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Continued development of the Sonogashira coupling has made it a well established and versatile reaction for the straightforward formation of C–C bonds, forging the carbon skeletons of broadly useful functionalized molecules. However, asymmetric Sonogashira coupling, particularly for C( sp 3 )–C( sp ) bond formation, has remained largely unexplored. Here we demonstrate a general stereoconvergent Sonogashira C( sp 3 )–C( sp ) cross-coupling of a broad range of terminal alkynes and racemic alkyl halides (>120 examples) that are enabled by copper-catalysed radical-involved alkynylation using a chiral cinchona alkaloid-based P,N-ligand. Industrially relevant acetylene and propyne are successfully incorporated, laying the foundation for scalable and economic synthetic applications. The potential utility of this method is demonstrated in the facile synthesis of stereoenriched bioactive or functional molecule derivatives, medicinal compounds and natural products that feature a range of chiral C( sp 3 )–C( sp / sp 2 / sp 3 ) bonds. This work emphasizes the importance of radical species for developing enantioconvergent transformations. Asymmetric Sonogashira C( sp 3 )–C( sp ) couplings provide complementary approaches to established C( sp 3 )–C( sp 2 / sp 3 ) couplings for chiral C–C bond formation; however, relatively few reactions have been developed. Now, a versatile, enantioconvergent Sonogashira coupling via a radical intermediate has been developed. The approach uses a copper catalyst featuring a multidentate electron-rich cinchona alkaloid-derived ligand.

Osmotically assisted reverse osmosis (OARO) has become an emerging membrane technology to tackle the limitations of a reverse osmosis (RO) process for water desalination. A strong membrane that can withstand a high hydraulic pressure is crucial for the OARO process. Here, we develop ultra-strong polymeric thin film composite (TFC) hollow fiber membranes with exceptionally high hydraulic burst pressures of up to 110 bar, while maintaining high pure water permeance of around 3 litre/(m 2  h bar) and a NaCl rejection of about 98%. The ultra-strong TFC hollow fiber membranes are achieved mainly by tuning the concentration of the host polymer in spinning dopes and engineering the fiber dimension and morphology. The optimal TFC membranes display promising water permeance under the OR and OARO operation modes. This work may shed new light on the fabrication of ultra-strong TFC hollow fiber membranes for water treatments and desalination. Osmotically assisted reverse osmosis can overcome limitations of the reverse osmosis process but a strong membrane which can withstand a high hydraulic pressure is crucial. Here, the authors develop strong polymer thin film composite hollow fiber membranes with exceptionally high hydraulic burst pressures of up to 110 bar, while maintaining high water permeance and salt rejection.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low-field NMR (LF-NMR) relaxation characteristic data from various sample oils were analyzed. We also suggest a new method for predicting the size of edible oil molecules using LF-NMR relaxation time. According to the relative molecular mass, the carbon chain length and the transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid were displayed, reflecting the composition information of different oils. We further studied the correlation between the rotation correlation time and the molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation and molecular dynamics of complex fluids. The measurements of grease in the free-fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured using low-field nuclear magnetic resonance.

Silicon–based composites are promising candidates as the next–generation anode materials for high–performance lithium–ion batteries (LIBs) due to their high theoretical specific capacity, abundant reserves, and reliable security. However, expensive raw materials and complicated preparation processes give silicon carbon anode a high price and poor batch stability, which become a stumbling block to its large–scale practical application. In this work, a novel ball milling–catalytic pyrolysis method is developed to fabricate a silicon nanosheet@amorphous carbon/N–doped graphene (Si–NSs@C/NG) composite with cheap high–purity micron–size silica powder and melamine as raw materials. Through systematic characterizations such as XRD, Raman, SEM, TEM and XPS, the formation process of NG and a Si–NSs@C/NG composite is graphically demonstrated. Si–NSs@C is uniformly intercalated between NG nanosheets, and these two kinds of two–dimensional (2D) materials are combined in a surface–to–surface manner, which immensely buffers the stress changes caused by volume expansion and contraction of Si–NSs. Attributed to the excellent electrical conductivity of graphene layer and the coating layer, the initial reversible specific capacity of Si–NSs@C/NG is 807.9 mAh g−1 at 200 mA g−1, with a capacity retention rate of 81% in 120 cycles, exhibiting great potential for application as an anode material for LIBs. More importantly, the simple and effective process and cheap precursors could greatly reduce the production cost and promote the commercialization of silicon/carbon composites.

MicroRNA-361-5p (miR-361-5p) is a tumor suppressor miRNA that is dysregulated in several types of human cancer. However, the functional significance of miR-361-5p in hepatocellular carcinoma (HCC) is unclear. This study explored the biological function of miR-361-5p in regulating the progression of HCC and the underlying molecular mechanism. RT-qPCR analysis showed that miR-361-5p was downregulated in HCC tissues and cell lines. Functional analysis revealed that miR-361-5p acted as a tumor suppressor, inhibiting cell proliferation, migration, and invasion in HCC cell lines. Bioinformatics analyses identified Twist1 as a direct target of miR-361-5p, which was validated by dual-luciferase reporter assays, RT-qPCR, and western blotting. Rescue experiments indicated that Twist1 may mediate the tumor-suppressive effect of miR-361-5p in HCC cells, and this was supported by the effect of miR-361-5p on inhibiting the epithelial-mesenchymal transition (EMT) by targeting Twist1. This study is the first to suggest that miR-361-5p inhibits tumorigenesis and EMT in HCC by targeting Twist1. These findings are valuable for the diagnosis and clinical management of HCC.

Although various polymer membrane materials have been applied to gas separation, there is a trade-off relationship between permeability and selectivity, limiting their wider applications. In this paper, the relationship between the gas permeation behavior of polyphenylsulfone(PPSU)-based materials and their chemical structure for gas separation has been systematically investigated. A PPSU homopolymer and three kinds of 3,3′,5,5′-tetramethyl-4,4′-biphenol (TMBP)-based polyphenylsulfone (TMPPSf) copolymers were synthesized by controlling the TMBP content. As the TMPPSf content increases, the inter-molecular chain distance (or d-spacing value) increases. Data from positron annihilation life-time spectroscopy (PALS) indicate the copolymer with a higher TMPPSf content has a larger fractional free volume (FFV). The logarithm of their O2, N2, CO2, and CH4 permeability was found to increase linearly with an increase in TMPPSf content but decrease linearly with increasing 1/FFV. The enhanced permeability results from the increases in both sorption coefficient and gas diffusivity of copolymers. Interestingly, the gas permeability increases while the selectivity stays stable due to the presence of methyl groups in TMPPSf, which not only increases the free volume but also rigidifies the polymer chains. This study may provide a new strategy to break the trade-off law and increase the permeability of polymer materials largely.

Background Glucocorticoid (GC) response heterogeneity has been recognized as an unfavorable prognostic factor, yet the underlying mechanism remains elusive. In this study, we endeavored to investigate the potential causes from an epigenetic perspective. Methods The protein expression levels of the glucocorticoid receptor (GR), acetylated GC receptor (Ac-GR), acetylated histone3 (Ac-H3), histone4 (Ac-H4), and the activity of nuclear factor-κB (NF-κB) were quantified in the peripheral blood lymphocytes of patients exhibiting diverse GC responses. Results Before GC treatment, the study included 32 children with steroid-sensitive nephrotic syndrome (SSNS) and 15 children with steroid-resistant nephrotic syndrome (SRNS). The expression levels of Ac-GR, Ac-H3, Ac-H4, and NF-κB activity were significantly different among the control, SSNS, and SRNS groups ( p -values < 0.05). Specifically, the expressions were relatively low in the control group, moderately high in the SSNS group, and significantly elevated in the SRNS group. After GC treatment, the expressions of Ac-GR, Ac-H3, Ac-H4, and NF-κB activity decreased in the SSNS children (mean = 0.397, SD = 0.049, p  = 4.42E-11 for NF-κB; mean = 0.429, SD = 0.107, p  = 8.41E-6 for Ac-GR, mean = 0.652, SD = 0.126, p  = 5.38E-8 for Ac-H3, and mean = 0.599, SD = 0.098, p  = 1.24E-7 for Ac-H4), while they increased in the SRNS patients (mean = 0.576, SD = 0.064, p  = 4.53E-5 for NF-κB, mean = 0.498, SD = 0.113, p  = 8.81E-3 for Ac-GR). The correlations among these expressions differed between the SSNS and SRNS groups. In the SSNS group, a positive correlation was identified between NF-κB (mean = -0.156, SD = 0.090) activity and Ac-GR (mean = -0.148, SD = 0.157) protein expression after GC treatment ( r  = 0.392, p  = 0.026), whereas a negative correlation was observed in the SRNS group (mean = 0.195, SD = 0.130 for NF-κB, mean = 0.173, SD = 0.221 for Ac-GR, r = -0.367, p  = 0.178). Additionally, a positive correlation for the difference between Ac-H3 and Ac-H4 expressions was observed in the SSNS group (mean = -0.239, SD = 0.190 for Ac-H3, mean = -0.203, SD = 0.168 for Ac-H4, r  = 0.394, p  = 0.026), which was absent in the SRNS group. Conclusion The expression levels of Ac-GR, Ac-H3, and Ac-H4 differed significantly among children’s patients with primary nephrotic syndrome (PNS) who manifested distinct GC responses. It is suggested that GC therapy may has a direct impact on the acetylation of GR, H3, and H4.

The target signal-to-noise ratio (SNR) can be notably improved by coherent-on-receive synthesis (CoRS) in distributed coherent aperture radar (DCAR). A core challenge of CoRS is to estimate the coherent parameters (CPs), including time, frequency, and phase, in order to cohere the multi-radar within DCAR. Conventional methods usually rely on the target’s own information to estimate the CPs, which is not available in highly dynamic environments. Additionally, the CPs of different targets, especially the phase, are unequal in high-frequency systems. This means that we cannot directly use the CPs of one target to compensate for others. To address these issues, an adaptive CoRS method using the dominant scatterer is proposed for millimeter-wave (MMW) DCAR in this paper. The basic idea is to correct the CPs of the dominant scatterer to compensate for other targets. The novelty lies in the adaptive phase compensation based on the estimated CPs. This phase compensation depends on a series of discrete phase values, which are derived from the limit of synthesis loss within a given configuration. Hence, this method avoids the requirement of prior information or massive searches for the possible locations of other targets. Moreover, the dominant scatterer in this work is an unknown target with strong scattering points in radar detection scenarios, and we focus on analyzing its selection criteria. To validate the proposed method, a prototype system has been fabricated and evaluated through experiments. It is demonstrated that the multi-target can realize CoRS effectively, thus enhancing the target SNR.

In this paper, we explore the problem of direction-of-arrival (DOA) estimation for a non-uniform linear array (NULA) under strong noise. The compressed sensing (CS)-based methods are widely used in NULA DOA estimations. However, these methods commonly rely on the tuning of parameters, which are hard to fine-tune. Additionally, these methods lack robustness under strong noise. To address these issues, this paper proposes a novel DOA estimation approach using a deep neural network (DNN) for a NULA in a low SNR. The proposed network is designed based on the denoising convolutional neural network (DnCNN) and the alternating direction method of multipliers (ADMM), which is dubbed as LDnADMM-Net. First, we construct an unfolded DNN architecture that mimics the behavior of the iterative processing of an ADMM. In this way, the parameters of an ADMM can be transformed into the network weights, and thus we can adaptively optimize these parameters through network training. Then, we employ the DnCNN to develop a denoising module (DnM) and integrate it into the unfolded DNN. Using this DnM, we can enhance the anti-noise ability of the proposed network and obtain a robust DOA estimation in a low SNR. The simulation and experimental results show that the proposed LDnADMM-Net can obtain high-accuracy and super-resolution DOA estimations for a NULA with strong robustness in a low signal-to-noise ratio (SNR).

The detection and quantitative analysis of shale components are of great significance for comprehensively understanding the properties of shale, assessing its resource potential and promoting efficient development and utilization of resources. The low-field NMR T1-T2 two-dimensional spectrum can detect shale components non-destructively and effectively. Unfortunately, due to its complexity, the two-dimensional spectral results of low-field NMR are mainly analyzed using manual qualitative analysis, and accurate results of the composition cannot be obtained. Since the information contained in its two-dimensional map is determined by the morphological texture and the position in the map, commonly used image analysis networks cannot adapt. In order to solve these problems, this paper improves a novel Faster Region-based Convolutional Neural Network (Faster-RCNN). Compared with previous models, the improved Faster-RCNN has better image classification and visual key point estimation capabilities. The results show that compared with traditional methods, the deep learning method using this model can directly obtain key information such as kerogen and movable oil and gas content in rocks. The information provided in this study can help complement and improve the development of analytical methods for low-field 2D NMR spectra.