Synergistic catalysis of the N-hydroxyphthalimide on flower-like bimetallic metal-organic frameworks for boosting oxidative desulfurization

Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.

Pachymic acid exerts antitumor activities by modulating the Wnt/β-catenin signaling pathway via targeting PTP1B

Objective:To determine the inhibitory effects of pachymic acid on lung adenocarcinoma(LUAD)cells and elucidate its underlying mechanism.Methods:CCK-8,wound healing,Transwell,Western blot,tube formation,and immunofluorescence assays were carried out to measure the effects of various concentrations of pachymic acid on LUAD cell proliferation,metastasis,angiogenesis as well as autophagy.Subsequently,molecular docking technology was used to detect the potential targeted binding association between pachymic acid and protein tyrosine phosphatase 1B(PTP1B).Moreover,PTP1B was overexpressed in A549 cells to detect the specific mechanisms of pachymic acid.Results:Pachymic acid suppressed LUAD cell viability,metastasis as well as angiogenesis while inducing cell autophagy.It also targeted PTP1B and lowered PTP1B expression.However,PTP1B overexpression reversed the effects of pachymic acid on metastasis,angiogenesis,and autophagy as well as the expression of Wnt3a andβ-catenin in LUAD cells.Conclusions:Pachymic acid inhibits metastasis and angiogenesis,and promotes autophagy in LUAD cells by modulating the Wnt/β-catenin signaling pathway via targeting PTP1B.

Enterogenic Stenotrophomonas maltophilia migrates to the mammary gland to induce mastitis by activating the calcium-ROS-AMPK-mTOR-autophagy pathway

Background Mastitis is an inflammatory disease of the mammary gland that has serious economic impacts on the dairy industry and endangers food safety.Our previous study found that the body has a gut/rumen-mammary gland axis and that disturbance of the gut/rumen microbiota could result in‘gastroenterogenic mastitis'.However,the mechanism has not been fully clarified.Recently,we found that long-term feeding of a high-concentrate diet induced mastitis in dairy cows,and the abundance of Stenotrophomonas maltophilia(S.maltophilia)was significantly increased in both the rumen and milk microbiota.Accordingly,we hypothesized that‘gastroenterogenic mastitis'can be induced by the migration of endogenous gut bacteria to the mammary gland.Therefore,this study investigated the mechanism by which enterogenic S.maltophilia induces mastitis.Results First,S.maltophilia was labelled with superfolder GFP and administered to mice via gavage.The results showed that treatment with S.maltophilia promoted the occurrence of mastitis and increased the permeability of the blood-milk barrier,leading to intestinal inflammation and intestinal leakage.Furthermore,tracking of ingested S.maltophilia revealed that S.maltophilia could migrate from the gut to the mammary gland and induce mastitis.Subsequently,mammary gland transcriptome analysis showed that the calcium and AMPK signalling pathways were significantly upregulated in mice treated with S.maltophilia.Then,using mouse mammary epithelial cells(MMECs),we verified that S.maltophilia induces mastitis through activation of the calcium-ROS-AMPK-mTOR-autophagy pathway.Conclusions In conclusion,the results showed that enterogenic S.maltophilia could migrate from the gut to the mammary gland via the gut-mammary axis and activate the calcium-ROS-AMPK-mTOR-autophagy pathway to induce mastitis.Targeting the gut-mammary gland axis may also be an effective method to treat mastitis.

Sm-like 5 knockdown inhibits proliferation and promotes apoptosis of colon cancer cells by upregulating p53,CDKN1A and TNFRSF10B

BACKGROUND The role of Sm-like 5(LSM5)in colon cancer has not been determined.In this study,we investigated the role of LSM5 in progression of colon cancer and the potential underlying mechanism involved.AIM To determine the role of LSM5 in the progression of colon cancer and the potential underlying mechanism involved.METHODS The Gene Expression Profiling Interactive Analysis database and the Human Protein Atlas website were used for LSM5 expression analysis and prognosis analysis.Real-time quantitative polymerase chain reaction and Western blotting were utilized to detect the expression of mRNAs and proteins.A lentivirus targeting LSM5 was constructed and transfected into colon cancer cells to silence LSM5 expression.Proliferation and apoptosis assays were also conducted to evaluate the growth of the colon cancer cells.Human GeneChip assay and bioinformatics analysis were performed to identify the potential underlying mechanism of LSM5 in colon cancer.RESULTS LSM5 was highly expressed in tumor tissue and colon cancer cells.A high expression level of LSM5 was related to poor prognosis in patients with colon cancer.Knockdown of LSM5 suppressed proliferation and promoted apoptosis in colon cancer cells.Silencing of LSM5 also facilitates the expression of p53,cyclin-dependent kinase inhibitor 1A(CDKN1A)and tumor necrosis factor receptor superfamily 10B(TNFRSF10B).The inhibitory effect of LSM5 knockdown on the growth of colon cancer cells was associated with the upregulation of p53,CDKN1A and TNFRSF10B.CONCLUSION LSM5 knockdown inhibited the proliferation and facilitated the apoptosis of colon cancer cells by upregulating p53,CDKN1A and TNFRSF10B.

A comprehensive review of heart rate variability as an indicator in the regulation of the autonomic nervous system by acupuncture:a bibliometric analysis

This study sought to conduct a bibliometric analysis of acupuncture studies focusing on heart rate variability(HRV)and to investigate the correlation between various acupoints and their effects on HRV by utilizing association rule mining and network analysis.A total of 536 publications on the topic of acupuncture studies based on HRV.The disease keyword analysis revealed that HRV-related acupuncture studies were mainly related to pain,inflammation,emotional disorders,gastrointestinal function,and hypertension.A separate analysis was conducted on acupuncture prescriptions,and Neiguan(PC6)and Zusanli(ST36)were the most frequently used acupoints.The core acupoints for HRV regulation were identified as PC6,ST36,Shenmen(HT7),Hegu(LI4),Sanyinjiao(SP6),Jianshi(PC5),Taichong(LR3),Quchi(LI11),Guanyuan(CV4),Baihui(GV20),and Taixi(KI3).Additionally,the research encompassed 46 reports on acupuncture animal experiments conducted on HRV,with ST36 being the most frequently utilized acupoint.The research presented in this study offers valuable insights into the global research trend and hotspots in acupuncture-based HRV studies,as well as identifying frequently used combinations of acupoints.The findings may be helpful for further research in this field and provide valuable information about the potential use of acupuncture for improving HRV in both humans and animals.

Conceptual design of a Cs2LiLaBr6 scintillator‑based neutron total cross section spectrometer on the back‑n beam line at CSNS

To reduce the experimental uncertainty in the 235 U resonance energy region and improve the detection efficiency for neutron total cross section measurements compared with those obtained with the neutron total cross section spectrometer(NTOX), a dedicated lithium-containing scintillation detector has been developed on the Back-n beam line at the China Spallation Neutron Source. The Fast Scintillator-based Neutron Total Cross Section(FAST) spectrometer has been designed based on a Cs2Li La Br6(CLLB) scintillator considering the γ-ray flash and neutron environment on the Back-n beam line. The response of the CLLB scintillator to neutrons and γ-rays was evaluated with different 6Li/7 Li abundance ratios using Geant4. The neutron-γdiscrimination performance of the CLLB has been simulated considering different scintillation parameters, physical designs,and light readout modes. A cubic 6Li-enriched( > 90%) CLLB scintillator, which has a thickness of 4-9 mm and side length of no less than 50 mm to cover the Φ 50 mm neutron beam at the spectrometer position, has been proposed coupling to a side readout SiPM array to construct the FAST spectrometer. The developed simulation techniques for neutron-γ discrimination performance could provide technical support for other neutron-induced reaction measurements on the Back-n beam line.

Micropropagation of Taxodium hybrid(T.distichum×T.mucronatum)

This study was carried out to optimize the culture media for the micropropagation of Taxodium hybrid Zhongshanshan（ T. distichum × T. mucronatum）.Using the tender stems of Zhongshanshan 301 as the explants,the effects of NAA,6-BA,IBA and KT on the induction of differentiation,proliferation and rooting were evaluated on MS or 1/2 MS medium. The results showed that Zhongshanshan can be proliferated via tissue culture. The combined use of NAA and 6-BA in MS medium greatly promoted the differentiation of Zhongshanshan explants,and the optimal medium was MS ＋ 0. 2 mg/L NAA ＋ 0. 4 mg/L6-BA. The optimal medium for the proliferation of differentiated buds was MS ＋ 0. 3 mg/L NAA ＋ 0. 4 mg/L 6-BA,on which the proliferation rate was up to 3. 8. 1/2 MS medium was more conducive than MS medium to the induction of rooting. The optimal medium for the rooting of tissue-cultured Zhongshanshan shoots was 1/2 MS ＋ 0. 3 mg/L IBA ＋ 0. 2 mg/L NAA.

Esophageal Cancer Patients with Less Lymph Node Dissection May Have Better Prognosis

Background: China is a high incidence area of esophageal cancer. Esophageal squamous cell carcinoma recurrence and mortality rates are relatively high. Recent studies show that the recurrence rate remains very high even through the implementation of lymph node expanding dissection. Methods and Results: In order to study the relationship between lymph node dissection number and survival of the patients with esophageal squamous cell carcinoma, 407 cases of esophageal cancer are selected in the First Affiliated Hospital of Xi’an Jiaotong University from January 2009 to June 2013. There were 15 cases without surgery, while the rest of the 392 patients were post-operation in follow-up. 54 patients were lost in follow-up, and the rate was 13.8%. Finally, there were 338 patients entered into our research. The median age was 58 (37 - 81), males accounted for 79%. The number of lymph node dissection is for a total of 2091, and a median of 5. Positive lymph nodes are 400, while the total positive rate is 19.1%. Conclusion: The number of lymph node dissection is divided into 3 groups that are 0 to 6, 7 to 11, 12 or more into three grades, and reduced number of lymph node dissection may prolong the survival (P < 0.05). The number of lymph node dissection should be as less as possible unless there is definitely positive lymph node metastasis.

LncRNA H19在食管癌组织中的表达及其与细胞转移的关系

目的探究长链非编码RNA H19(LncRNA H19)在食管癌组织中表达的临床意义及其对人食管癌细胞TE1体外转移能力的影响。方法选取2015年6月—2017年3月齐齐哈尔医学院附属第三医院心胸外科收治的72例食管癌患者的肿瘤组织及相应癌旁组织,采用实时荧光定量聚合酶链反应检测组织中LncRNA H19的表达情况,分析患者肿瘤组织中LncRNA H19与性别、年龄、肿瘤组织分化程度及TNM分期的关系。实验组和对照组分别转染表达Lnc RNA H19的慢病毒载体及空白载体于人食管癌细胞TE1中,采用Transwell侵袭及迁移模型检测两组细胞的转移能力,Western blotting检测两组细胞基质金属蛋白酶2(MMP-2)及金属基质蛋白酶9(MMP-9)的表达。结果食管癌组织中Lnc RNA H19的表达高于癌旁组织(P <0.05);不同TNM分期、淋巴结转移率及组织分化程度患者的LncRNA H19表达水平有差异(P <0.05)。实验组Lnc RNA H19表达水平高于对照组(P<0.05);实验组侵袭及迁移细胞数高于对照组(P<0.05);实验组MMP-2及MMP-9蛋白表达水平高于对照组(P<0.05)。结论 LncRNA H19在食管癌组织中高表达,且可通过上调MMP的表达促进食管癌细胞的转移。

Performance evaluation of two types of heated cables for distributedtemperature sensing-based measurement of soil moisture content

Distributed temperature sensing(DTS)using heated cables has been recently developed for distributed monitoring of in-situ soil moisture content.In this method,the thermal and electrical properties of heated cables have a significant influence on the measurement accuracy of soil moisture content.In this paper,the performances of two heated cables,i.e.the carbon-fiber heated cable(CFHC)and the metalnet heated cable(MNHC),are studied in the laboratory.Their structures,uniformity in the axial direction,measurement accuracy and suitability are evaluated.The test results indicate that the MNHC has a better uniformity in the axial direction than CFHC.Both CFHC and MNHC have high measurement accuracy.The CFHC is more suitable for short-distance measurement(500 m),while the MNHC can be used for longdistance measurement(>500 m).

Early active immunization with Aβ3–10-KLH vaccine reduces tau phosphorylation in the hippocampus and protects cognition of mice

Active and passive anti-Aβimmunotherapies have successfully been used for the prevention and treatment of Alzheimer’s disease animal models.However,clinical use of these immunotherapies is not effective,because the vaccination is administered too late.At 1 month of age,100μL of Aβ3–10-KLH peptide(vaccine,2μg/μL)was subcutaneously injected into the neck of an amyloid precursor protein/presenilin-1/tau transgenic(3×Tg-AD)mouse model.Aβ3–10-KLH peptide was re-injected at 1.5,2.5,3.5,4.5,5.5,and 6.5 months of age.Serum levels of Aβantibody were detected by enzyme-linked immunosorbent assay,while spatial learning and memory ability were evaluated by Morris water maze.Immunohistochemistry was used to detect total tau with HT7 and phosphorylated tau with AT8(phosphorylation sites Ser202 and Thr205)and AT180(phosphorylation site Thr231)antibodies in the hippocampus.In addition,western blot analysis was used to quantify AT8 and AT180 expression in the hippocampus.The results showed that after vaccine injection,mice produced high levels of Aβantibody,cognitive function was significantly improved,and total tau and phosphorylated tau levels were significantly reduced.These findings suggest that early active immunization with Aβ3–10-KLH vaccine can greatly reduce tau phosphorylation,thereby mitigating the cognitive decline of 3×Tg-AD mice.This study was approved by the Animal Ethics Committee of China Medical University,China(approval No.103-316)on April 2,2016.

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering.However,due to the limitations of current machine learning algorithms,we can’t achieve good effect for defect prediction by only using machine learning algorithms.In previous studies,some researchers used extreme learning machine(ELM)to conduct defect prediction.However,the initial weights and biases of the ELM are determined randomly,which reduces the prediction performance of ELM.Motivated by the idea of search based software engineering,we propose a novel software defect prediction model named KAEA based on kernel principal component analysis(KPCA),adaptive genetic algorithm,extreme learning machine and Adaboost algorithm,which has three main advantages:(1)KPCA can extract optimal representative features by leveraging a nonlinear mapping function;(2)We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM,so as to improve the generalization ability and prediction capacity of ELM;(3)We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor,which can further improve the effect of defect prediction.To effectively evaluate the performance of KAEA,we use eleven datasets from large open source projects,and compare the KAEA with four machine learning basic classifiers,ELM and its three variants.The experimental results show that KAEA is superior to these baseline models in most cases.

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'.

CO2 absorption performance in a rotating disk reactor using DBU-glycerol as solvent

Gas–liquid mass transfer of rotating disk reactor was studied in CO2 absorption using 1,8-diazabicyclo-[5.4.0]-undec-7-ene(DBU)-glycerol solution as solvent. Effects of the rotating disk structure and various operation parameters on the CO2 absorption rate and CO2 removal efficiency were investigated. The rotating disk with optimal holes is conducive to mass transfer of CO2 and the formation of thin liquid film at the opening increases the gas–liquid contact area. With the increase of rotating speed, the liquid flow pattern on the rotating disk surface changes from thin film flow to separated streams and creates extra liquid lines attached to the rim of the disk,which leads to a very complicated change on the CO2 absorption rate and CO2 removal efficiency. The overall gas-phase mass transfer coefficient increases 138% as the rotating speed increasing from 250 to 1400 r·min^-1.Increasing temperature from 298 to 338 K can enhance the CO2 absorption rate due to lowering the viscosity of the solvent. The rate-determined step for the absorption is focused on the gas side. The rotating disk reactor can effectively enhance the absorption of CO2 with viscous DBU-glycerol solvents.

Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

A novel 4D resolution imaging method for low and medium atomic number objects at the centimeter scale by coincidence detection technique of cosmic-ray muon and its secondary particles

The muon radiography imaging technique for high-atomic-number objects(Z)and large-volume objects via muon transmission imaging and muon multiple scattering imaging remains a popular topic in the field of radiation detection imaging.However,few imaging studies have been reported on low and medium Z objects at the centimeter scale.This paper presents an imaging system that consists of three layers of a position-sensitive detector and four plastic scintillation detectors.It acquires data by coincidence detection technique of cosmic-ray muon and its secondary particles.A 3D imaging algorithm based on the density of the coinciding muon trajectory was developed,and 4D imaging that takes the atomic number dimension into account by considering the secondary particle ratio information was achieved.The resultant reconstructed 3D images could distinguish between a series of cubes with 5-mm-side lengths and 2-mm-intervals.If the imaging time is more than 20 days,this method can distinguish intervals with a width of 1 mm.The 4D images can specify target objects with low,medium,and high Z values.

Detailed study of RF properties of cold models for CW window-type RFQ

A 50 mA CW deuteron RFQ is being built for a joint 973 project between Peking University and the Institute of Modern Physics. This RFQ adopts a high-frequency window-type structure. To study its RF properties and to validate the reliability of an electromagnetic simulation, two full-length aluminum models with tuners were built in succession. RF measurements were obtained from the test bench and compared to the simulations, including frequencies, quality factors, and electric fields of different modes and the field in aperture. Through field tuning, the maximal field unflatness for a single quadrant and the average asymmetry of four quadrants were reduced from 8.7% and ± 3.6% to 5.8% and ± 1.7%, respectively.Moreover, a tuning method of adjusting the gap distance between the endplates and the vanes was also studied in this paper.

Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids

As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.