curves. Additionally, six features such as the response area, maximum value, average differential coefficient, standard deviation value, average value, and 15th-second transient value of each sensor response curve were extracted to construct an artificial olfactory feature space (10 × 6). Moreover, the relationship between feature space and soil total nitrogen content was used to establish backpropagation neural network (BPNN), extreme learning machine (ELM), and partial least squares regression (PLSR) models were used, and the coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were selected as prediction performance indicators. The Monte Carlo cross-validation (MCCV) and K-means improved leave-one-out cross-validation (K-means LOOCV) were adopted to identify and remove abnormal samples in the feature space and establish the BPNN model, respectively. There were significant improvements before and after comparing the two rejection methods, among which the MCCV rejection method was superior, where values for R2, RMSE, and RPD were 0.75671, 0.33517, and 1.7938, respectively. After removing the abnormal samples, the soil samples were then subjected to feature-optimized dimensionality reduction using principal component analysis (PCA) and genetic algorithm-based optimization backpropagation neural network (GA-BP). The test results showed that after feature optimization the model indicators performed better than those of the unoptimized model, and the PLSR model with GA-BP for feature optimization had the best prediction effect, with an R2 value of 0.93848, RPD value of 3.5666, and RMSE value of 0.16857 in the test set. R2 and RPD values improved by 14.01% and 50.60%, respectively, compared with those before optimization, and RMSE value decreased by 45.16%, which effectively improved the accuracy of the artificial olfactory system in detecting soil total nitrogen content and could achieve more accurate quantitative prediction of soil total nitrogen content