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The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication (JRC) systems, by which the problem of radio frequency spectrum congestion can be well alleviated. A Robust Joint Frequency Spectrum and Power Allocation (RJFSPA) strategy is proposed for the Coexisting Radar and Communication (CRC) system. Specifically, we consider the uncertainty of target Radar Cross Section (RCS) and communication channel gain to formulate a bi-objective optimization model. The joint probabilities that the Cramér-Rao Lower Bound (CRLB) of each target satisfying the localization accuracy threshold and the Communication Data Ratio (CDR) of each user satisfying the communication threshold are simultaneously maximized, under the constraint of the total power budget. A Three-Stage Alternating Optimization Method (TSAOM) is proposed to obtain the Best-Known Pareto Subset (BK-PS) of this problem, where the frequency spectrum, radar power, and communicator power are allocated using the greedy search and standard convex optimization methods, respectively. Simulation results confirm the effectiveness of the proposed RJFSPA strategy, compared with the resource allocation methods in a uniform manner and that ignores the uncertainties. The efficiency of the TSAOM is also verified by the comparison with the exhaustive search-based method.

The present study investigates the effects of congruency and frequency on adjective-noun collocational processing for Chinese learners of English at two proficiency levels based on the data obtained in an online acceptability judgment task. The subject pool of this research included 60 English majors studying at a university in China; 30 were selected as a higher-proficiency group and 30 as a lower-proficiency group according to their Vocabulary Levels Test (Schmitt et al., 2001) scores and their self-reported proficiency in English. The experimental materials were programmed to E-prime 2.0 and included six types of collocations: (1) 15 high-frequency congruent collocations, (2) 15 low-frequency congruent collocations, (3) 15 high-frequency incongruent collocations, (4) 15 low-frequency incongruent collocations, (5) 15 Chinese-only items, and (6) 75 unrelated items for baseline data. The collected response times (RTs) and accuracy rates data were statistically analyzed by the use of an ANOVA test and pairwise comparisons through SPSS 16.0 software. The results revealed that: (1) the adjective-noun collocational processing of Chinese English learners is influenced by collocational frequency, congruency and L2 proficiency; (2) the processing time is affected by the interaction of congruency and frequency; and (3) the interactive effect of L2 proficiency in conjunction with congruency and frequency also influences the processing quality.

Alteration in lipid metabolism is recognized as a hallmark feature of colorectal cancer (CRC). Protein S-palmitoylation plays a critical role in many different cellular processes including protein-lipid interaction. Zinc Finger DHHC-Type Containing 1 (ZDHHC1, also known as ZNF377) belongs to the palmitoyl-transferase ZDHHC family, and is a potential tumor suppressor. However, our knowledge of the functional roles of ZDHHC1 in CRC is limited. We discovered that ZDHHC1 expression was downregulated in CRC tissues and that low levels of ZDHHC1 were associated with unfavorable prognosis. Functional studies showed that ZDHHC1 inhibited CRC cell proliferation and invasion in vitro and in vivo. We also found that lipase G (LIPG) is negatively regulated by ZDHHC1 and plays a key role in CRC cell growth through lipid storage. Additionally, we demonstrated that ZDHHC1 functions as a IGF2BP1-palmitoylating enzyme that induces S-palmitoylation at IGF2BP1-C337, which results in downregulated LIPG expression via m6A modification. Mechanistic investigations revealed that the ZDHHC1/IGF2BP1/LIPG signaling axis is associated with inhibition of CRC cell growth. Our study uncovers the potential role of ZDHHC1 in CRC, including inhibition of CRC growth by reducing the stability of LIPG mRNA in an m6A dependent-manner by palmitoylation of IGF2BP1.

In digital subcarrier multiplexing (DSCM) systems, imperfections in transceiver hardware lead to mismatches between the in-phase (I) and quadrature (Q) components of the signal, causing inter-subcarrier interference and significantly degrading overall system performance. This paper proposes a channel impairment-insensitive scheme for online monitoring of multi-dimensional transceiver impairments tailored for dual-polarization coherent DSCM systems. The proposed scheme enables comprehensive monitoring of transceiver impairments from both the transmitter and receiver, including amplitude imbalance, phase imbalance, and skew between the I and Q components, as well as amplitude imbalance and skew between the X and Y polarizations. In the proposed scheme, frequency-domain pilot tones (FPTs) are inserted into the gap between adjacent subcarriers of the I and Q components of the transmitter signals. The FPTs are extracted for multi-dimensional monitoring of transceiver impairments at the receiver DSP. Unlike conventional techniques relying on intricate DSP algorithms to sequentially compensate for individual impairments, our proposed scheme exhibits remarkable resilience against channel impairments. By utilizing FPTs, the scheme effectively mitigates the influences of chromatic dispersion, polarization variations, and carrier phase noise, enabling transceiver impairment monitoring without compensation for channel impairments. The effectiveness of the proposed scheme is validated in dual-polarization 16-QAM DSCM system with baud rate of 12 Gbaud/SC×4. The experimental results demonstrate that the proposed scheme achieves accurate and wide-range estimation for multi-dimensional transceiver impairments, even in scenarios with coexisting of transceiver and link impairments.

Lithium (Li) is an important resource that drives sustainable mobility and renewable energy. Its demand is projected to continue to increase in the coming decades. However, the risk of Li pollution has also emerged as a global concern. Here, we investigated the pollution characteristics, sources, exposure levels, and associated health risks of Li in the Jinjiang River basin, the largest area for Li2CO3production in China. Our results revealed the dominant role of Li extraction activities in the pollution of the river, with over 95% of dissolved Li in downstream river water being emitted from this source. Moreover, the Li concentration in aquatic plants (i.e., water hyacinth) and animals (i.e., fish) significantly increased from upstream to downstream areas, indicating a significant risk to local aquatic ecosystems. More importantly, our study found that local residents were suffering potential chronic noncarcinogenic health risks primarily from consuming contaminated water and vegetables. We also investigated the pollution characteristics of associated elements present in Li ores (e.g., Rb, Cs, Ni, and F–). By uncovering the remarkable impact of Li extraction activities on the Li content in ecosystems for the first time, our study emphasizes the importance of evaluating Li pollution from Li-related industrial activities, including mining, extraction, and recovery.

Cytoprotective autophagy and an immunosuppressive tumor microenvironment (TME) are two positive promoters for tumor proliferation and metastasis that severely hinder therapeutic efficacy. Inhibiting autophagy and reconstructing TME toward macrophage activation simultaneously are of great promise for effective tumor elimination, yet are still a huge challenge. Herein, a kind of dendrimer-based proton sponge nanocomposites was designed and constructed for tumor chemo/chemodynamic/immunotherapy through autophagy inhibition-promoted cell apoptosis and macrophage repolarization-enhanced immune response. These obtained nanocomposites contain a proton sponge G5AcP dendrimer, a Fenton-like agent Cu(II), and chemical drug doxorubicin (DOX). When accumulated in tumor regions, G5AcP can act as an immunomodulator to realize deacidification-promoted macrophage repolarization toward antitumoral type, which then secretes inflammatory cytokines to activate T cells. They also regulate intracellular lysosomal pH to inhibit cytoprotective autophagy. The released Cu(II) and DOX can induce aggravated damage through a Fenton-like reaction and chemotherapeutic effect in this autophagy-inhibition condition. Tumor-associated antigens are released from these dying tumor cells to promote the maturity of dendritic cells, further activating T cells. Effective tumor elimination can be achieved by this dendrimer-based therapeutic strategy, providing significant guidance for the design of a promising antitumor nanomedicine.

Cu coatings were prepared via the laser-assisted low-pressure cold spray (LPCS) method to study the effect of laser irradiation on microstructure and corrosion behaviors of the coatings. The results reveal that the laser-assisted LPCS-Cu coatings are denser and have better coating−substrate interfacial bonding as compared to LPCS-Cu coating. Laser irradiation improves the overall plastic deformation of deposited particles, resulting in uniform grain refinement of Cu particles. Therefore, the laser-assisted LPCS-Cu coatings show more uniform microstructure and smaller grain size. The results of electrochemical tests demonstrated that laser-assisted LPCS-Cu coatings have higher corrosion potential, lower corrosion current and corrosion rate than the LPCS-Cu coating in 3.5 wt.% NaCl solution. The main reason is that laser irradiation improves the coating density and intimate bonding between particles, and a continuous and dense corrosion product film composed of CuCl and Cu2O is formed on the surface of the laser-assisted LPCS-Cu coating to block the erosion of the corrosive solution.

The optimal resource sharing is key to delivering the promise of radar–communication systems, since the spectrum is shared while competed to perform radar and communication tasks. In this article, a joint target and user assignment as well as dwell time and spectrum allocation strategy is proposed for the distributed radar–communication coexistence network. The optimization model is formulated as minimizing the sum of weighted position Bayesian Cramér–Rao lower bounds under the dwell time and spectrum budgets while meeting the communication downlink requirements. The optimization model is shown to be a mixed-integer programming problem, where the binary assignment variables and continuous resource allocation variables are coupled in both the objective function and constraints. A three-stage alternate optimization method (TSAOM) is developed for problem solving. The target assignment and dwell time allocation (TADTA) problem and the user assignment and spectrum allocation (UASA) problem are separately relaxed as convex ones. Then, a cyclical minimizer framework is applied for the suboptimal solution. Simulations confirm the tracking performance improvement of the proposed strategy compared with two baseline allocation strategies. The results also show the effectiveness and efficiency of the developed TSAOM in comparison with the baseline algorithm.

For space targets with the same shapes and composite relationships in terms of micromotion forms (STSSCRMFs), the accuracy of deep learning approaches with single-domain data is seriously degraded. To address this issue, this article proposes a hybrid neural network based on feature-level fusion that utilizes multidomain radar information to recognize STSSCRMFs. The proposed network simultaneously processes radar cross-section (RCS) time series, high-resolution range profile (HRRP) sequences, and time-frequency (TF) spectrograms through three branches, leveraging their complementary characteristics. Additionally, an attention mechanism is incorporated into the feature-level fusion module to selectively enhance the features of important domains, thereby achieving improved space target recognition. Consequently, the temporal-spatial features of multidomain data are adaptively fused, capturing discriminative and complementary representations, which effectively enhances the accuracy of space target recognition. Experimental results demonstrate the superiority and effectiveness of the proposed method.

This study investigates the target detection and discrimination method in the presence of deceptive jamming using a full-polarization radar network. Within the target detection framework by multiple radars with full-polarization capabilities, we formulate the problem of detecting false targets as a multivariate hypothesis testing task. Based on the generalized likelihood ratio test, we propose a two-stage detection scheme comprising a predetection stage at each radar system and a discrimination stage at the information fusion center. The predetection step is crucial when interference signals cannot deceive all radars. The discrimination stage exploits spatial distinctions in polarization scattering properties between physical and false targets. By leveraging the capabilities of full-polarization radar for acquiring and processing target polarization information, along with the multiview observation potential of radar networks, our proposed approach can obtain more comprehensive scattering information about physical and false targets as well as their differences, thereby achieving enhanced accuracy in target discrimination. We validate the effectiveness of our algorithm through Monte Carlo experiments.

We developed a visible-light-driven expanded EDA complex profile for the synthesis of aza-arenes via aza-6π electrocyclization of 2-styrylanilines with aromatic aldehydes. This protocol relies on the EDA complexes of AlCl3with imine to induce the absorption red-shift to visible light from ultraviolet light. An array of 2,3-disubstituted quinolines were constructed smoothly after excitation with blue-light-emitting diodes at room temperature. In addition, the resultant product, used as a cell permeable lipid droplet-specific probe, shows a low working concentration, a short staining time, and functionality in living and fixed cells.

Optical performance monitoring is vital for enabling dynamically reconfigurable optical networks. This paper introduces a monitoring scheme that is robust to transceiver impairments for coherent digital subcarrier multiplexing (DSCM) systems that simultaneously monitors polarization-dependent loss (PDL), differential group delay (DGD), and chromatic dispersion (CD). In the proposed scheme, a pair of frequency domain pilot tones (FPTs) are inserted into the ${X}$ and ${Y}$ polarizations of the transmitted dual-polarization signal. Both the signal and the FPTs endure identical channel impairments during fiber transmission. At the receiver side, the transmitted FPTs are extracted to monitor the channel impairments. We begin by establishing a simplified system model using a single frequency tone to analyze the effects of system impairments on the frequency domain pilots. Based on this model, CD, PDL, and DGD are jointly and accurately estimated. In addition, the influences of temporal, amplitude, and phase mismatches between in-phase (I) and quadrature (Q) components of transceivers on channel impairment monitoring are completely eliminated. This achieves a channel impairment monitoring scheme that is robust to transceiver impairments. Experimental verification shows that the proposed scheme can jointly and accurately estimate a wide range of CD, PDL, and DGD. Additionally, the proposed monitoring scheme demonstrates strong robustness against transceiver impairments, rapid polarization fluctuations, and amplified spontaneous emission (ASE) noise.

A robust digital polarization-dependent loss (PDL) compensation and monitoring scheme for the coherent digital subcarrier multiplexing (DSCM) system using frequency domain pilot tone (FPT) is proposed and demonstrated. In the transmitted polarization multiplexed DSCM signal, two FPTs with different and asymmetric frequencies are inserted into the X and Y polarizations, respectively. The polarization-dependent matrix, including polarization variation and PDL, can be obtained from the inserted FPTs in the digital signal processing (DSP) at the receiver side. The proposed PDL compensation technique avoids the singularity problem in conventional adaptive equalization schemes and achieves performance close to that of ideal polarization compensation, where the inverse of the channel matrix is used directly to compensate for the received signal. In addition, the proposed scheme can still track polarization variation up to 10 Mrad/s in the presence of 3-dB PDL. For the PDL monitoring, the proposed scheme is insensitive to chromatic dispersion (CD) and shows strong robustness to amplification of spontaneous emission noise (ASE), polarization variation and transmitter (Tx) in-phase/quadrature (IQ) impairments. Both simulation and experimental results indicate that the mean estimation error is less than 0.26 dB for PDL larger than 2 dB in the presence of severe ASE (the resulting BER is about 1e-1). When the CD varies from 0 to 200 km fiber transmission, or the polarization variation rate varies from 0 to 10 Mrad/s, there is no significant deterioration in the mean estimation error and variance of the PDL estimation. Even with Tx IQ skew of 20 ps, IQ phase imbalance of 40 $^{\circ }$ and IQ amplitude imbalance of 4 dB in both polarizations, an estimation error less than 0.5 dB can be achieved for PDL ranging from 1 to 10 dB. Therefore, the proposed scheme provides a robust PDL compensation and monitoring solution for coherent DSCM system.

As a result of residual phase error caused by motion parameters, ground moving targets (GMTs) are defocused and displaced in conventional synthetic aperture radar (SAR) imaging. Although some refocusing algorithms for GMT have been proposed, these methods are difficult to handle geometric correction and sparse recovery in squint mode simultaneously. Deep learning (DL) technology has been successfully used to solve radar imaging problems in microwave vision, where deep unfolding networks (DUNs) and convolutional neural networks (CNNs) are the most widely applied. In this article, a novel GMT imaging network (GMTIm-Net) is proposed for squint SAR and sparse sampling, whose framework combines DUN and CNN advantages. Specifically, we first incorporate a matched filter-based approximated observation model and a minimum entropy-based motion parameter estimation method within a sparse reconstruction framework. An iterative shrinkage threshold algorithm is adopted to solve this framework, and the solution procedure is unfolded as a GMT refocusing network. Then, we introduce plug-and-play (PnP) technology to replace the $\ell _{1}$ norm-based regularizer for improving its noise immunity. Finally, a CNN-based image transformation network is proposed to perform geometric correction of imaging results in squint mode. By inputting the 2-D sparse complex-valued GMT echo, the trained GMTIm-Net can efficiently output focused and corrected GMT images. The experiments demonstrate that our proposed GMTIm-Net outperforms conventional GMT focusing methods in terms of focusing performance and computing efficiency.

Photocatalytic overall water splitting holds great promise for solar-to-hydrogen conversion. Maintaining charge separation is a major challenge but is key to unlocking this potential. Here we discovered a metal–organic framework (MOF) that shows suppressed charge recombination. This MOF features electronically insulated Zn2+nodes and two chemically equivalent, yet crystallographically independent, linkers. These linkers behave as an electron donor–acceptor pair with non-overlapping band edges. Upon photoexcitation, the MOF undergoes a dynamic excited-state structural twist, inducing orbital rearrangements that forbid radiative relaxation and thereby promote a long-lived charge-separated state. As a result, the MOF achieves visible-light photocatalytic overall water splitting, in the presence of co-catalysts, with an apparent quantum efficiency of 3.09 ± 0.32% at 365 nm and shows little activity loss in 100 h of consecutive runs. Furthermore, the dynamic excited-state structural twist is also successfully extended to other photocatalysts. This strategy for suppressing charge recombination will be applicable to diverse photochemical processes beyond overall water splitting.

To achieve accurate prediction of energy demand, this study designed a new method for predicting comprehensive energy demand in industrial parks using echo state networks. Firstly, analyse the comprehensive energy structure of the park, then collect and supplement historical comprehensive energy load consumption data. Secondly, select the factors that affect the load demand forecast, and calculate the comprehensive similarity of similar days of historical energy demand according to the mutual information between the influencing factors. Finally, input the calculation results into the optimised echo state network of the crossbar algorithm, and output the predicted comprehensive energy demand of the park. Experiment shows that after applying this method, the predicted values fluctuate between 1.410%-2.384%, RMSE values fluctuate between 176.4 MW-205.3 MW, indicating that the error of the predicted results using this method is relatively small.

Large eddy simulation was employed to simulate the effects of different vane angles of the primary and pilot stages on the ignition process of a combustor under normal temperature and pressure conditions. The simulation results of different vane angles of the pilot stage were then verified experimentally. Kernel initiation and flame propagation in the cases of ignition success and failure were analysed. It was found that in the ignition failure case, the flame kernels are confined to the downstream zones of the venturi and cannot propagate radially, and almost no negative displacement exists in the axial direction. However, in the ignition success case, sub-fire kernels are always present outside the radial boundary of the primary stage. Five ignition modes were finally proposed: (I1) typical ignition, (I2) relatively high-speed ignition, (I3) single-kernel alternating dominant, (I4) main kernel dominant, and (I5) dual-kernel dominant modes. Using high-speed photography, the flame shape changes and ignition characteristics in the cases of successful and failed ignition with different pilot-stage vane angles were investigated.

Conventional polymeric phase change materials (PCMs) have been widely used due to their high heat storage density, small temperature variation, and nontoxicity. However, the high flammability and unrecyclable problems restrict their applications in energy storage devices (ESDs). Although it is facile to introduce a flame retardant into phase change materials to improve fire resistance, the physical blending will deteriorate the mechanical performance and thermal stability of PCMs. Herein, flame-retardant solid–solid PCMs (FRPCMs) with intrinsic flame retardancy, phase change property, self-healing, and recyclability were synthesized by simultaneously integrating tetrabromobisphenol A (TBBPA) and poly(ethylene glycol) (PEG) into polyurethane network skeletons. PEG ingredients acted as phase change materials, and TBBPA not only worked as an efficient flame retardant but also provided dynamic covalent bonds for thermally induced self-healing and recyclability. FRPCMs possess the highest latent heat of 124.7 J/g, high self-healing ability, and high thermal reliability and recyclability. Besides, with the introduction of TBBPA, the limiting oxygen index (LOI) value and char residue significantly increased, the heat release rate (HRR) and total heat release (THR) values decreased, and most of the FRPCMs reached UL94 V-2 rating as well. Hence, the synthesized FRPCMs could expand the application scope of PCMs for thermal energy storage.

The very high frequency (VHF) radar suffers from the multipath interference and false alarms when tracking low-angle targets. The colocated multiple-input multiple-output (MIMO) radar technique can mitigate these adverse effects with waveform diversity. However, the limited resources restrict the potential of the VHF-MIMO radar. In this article, an array scheduling with power and bandwidth allocation (ASPBA) strategy is proposed for the VHF-MIMO radar tracking low-angle targets. The predicted posterior Cramér-Rao lower bound (PCRLB) under the multipath interference and measurement origin uncertainty effects is derived to serve as the optimization metric. The measurement amplitude information is incorporated into the PCRLB to facilitate target detection and tracking. It is shown that the ASPBA problem is a mixed integer programming and NP-hard problem, where the array, power, and bandwidth allocation variables are both coupled in the objective and constraints. An efficient three-stage-based solution is proposed for problem-solving. The approximated relationship among three variables is derived using Hölder's inequality, and an auxiliary variable is introduced to describe the array contribution. Thereby, the array scheduling is determined by a heuristic rounding algorithm, and the power and bandwidth allocation are achieved using the approximated relationship. Simulation results confirm the effectiveness and efficiency of the proposed ASPBA strategy, compared with state-of-the-art algorithms. It is also shown that the target height is a main factor that influences the resource allocation results in the low-angle tracking scenario.

Range instantaneous Doppler (RID) methods show superior advantages in imaging performance over range-Doppler (RD) algorithms for maneuvering targets. However, traditional RID methods are susceptible to noise and sparse aperture (SA), which makes the resolution of imaging results low and the quality degraded. To improve the imaging performance, we propose an RID high-resolution (HR) imaging framework for maneuvering targets, in which a hybrid transformer-based HR network (HTHRNet) is introduced into the RID method. HTHRNet is trained to map low-resolution (LR) time–frequency distribution (TFD) images to HR TFD images under different conditions. Our deep neural network fuses the convolutional neural network (CNN) and transformer to make full use of their advantages. In addition, we modify the network structure to make HTHRNet training easier and more efficient. The experimental results of TFD data, simulated data, microwave chamber data, and measured data show that the proposed imaging method has superior performance, which further indicates that the complex deep neural network can be well applied to ISAR HR imaging.

A hardware-efficient and robust DSP scheme for the coherent digital subcarrier multiplexing (DSCM) system in presence of transmitter (Tx) in-phase/quadrature (IQ) impairments is introduced and demonstrated. An analytic complex model is given to describe the effects of IQ skew, IQ amplitude and phase imbalance (referred to as IQ mixing) on the DSCM signal. Based on the model, residual frequency offset, polarization and carrier phase noise are jointly recovered using frequency pilot tones (FPT) in presence of IQ mixing. This process avoids the coupling effect of phase noise and Tx IQ mixing in conventional schemes, which allows the Tx IQ mixing to be compensated for by the following joint equalization of conjugate symmetric subcarriers. Compared to the conventional scheme based on widely linear equalizer, the polarization and carrier phase tracking capability of the proposed scheme is robust to IQ mixing. Meanwhile, the proposed scheme can tolerate a wide range of Tx IQ mixing in the presence of residual frequency offset and carrier phase noise. Since polarization crosstalk is eliminated, the signals of the two polarizations can be equalized separately in the proposed scheme, thus reducing the computational complexity by half. Therefore, the proposed scheme provides a hardware-efficient and robust DSP solution for coherent DSCM targeting access network applications.

The significant boost in surface-enhanced Raman scattering (SERS) by the chemical enhancement of semiconducting oxides is a pivotal finding. It offers a prospective path toward high uniformity and low-cost SERS substrates. However, a detailed understanding of factors that influence the charge transfer process is still insufficient. Herein, we reveal the important role of defect-induced band offset and electron lifetime change in SERS evolution observed in a MoO3oxide semiconductor. By modulating the density of oxygen vacancy defects using ultraviolet (UV) light irradiation, SERS is found to be improved with irradiation time in the first place, but such improvement later deteriorates for prolonged irradiation even if more defects are generated. Insights into the observed SERS evolution are provided by ultraviolet photoelectron spectroscopy and femtosecond time-resolved transient absorption spectroscopy measurements. Results reveal that (1) a suitable offset between the energy band of the substrate and the orbitals of molecules is facilitated by a certain defect density and (2) defect states with relatively long electron lifetime are essential to achieve optimal SERS performance.

The study aimed to investigate the short‐term outcomes of hospitalized patients with chronic liver disease (CLDs) and assess the prognostic impact of predisposition and precipitants, which currently remains unclear. The study included 3970 hospitalized patients with CLDs from two prospective longitudinal multicenter studies (NCT02457637 and NCT03641872) conducted in highly endemic hepatitis B virus (HBV) areas. Competing risk analysis was used to evaluate the effect of predispositions, including the etiology and severity of CLDs and precipitants; on sequential 28, 90, and 365‐day liver transplantation (LT)‐free mortality. Among all enrolled patients, 76.8% of adverse outcomes (including death and LT) within one year occurred within 90 days. Compared with alcoholic etiology, the association of HBV etiology with poorer outcomes was remarkably on the 28thday (hazard ratio [HR], 1.81; 95% confidence interval [CI], 1.07–3.06; p= 0.026); however, and diminished or became insignificant at 90 days and 365 days. Cirrhosis increased the adjusted risk for 365‐day (HR, 1.50; CI, 1.13–1.99; p= 0.004) LT‐free mortality when compared with noncirrhosis. In patients with cirrhosis, prior decompensation (PD) independently increased the adjusted risk of 365‐day LT‐free mortality by 1.25‐fold (p= 0.021); however, it did not increase the risk for 90‐day mortality. Neither the category nor the number of precipitants influenced the adjusted risk of 28 or 90‐day LT‐free mortality. The 90‐day outcome should be considered a significant endpoint for evaluating the short‐term prognosis of hospitalized patients with CLD. Predisposing factors, other than etiology, mainly affected the delayed (365‐day) outcome. Timely effective therapy for CLD etiology, especially antiviral treatments for HBV, and post‐discharge long‐term surveillance monitoring in cirrhotic patients undergoing PD are suggested to enhance disease management and reduce mortality. This study revealed the predisposition and precipitant factors that affect short‐term outcomes in hospitalized patients with chronic liver diseases (CLDs). Numerous 1‐year adverse outcomes (death and liver transplantation) occurred within 90 days in CLDs. The 90‐day outcome should be considered a major endpoint for evaluating the short‐term prognosis of hospitalized patients with CLDs. Predisposing factors, other than etiology, mainly impacted the delayed short‐term outcome. Compared to alcoholic etiology, hepatitis B virus (HBV) had a significantly poorer 28‐day outcome. Timely antiviral therapy for HBV and post‐discharge long‐term surveillance monitoring in patients with cirrhosis with prior decompensation are suggested to enhance disease management. Significant findings of the studyNumerous 1‐year adverse outcomes (death and liver transplantation) occurred within 90 days in chronic liver diseases (CLDs). Predisposing factors, other than etiology, mainly impacted the delayed short‐term outcome. Compared to alcoholic etiology, hepatitis B virus (HBV) had a significantly poorer 28‐day outcome.What this study addsThe 90‐day outcome should be considered a major endpoint for evaluating the short‐term prognosis of hospitalized patients with CLDs. Timely antiviral therapy for HBV and postdischarge long‐term surveillance monitoring in patients with cirrhosis with prior decompensation are suggested to enhance disease management. Significant findings of the study Numerous 1‐year adverse outcomes (death and liver transplantation) occurred within 90 days in chronic liver diseases (CLDs). Predisposing factors, other than etiology, mainly impacted the delayed short‐term outcome. Compared to alcoholic etiology, hepatitis B virus (HBV) had a significantly poorer 28‐day outcome. What this study adds The 90‐day outcome should be considered a major endpoint for evaluating the short‐term prognosis of hospitalized patients with CLDs. Timely antiviral therapy for HBV and postdischarge long‐term surveillance monitoring in patients with cirrhosis with prior decompensation are suggested to enhance disease management.

The dwell time resource is usually limited when a phased array radar (PAR) tracks multiple targets. In this paper, a fast solver is proposed for the dwell time allocation (DTA) in the cluttered environments. The optimization model is established as minimizing the worst case of position Bayesian Cramér-Rao lower bounds (BCRLBs) among multiple targets under the constraint of total dwell time budget. The BCRLB minimization is hindered by the intractable information reduction factor (IRF) term, which expresses the information loss due to clutter. By replacing the IRF with its upper bound, the probability of detection (PD), we formulate a closed-form objective function-based optimization problem. By using the strictly decreasing property of the BCRLB, two optimal conditions are proposed to obtain a unique subset of the optimal solutions. Then, the remaining parts can be directly set as the lower or upper bounds. Simulation results confirm the competitive performance and computation saving, compared with existing convex optimization-based methods and the sequential relaxation-based method, especially when facing a large number of targets. The target with a lower signal-to-noise ratio (SNR) will be given more dwell time resources.

A simple and efficient pilot-scale process was developed for the synthesis and purification of α-asaronol ((E)-3′-hydroxyasarone). 4.29 kg of α-asaronol 4(purity 99.92%) was produced in one batch, starting with 2,4,5-trimethoxybenzaldehyde 1and ethyl hydrogen malonate 2as raw materials to form intermediate ethyl (E)-3-(2,4,5-trimethoxyphenyl)acrylate 3(yield 93.3%) by the Knoevenagel condensation reaction, which was then reduced by diisobutylaluminum hydride to produce α-asaronol 4with a yield of 89.2%. Liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analysis revealed four major impurities in the synthesis process, namely, (2,4,5-trimethoxyphenyl)methanol, 3-(2,4,5-trimethoxyphenyl)propan-1-ol, 5,5′-((1E,1′E)-oxybis(prop-1-ene-3,1-diyl)) bis(1,2,4-trimethoxybenzene), and diethyl 2-(2,4,5-trimethoxybenzyl)malonate. By adapting a commonly used recrystallization process through optimization, a large-scale purification method was developed for the purification of α-asaronol, achieving a purity of 99.92% by recrystallization. The pilot study lays the groundwork for the large-scale, high-yield, and high-purity preparation of the candidate drug.

Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88–32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.

Lead halide perovskite nanocrystals (LHP NCs) are regarded as promising emitters for next-generation ultrahigh-definition displays due to their high color purity and wide color gamut. Recently, the external quantum efficiency (EQE) of LHP NC based light-emitting diodes (PNC LEDs) has been rapidly improved to a level required by practical applications. However, the poor operational stability of the device, caused by halide ion migration at the grain boundary of LHP NC thin films, remains a great challenge. Herein, we report a resurfacing strategy via pseudohalogen ions to mitigate detrimental halide ion migration, aiming to stabilize PNC LEDs. We employ a thiocyanate solution processed post-treatment method to efficiently resurface CsPbBr3NCs and demonstrate that the thiocyanate ions can effectively inhibit bromide ion migration in LHP NC thin films. Owing to thiocyanate resurfacing, we fabricated LEDs with a high EQE of 17.3%, a maximum brightness of 48000 cd m–2, and an excellent operation half-life time.

The microstructure evolution and strengthening mechanism of materials after shot peening have always been the focus of research. In this paper, a novel coupled constitutive model was proposed to predict the depth of fine-grain of the 2060-T8 aluminum-lithium (Al–Li) alloy after shot peening. Besides, combining the numerical and experimental methods, the influences of shot size on yield stress, dislocation density, and crystal orientation of Al–Li alloy were systematically studied to reveal the strengthening mechanism. The results show that larger shot sizes can significantly improve the depth of the dislocation density layer and the fine-grain layer of 2060 Al–Li alloy. The depth of the refinement layer increases from 50 μm to 80 μm when the shot size grows from 0.3 mm to 0.6 mm. It is found the depth of the grain refinement layer in the experiment is consistent with the depth where is a sudden change of grain size in the simulation. Additionally, electron backscatter diffraction (EBSD) shows that many sub-grains are produced in the material due to high dislocation density, and the proportion of low-angle grain boundaries (LAGB) increases significantly, from 48.3% to more than 98.4%. The crystal orientation in the grain has changed obviously and Goss texture {100}<001> obtained in the deformation band. In contrast, with the increase in shot size, the proportion of the Schmid factor with a high-level trend increases. However, the hardening effect of the material is much greater than the softening effect which results in higher yield stress and microhardness of the top surface.

Artificial active matters on a macroscopic scale, including vibrating particles, robots, and camphor boats, have attracted increasing attentions due to their uniform properties, rich and easily controllable parameters, convenient observation, and the independence of biochemical processes from physical processes, especially providing these unique advantages for researching the collective behaviors under strong confinement and crowded surroundings. In this review, we present an overview of motion models, mechanisms, and dynamic characteristics of various active particles, both in free and complex media. Additionally, we delve into the collective behaviors of “dry” active matter, covering structural and dynamic properties observed in experiments and theoretical models. We summarize the impact of hydrodynamic interactions on the dynamics and structures of these active particles within hydrodynamic environments. Lastly, we discuss emerging opportunities and challenges for future advancement of macroscopic artificial active matter.

Ammonia (NH3) plays a crucial role in the production of fertilizers, medicines, fibers, etc., which are closely relevant to the development of human society. However, the inert and nonpolar properties of NN seriously hinder artificial nitrogen fixation under mild conditions. Herein, we introduce a novel strategy to enhance the photocatalytic efficiency of N2fixation through the directional polarization of N2by rare earth metal atoms, which act as a local “electron transfer bridge.” This bridge facilitates the transfer of delocalized electrons to the distal N atom and redirects the polarization of adsorbed N2molecules. Taking cerium doped BiOCl (Ce–BiOCl) as an example, our results reveal that the electrons transfer to the distal N atom through the cerium atom, resulting in absorbed nitrogen molecular polarization. Consequently, the polarized nitrogen molecules exhibit an easier trend for NN cleavage and the subsequent hydrogenation process, and exhibit a greatly enhanced photocatalytic ammonia production rate of 46.7 μmol g−1h−1in cerium doped BiOCl, nearly 4 times higher than that of pure BiOCl. The original concept of directional polarization of N2presented in this work not only deepens our understanding of the N2molecular activation mechanism but also broadens our horizons for designing highly efficient catalysts for N2fixation.

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This article proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multiradar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time-domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vector (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.

Further insights on the secondary metabolites of a soft coral-derived fungus Aspergillus versicolorunder the guidance of MS/MS-based molecular networking led to the isolation of seven known cycloheptapeptides, namely, asperversiamides A–C (1–3) and asperheptatides A–D (4–7) and an unusual pyrroloindoline-containing new cycloheptapeptide, asperpyrroindotide A (8). The structure of 8was elucidated by comprehensive spectroscopic data analysis, and its absolute configuration was determined by advanced Marfey’s method. The semisynthetic transformation of 1into 8was successfully achieved and the reaction conditions were optimized. Additionally, a series of new derivatives (10−19) of asperversiamide A (1) was semi-synthesized and their anti-tubercular activities were evaluated against Mycobacterium tuberculosisH37Ra. The preliminary structure−activity relationships revealed that the serine hydroxy groups and the tryptophan residue are important to the activity.

The coherent digital subcarrier multiplexing (DSCM) has recently been proposed as a cost saving solution to realize flexible and scalable point-to-multipoint (PTMP) networks. In this work, a hardware-efficient, ultra-fast and joint polarization and carrier phase tracking scheme for the DSCM systems based on frequency domain pilot tones (named as PTJ) is proposed and demonstrated via both simulations and experiments. By extracting the amplitude and phase of the pilot tones with a low-pass filter (LPF), the transmission matrix of random birefringence and carrier phase can be obtained prior to subcarrier demultiplexing and channel equalization. For the hardware-efficient implementation, a simplified LPF based on sliding window averaging (SWA) is proposed to filter out the pilot tones. Compared to previous scheme using pilot tones to track polarization and carrier phase in a cascaded manner (named as PTC), our proposed scheme shows better BER performance over wide polarization rotation rates and reduces more than 90% of computation resources by the SWA and joint tracking of polarization and carrier phase. The experimental results show that the proposed scheme can track polarization rotation rate up to 20 Mrad/s with the OSNR penalty of 0.2 dB at 7% FEC threshold. This indicates that an OSNR sensitivity gain of 2.8 dB is achieved compared with the PTC scheme. The proposed scheme provides a hardware-efficient and fast-tracking solution to recovery the polarization and carrier phase for digital subcarrier multiplexing (DSCM) and polarization division multiplexing (PDM) based optical fiber communication system.

The micro-Doppler (m-D) effect induced by micro-motion parts of the target degrades the quality of inverse synthetic aperture radar (ISAR) imaging results. To solve this issue, this article proposes a data-driven deep network framework to remove the m-D effect in ISAR imaging, so as to obtain better imaging results. First, a new training data generation method is proposed, which is suitable for both point targets and block targets. Through this method, a plenty of m-D ISAR images and label images without m-D effect can be obtained, which can be used as training data. Then, we improve the UNet as our training network, namely, IUNet. After that, training data are used to train the IUNet. After training, the network learns the mapping relationship between training data, and the network model is saved. Finally, the well-trained network can remove the m-D effects in test ISAR images. The experiments of simulated data and measured data show that the proposed method is superior in terms of eliminating m-D effect than other methods and has good performance.

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

High-resolution range profile (HRRP) sequences have great potential for space target classification because they can provide both scattering information and micromotion information. However, many factors cause an obtained HRRP sequence for a space target to be corrupted in real cases due to noise interference, limited radar resources, and the requirement of multitarget observations. Many space target classification methods cease to be effective when HRRP sequences are corrupted, so classifying space targets with corrupted HRRP sequences is still a challenging problem. To solve this problem, a novel space target classification method based on a temporal–spatial feature aggregation network (TSFA-Net) is proposed by using the corrupted HRRP sequences directly. First, a sequence-to-token module (S2T-module) is designed to extract low-level and fine-grained features from the raw inputs. Second, to effectively model the long-range dependencies among corrupted HRRP sequences and capture global representations without losing target local features, we propose a parallel and dual-branch block, i.e., a temporal–spatial feature aggregation block (TSFA-block), by combining a Transformer network and a convolutional neural network (CNN). Then, via progressively hierarchically stacking TSFA-blocks, a hierarchical temporal–spatial feature aggregation subnetwork (H-TSFA-subnetwork) is constructed to obtain the final temporal–spatial features. Finally, a token-to-label module (T2L-module) is adopted to obtain the classification results. Extensive experiments demonstrate that the proposed method achieves state-of-the-art classification accuracy for space target classification with HRRP sequences, especially under the conditions of a low signal-to-noise ratio and a high missing rate.

AbstractTourism development contributes to higher economic output and is highly integrated with environmental quality and associated technologies. Although many studies explore the impact of tourism on carbon emissions; however, little is known regarding the effects of environmental pollution and technology innovation on tourism growth. Therefore, this study examines the impact of technology innovation and environmental pollution on inbound tourism in emerging economies. In doing so, we employ a recently developed panel quantiles regression and found that technology innovation and economic growth stimulate inbound tourism while increasing emissions limit tourist arrivals. These effects are not equally observed across all quantiles. Particularly, the impact of technology innovation is highest at higher quantiles, while the impact of the emissions is highest at lower quantiles. These results suggest that inbound tourism is asymmetrically affected by technology innovation and environmental quality of host destinations. Hence, emerging economies should encourage sustainable tourism by integrating green technologies and minimizing ecological hazards.

Blastoid/pleomorphic morphology is associated with short survival in mantle cell lymphoma (MCL), but its prognostic value is overridden by Ki-67 in multivariate analysis. Herein, a nuclear segmentation model was developed using deep learning, and nuclei of tumor cells in 103 MCL cases were automatically delineated. Eight nuclear morphometric attributes were extracted from each nucleus. The mean, variance, skewness, and kurtosis of each attribute were calculated for each case, resulting in 32 morphometric parameters. Compared with those in classic MCL, 17 morphometric parameters were significantly different in blastoid/pleomorphic MCL. Using univariate analysis, 16 morphometric parameters (including 14 significantly different between classic and blastoid/pleomorphic MCL) emerged as significant prognostic factors. Using multivariate analysis, Biologic MCL International Prognostic Index (bMIPI) risk group (P = 0.025), low skewness of nuclear irregularity (P = 0.020), and high mean of nuclear irregularity (P = 0.047) emerged as independent adverse prognostic factors. Additionally, a morphometric score calculated from the skewness and mean of nuclear irregularity (P = 0.0038) was an independent prognostic factor in addition to bMIPI risk group (P = 0.025), and a summed morphometric bMIPI score was useful for risk stratification of patients with MCL (P = 0.000001). These results demonstrate, for the first time, that a nuclear morphometric score is an independent prognostic factor in MCL. It is more robust than blastoid/pleomorphic morphology and can be objectively measured.

•Analyzing twenty soil samples from human skeletons across seven archaeological sites in Hubei, China.•Confirming the infection of Chinese liver flukes among populations of 3rd century BCE to 1st century.•CE Reviewing the archaeoparasitology research in mainland China.•Exploring the contributing factors to the infection of Chinese liver fluke, including environment, dietary behaviors, and feces management.•Proposing ancient Hubei as a possible endemic region of Chinese liver fluke.

Phosphor-converted white light-emitting diodes (pc-wLEDs) are promising candidates for next-generation solid-state lighting and display technologies. However, most of the conventional phosphors in pc-wLED devices suffer from serious thermal quenching (TQ) at high temperatures. Herein, we investigate an unconventional high-efficiency metal–halide cluster-based phosphor with dynamic Cu–Cu interactions that can resist the TQ effect of photoluminescence. The temperature-dependent structure and solid-state and in situNMR spectroscopy reveal that the weakening of the Cu–Cu interaction in such a phosphor system enables the electronic structural transition from a bonding to a nonbonding state and hence sustains the PL efficiency at high temperatures (up to 100 °C). The pc-wLEDs incorporating the zero-TQ phosphor show a rapid brightness rise even at a high bias current (1000 mA) with a color rendering index as high as 90, comparable to the commercial phosphor-based prototype LEDs (e.g., YAG:Ce3+). This work establishes a novel prototype of a cluster-based phosphor featuring dynamic intermetallic interactions, which paves the way for the exploration of pc-wLEDs against thermal quenching.

In order to improve the bioactivity of 316L stainless steel, a titanium layer was prepared on the surface of 316L by laser cladding (LC), followed by plasma electrolytic oxidation (PEO) to form a porous ceramic coating on titanium layer. The morphologies, microstructure and compositions of the coated samples were characterized by 3D surface profiler, SEM, EDS, XRD and XPS. The corrosion resistance and bioactivity of the coatings were evaluated by potentiodynamic polarization and immersion test in simulated body fluid (SBF), respectively. The results showed that the porous ceramic coating mainly consisted of anatase and rutile, and highly crystalline HA was also detected. The main elements of the PEO coating are Ca, P, Ti and O. The LC+PEO composite bio-coating has more excellent corrosion resistance than the 316L substrate in simulated body fluid. Furthermore, the composite coating could effectively improve the bioactivity of 316L stainless steel.

Semiconductor materials were adopted in their solid states for photovoltaic applications owing to their nonsolubility and/or breaking of the photogenerated carrier transfer pathway in solution. The liquid-state photovoltaic device fills in a gap between currently prevailing full-solid-state and solid–liquid-state solar cells; however, reports on the photovoltaic effect from realistic semiconductor solution are absent so far. Herein, we report a hybrid inorganic–organic ionic semiconductor [Ni(Phen)3][V14O34Cl]Cl (Phen = 1,10-phenanthroline) and observe its photovoltaic effect in ionic liquid solution. This photovoltaic effect arises as a result of charge transfer between the coordination cation and inorganic polyoxovanadate in solution under illumination and subsequent transfer to electrodes. The liquid-state photovoltaic device (cell configuration: carbon cloth||[Ni(Phen)3][V14O34Cl]Cl in ionic liquid||Al foam) yields an open-circuit voltage of ca. 1.199 V and a photocurrent density of 3.268 mA cm–2upon illumination using an air mass of 1.5 (100 mW cm–2) at 80 °C with a fill factor of 42.48% and an efficiency of 1.665%. This novel type of hybrid ionic semiconductorpossesses great structural tunability for an optimized photovoltaic performance.

The efficient clinical treatment of oral squamous cell carcinoma (OSCC) is still a challenge that demands the development of effective new drugs. Phenformin has been shown to produce more potent anti-tumor activities than metformin on different tumors, however, not much is known about the influence of phenformin on OSCC cells. We found that phenformin suppresses OSCC cell proliferation, and promotes OSCC cell autophagy and apoptosis to significantly inhibit OSCC cell growth both in vivo and in vitro. RNA-seq analysis revealed that autophagy pathways were the main targets of phenformin and identified two new targets DDIT4 (DNA damage inducible transcript 4) and NIBAN1 (niban apoptosis regulator 1). We found that phenformin significantly induces the expression of both DDIT4 and NIBAN1 to promote OSCC autophagy. Further, the enhanced expression of DDIT4 and NIBAN1 elicited by phenformin was not blocked by the knockdown of AMPK but was suppressed by the knockdown of transcription factor ATF4 (activation transcription factor 4), which was induced by phenformin treatment in OSCC cells. Mechanistically, these results revealed that phenformin triggers endoplasmic reticulum (ER) stress to activate PERK (protein kinase R-like ER kinase), which phosphorylates the transitional initial factor eIF2, and the increased phosphorylation of eIF2 leads to the increased translation of ATF4. In summary, we discovered that phenformin induces its new targets DDIT4 and especially NIBAN1 to promote autophagic and apoptotic cell death to suppress OSCC cell growth. Our study supports the potential clinical utility of phenformin for OSCC treatment in the future.

Background: He long noncoding RNA small nucleolar host RNA 5 (SNHG5) is highly expressed in many cancers, and there is a notable correlation between the elevated expression of SNHG5and survival outcome in cancer patients. The objective of this study was to conduct a meta-analysis to evaluate the correlation between SNHG5expression and the clinical outcome of cancer patients. Methods: Six relevant electronic databases were exhaustively searched, and, depending on the inclusion and exclusion criteria, appropriate literature was obtained. The Newcastle-Ottawa Scale (NOS) score was utilized to evaluate the quality of the research for every article included, and pertinent data from each study were carefully extracted. Hazard ratios (HRs), odds ratios (ORs) and 95% confidence intervals (CIs) were combined to explore the association of SNHG5expression levels with cancer prognosis, and sensitivity analyses and assessments of publication bias were also conducted to investigate any possibility in the publication of the studies. Results: Eleven studies encompassing 721 patients were ultimately collected. When combined, the hazard ratios (HRs) revealed a substantial direct correlation between elevated SNHG5expression and an unfavourable prognosis for cancer patients (HR = 1.90, 95% CI 0.87–4.15); however, the correlation did not reach statistical significance. Furthermore, high SNHG5expression was predictive of advanced TNM stage (OR: 1.988, 95% CI 1.205–3.278) and larger tumour size (OR: 1.571, 95% CI 1.090–2.264); moreover, there were nonsignificant relationships between SNHG5expression and DM (OR: 0.449, 95% CI 0.077–2.630), lymph node metastasis (OR: 1.443, 95% CI 0.709–2.939), histological grade (OR: 2.098, 95% CI 0.910–4.838), depth of invasion (OR: 1.106, 95% CI 0.376–3.248), age (OR: 0.946, 95% CI 0.718–1.247) and sex (OR: 0.762, 95% CI 0.521–1.115). Conclusion: SNHG5expression is typically increased in the majority of tumour tissues. Elevated SNHG5expression may indicate poor prognosis in cancer patients. Therefore, SNHG5is a promising potential therapeutic target for tumours and a reliable prognostic biomarker.