A Rh‐catalyzed asymmetric synthesis of silicon‐stereogenic dihydrodibenzosilines featuring axially chiral 6‐membered bridged biaryls is demonstrated. In the presence of a RhI catalyst with a chiral ...diphosphine ligand, a wide range of dihydrodibenzosilines containing both silicon‐central and axial chiralities are conveniently constructed in excellent enantioselectivities via dehydrogenative C(sp3)−H silylation. Absolute configuration analysis by single‐crystal X‐ray structures revealed a novel silicon central‐to‐axial chirality relay phenomenon, which we believe will inspire further research in the field of asymmetric catalysis and chiroptical materials.
A Rh‐catalyzed asymmetric synthesis of silicon‐stereogenic dihydrodibenzosilines featuring axially chiral 6‐membered bridged biaryls is demonstrated. A wide range of dihydrodibenzosilines containing both silicon‐central and axial chirality are conveniently constructed in excellent enantioselectivity via intramolecular dehydrogenative C−H silylation.
Methamphetamine use disorder (MAUD) can substantially jeopardize public security due to its high‐risk social psychology and behaviour. Given that the dopamine reward system is intimately correlated ...with MAUD, we investigated the association of single nucleotide polymorphisms (SNPs), as well as methylation status of dopamine receptor type 4 (DRD4), catechol‐O‐methyltransferase (COMT) genes, and paranoid and motor‐impulsive symptoms in MAUD patients. A total of 189 MAUD patients participated in our study. Peripheral blood samples were used to detect 3 SNPs and 35 CpG units of methylation in the DRD4 gene promoter region and 5 SNPs and 39 CpG units in the COMT gene. MAUD patients with the DRD4 rs1800955 C allele have a lower percentage of paranoid symptoms than those with the rs1800955 TT allele. Individuals with paranoid symptoms exhibited a reduced methylation degree at a particular DRD4 CpG2.3 unit. The interaction of the DRD4 rs1800955 C allele and the reduced DRD4CpG2.3 methylation degree were associated with a lower occurrence of paranoid symptoms. Meanwhile, those with the COMT rs4818 CC allele had lower motor‐impulsivity scores in MAUD patients but greater COMT methylation levels in the promoter region and methylation degree at the COMT CpG 51.52 unit. Therefore, based only on the COMT rs4818 CC polymorphism, there was a negative correlation between COMT methylation and motor‐impulsive scores. Our preliminary results provide a clue that the combination of SNP genotype and methylation status of the DRD4 and COMT genes serve as biological indicators for the prevalence of relatively high‐risk psychotic symptoms in MAUD patients.
The interaction of the DRD4 rs1800955 C allele and the reduced DRD4 CpG2.3 methylation degree were associated with a lower occurrence of paranoid symptoms. Based only on the COMT rs4818 CC polymorphism, there was a negative correlation between COMT methylation and motor‐impulsive scores. The combination of SNP genotype and methylation status of the DRD4 and COMT genes may serve as biological indicators to evaluate the prevalence of relatively high‐risk psychotic symptoms in MAUD patients.
Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do ...not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.
Radiotherapy is commonly used for abdominal or pelvic cancer, and patients receiving radiotherapy have a high risk developing to an acute radiation-induced diarrhea. Several previous studies have ...discussed the effect of probiotics on prevention of radiation-induced diarrhea, but the results are still inconsistent.
We performed a meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of probiotic supplementation for prevention the radiation-induced diarrhea.
Relevant RCTs studies assessing the effect of probiotic supplementation on clinical outcomes compared with placebo were searched in PubMed, EMBASE, and the Cochrane Library databases (up to March 30 2016). Heterogeneity was assessed with I2 and H2, and publication bias was evaluated using sensitive analysis.
Six trials, a total of 917 participants (490 participants received prophylactic probiotics and 427 participants received placebo), were included in this meta-analysis. Compared with placebo, probiotics were associated with a lower incidence of radiation-induced diarrhea (RR: 0.55; 95% CI: 0.34-0.88; P = 0.01; I2: 87%; 95% CI: 75%-94%; H2: 2.8; 95% CI: 2.0-4.0). However, there is no significant difference in the anti-diarrheal medication use (RR: 0.68; 95% CI: 0.40-1.14; P = 0.14) or bristol scale on stool form (RR: 0.64; 95% CI: 0.35-1.17; P = 0.14).
Probiotics may be beneficial to prevent radiation-induced diarrhea in patients who suffered from abdominal or pelvic cancers during radiotherapy period.
To obtain insight into potential mechanisms underlying the influence of rootstock on scion growth, we performed a comparative analysis of 'Shatangju' mandarin grafted onto 5 rootstocks: Fragrant ...orange (Citrus junons Sieb. ex. Tanaka), Red tangerine (Citrus reticulata Blanco), 'Shatangju' mandarin (Citrus reticulata Blanco), Rough lemon (Citrus jambhiri Lush) and Canton lemon (Citrus limonia Osbeck). The tree size of 'Shatangju' mandarin grafted onto Canton lemon and Rough lemon were the largest, followed by self-rooted rootstock trees, and the lowest tree sizes correspond to ones grafted on Red tangerine and Fragrant orange rootstocks. The levels of indoleacetic acid (IAA) and gibberellin (GA) were significantly and positively related to growth vigor. The differences of gene expression in leaves of trees grafted onto Red tangerine, Canton lemon and 'Shatangju' mandarin were analyzed by RNA-Seq. Results showed that more differentially expressed genes involved in oxidoreductase function, hormonal signal transduction and the glycolytic pathway were enriched in 'Red tangerine vs Canton lemon'. qRT-PCR analysis showed that expression levels of ARF1, ARF8, GH3 and IAA4 were negatively correlated with the growth vigor and IAA content. The metabolism of GA was influenced by the differential expression of KO1 and GA2OX1 in grafted trees. In addition, most of antioxidant enzyme genes were up-regulated in leaves of trees grafted onto Red tangerine, resulting in a higher peroxidase activity. We concluded that different rootstocks significantly affected the expression of genes involved in auxin signal transduction pathway and GA biosynthesis pathway in the grafted plants, and then regulated the hormone levels and their signal pathways.
Pulse streams of many emitters have flexible features and complicated patterns. They can hardly be identified or further processed from a statistical perspective. In this paper, we introduce ...recurrent neural networks (RNNs) to mine and exploit long-term temporal patterns in streams and solve problems of sequential pattern classification, denoising, and deinterleaving of pulse streams. RNNs mine temporal patterns from previously collected streams of certain classes via supervised learning. The learned patterns are stored in the trained RNNs, which can then be used to recognize patterns-of-interest in testing streams and categorize them to different classes, and also predict features of upcoming pulses based on features of preceding ones. As predicted features contain sufficient information for distinguishing between pulses-of-interest and noises or interfering pulses, they are then used to solve problems of denoising and deinterleaving of noise-contaminated and aliasing streams. Detailed introductions of the methods, together with explanative simulation results, are presented to describe the procedures and behaviors of the RNNs in solving the aimed problems. Statistical results are provided to show satisfying performances of the proposed methods.
Ferula ferulaeoides (Steud.) Korov. is a perennial herb that belongs to Umbelliferae (Apiaceae). Its resin and roots have extensive commercial and medicinal value in the Xinjiang region. However, the ...resin‐secreting resin ducts (RDs) of F. ferulaeoides have not been studied in detail. This study used optical and transmission electron microscopy to explore the anatomical features, including the distribution, size, and structure, of the RDs among different organs of F. ferulaeoides. The microstructure data revealed that the RDs consisted of a round lumen, a layer of secretory cells, and multiple layers of sheath cells. Notably, the RDs in stem were arranged alternatively in a multilayered ring with vascular bundles of three distinct sizes. The ultrastructural analysis revealed that organelles in the secretory cells potentially play important roles in resin secretion. Those data may be of great significance to understanding the anatomy of the RDs in Ferula L. and Umbelliferae.
The distribution of the resin ducts in the stem, petiole, scape, and involucre was associated with the vascular bundles, especially in stem, resin ducts were arranged alternatively in a ring shape with vascular bundles of three distinct sizes, and this ring was multilayered.
Resin ducts consisted of a lumen, a layer of secretory cells, and multiple layer of sheath cells.
Resin ducts formation was related to multiple organs of the secretory cells.
Recognition of multifunction radar (MFR) is an open problem in the field of electronic intelligence. Parameters of MFR pulses are generally agile and difficult to distinguish statistically. A ...prospective way to realize credible MFR recognition is mining and exploiting more distinguishable high-dimensional patterns buried in pulse groups, which may be designed for implementing infrequently used radar modes such as target tracking. A high-dimensional pattern is defined according to the agile range and switching law of sequential pulse repetitive intervals within a pulse group. This article establishes deep recurrent neural networks (RNN) to discriminate and coarsely cluster different pulse groups hierarchically with respect to their sequential structures. Afterwards, RNN-based classifiers are trained to extract and exploit features within different pulse group clusters. Distinct degrees of confidence are then attached to these classifiers to indicate the discriminabilities of the corresponding pulse group clusters. The pulse group clustering and classifying models are finally cascaded to form an integrated classification model, which mines distinguishable patterns from sequentially arriving pulse groups of the same radar and accumulate them to realize MFR recognition. Simulation results demonstrate the much improved performance of the proposed method over existing counterparts in different scenarios.
Multi-function radars (MFRs) work with pulse groups, and pulses in different groups are weakly correlated, which greatly increases the difficulty for MFR pulse deinterleaving, especially in cases of ...significant data noises. At present, no relevant research results have been reported to address this problem. In this article, a hierarchical deep learning model will be established to describe the sequential patterns of pulse trains from MFRs. The bottom layer of the model represents discrete pulse groups with continuous vectors, which describes the temporal pattern of pulse groups and makes them machine readable. The top layer uses a recursive neural network to mine the sequential pattern between consecutive pulse groups. The two layers are then synthesized to form a hierarchical model, which is able to describe the semantic correlations between different pulses within MFR pulse trains, and the parameters of subsequent pulse groups and their inner pulses can be predicted based on preceding pulses. Based on the hierarchical model, this article proposes an iterative pulse deinterleaving method and a parallel one for MFRs. Simulation results demonstrate that, the proposed methods perform satisfyingly in separating interleaved pulses from radars of the same or different types, and they are robust to significant pulse noises.
The catalytic conversion of dinitrogen (N2) into ammonia under ambient conditions represents one of the Holy Grails in sustainable chemistry. As a potential alternative to the Haber–Bosch process, ...the electrochemical reduction of N2 to NH3 is attractive owing to its renewability and flexibility, as well as its sustainability for producing and storing value‐added chemicals from the abundant feedstock of water and nitrogen on earth. However, owing to the kinetically complex and energetically challenging N2 reduction reaction (NRR) process, NRR electrocatalysts with high catalytic activity and high selectivity are rare. In this contribution, as a proof‐of‐concept, we demonstrate that both the NH3 yield and faradaic efficiency (FE) under ambient conditions can be improved by modification of the hematite nanostructure surface. Introducing more oxygen vacancies to the hematite surface renders an improved performance in NRR, which leads to an average NH3 production rate of 0.46 μg h−1 cm−2 and an NH3 FE of 6.04 % at −0.9 V vs. Ag/AgCl in 0.10 m KOH electrolyte. The durability of the electrochemical system was also investigated. A surprisingly high average NH3 production rate of 1.45 μg h−1 cm−2 and a NH3 FE of 8.28 % were achieved after the first 1 h chronoamperometry test. This is among the highest FEs reported so far for non‐precious‐metal catalysts that use a polymer‐electrolyte‐membrane cell and is much higher than the FE of precious‐metal catalysts (e.g., Ru/C) under comparable reaction conditions. However, the NH3 yield and the FE dropped to 0.29 μg h−1 cm−2 and 2.74 %, respectively, after 16 h of chronoamperometry tests, which indicates poor durability of the system. Our results demonstrate the important role that the surface states of transition‐metal oxides have in promoting electrocatalytic NRR under ambient conditions. This work may spur interest towards the rational design of electrocatalysts as well as electrochemical systems for NRR, with emphasis on the issue of stability.
A sustainable alternative to the Haber–Bosch process: The introduction of oxygen vacancies into hematite (α‐Fe2O3) nanorods promotes the electrocatalytic synthesis of ammonia from N2 and water at room temperature and atmospheric pressure (see picture). A higher concentration of surface oxygen vacancies leads to both improved NH3 yield and a large NH3 faradaic efficiency.
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