Objective:To evaluate the predictive value of secreted phosphoprotein 1(SPP1)gene expression for postoperative survival in patients with advanced liver cancer undergoing hepatic artery interventional chemoembolization...Objective:To evaluate the predictive value of secreted phosphoprotein 1(SPP1)gene expression for postoperative survival in patients with advanced liver cancer undergoing hepatic artery interventional chemoembolization treatment.Method:Bioinformatics methods,including gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis,were used to identify genes related to survival prognosis in hepatocellular carcinoma(HCC)patients.A retrospective analysis of 115 advanced liver cancer patients treated between January 2016 and October 2017 was conducted.Patients were categorized into SPP1 high-expression(n=89)and low-expression groups(n=26).Additionally,115 healthy individuals served as the control group.The relationship between SPP1 expression and clinical pathological features was analyzed.A 60-month follow-up and logistic regression analysis identified risk factors affecting survival.Results:SPP1 mRNA expression was significantly higher in liver cancer patients compared to healthy controls(P<0.05).SPP1 expression levels were significantly associated with tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging(P<0.05).High SPP1 expression,along with tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging,were independent risk factors for survival(P<0.05).The 60-month survival rate was 17.39%,with a median survival of 40 months in the low-expression group versus 18 months in the high-expression group(P<0.05).Conclusion:SPP1 expression is significantly upregulated in advanced liver cancer patients and has predictive value for postoperative survival following hepatic artery chemoembolization treatment.SPP1,combined with clinical indicators such as tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging,may serve as a prognostic biomarker for interventional treatment outcomes.展开更多
Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which ca...Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNsS satellites.Pres-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)series.However,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC.In light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term prediction.By using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid methods.According to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LoD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28%.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20%.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further reduction.展开更多
Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currentl...Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.展开更多
Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g...Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.展开更多
AIM:To determine if serum inter-cellular adhesion molecule 1(ICAM-1)is an early marker of the diagnosis and prediction of severe acute pancreatitis(SAP) within 24 h of onset of pain,and to compare the sensitivity,spec...AIM:To determine if serum inter-cellular adhesion molecule 1(ICAM-1)is an early marker of the diagnosis and prediction of severe acute pancreatitis(SAP) within 24 h of onset of pain,and to compare the sensitivity,specificity and prognostic value of this test with those of acute physiology and chronic health evaluation(APACHE)Ⅱscore and interleukin-6(IL-6). METHODS:Patients with acute pancreatitis(AP)were divided into two groups according to the Ranson's criteria:mild acute pancreatitis(MAP)group and SAP group.Serum ICAM-1,APACHEⅡand IL-6 levels were detected in all the patients.The sensitivity,specificity and prognostic value of the ICAM-1,APACHEⅡscore and IL-6 were evaluated. RESULTS:The ICAM-1 level in 36 patients with SAP within 24 h of onset of pain was increased and was significantly higher than that in the 50 patients with MAP and the 15 healthy volunteers(P<0.01).The ICAM-1 level(25 ng/mL)was chosen as the optimum cutoff to distinguish SAP from MAP,and the sensitivity,specificity,positive predictive value,negative predictive value(NPV),positive likelihood ratio and negative likelihood ratio were 61.11%,71.42%,0.6111,0.7142, 2.1382 and 0.5445,respectively.The area under the curve demonstrated that the prognostic accuracy of ICAM-1(0.712)was similar to the APACHE-Ⅱscoring system(0.770)and superior to IL-6(0.508)in distinguishing SAP from MAP. CONCLUSION:ICAM-1 test is a simple,rapid and reliable method in clinical practice.It is an early marker of diagnosis and prediction of SAP within the first 24 h after onset of pain or on admission.As it has a relatively low NPV and does not allow it to be a stand-alone test for the diagnosis of AP,other conventional diagnostic tests are required.展开更多
As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have a...As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ～1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.展开更多
Objective:To study the drug activity and therapeutic targets of Niga-ichigoside F1 predicted based on network pharmacology and molecular docking.Methods:Download the 2D and 3D structures of Niga-ichigoside F1 from the...Objective:To study the drug activity and therapeutic targets of Niga-ichigoside F1 predicted based on network pharmacology and molecular docking.Methods:Download the 2D and 3D structures of Niga-ichigoside F1 from the PubChem database for target prediction and molecular docking,respectively.Target information was predicted by PharmMapper and swiss ADME databases,target gene names were extracted and rechecked by Uniprot database,and disease information corresponding to target was queried by TTD database.The enrichment analysis of GO and KEGG signal pathway was conducted by Metascape database.AutoDuck Vina was used for molecular docking of Niga-ichigoside F1 3D structure with key proteins of related diseases and common pathways.Finally,the conformation of molecular docking was visualized by PyMOL.Results:A total of 34 targets and 69 related disease information were obtained from the database screening.The targets with high degree of acquisition of the association network between target and disease were AR,F2,VDR,PDE10A,mTOR,and NR3C2,etc..Diseases with a high degree of relief were solid tumour,breast cancer, acute myeloid leukemia, hypertension, and thrombocytopenia,etc..The items with significance in GO analysis included positive regulation of transferase activity,protein autophosphorylation,negative regulation of cGMP-mediated signaling,intracellular receptor signaling pathway,regulation of cellular response to stress,blood vessel development,reactive oxygen species metabolic process,negative regulation of immune response,regulation of transcription from RNA polymerase Ⅱ promoter in response to stress,and nucleobase-containing small molecule metabolic process,etc..The items with significance in KEGG enrichment analysis(P<0.01) included Pathways in cancer,Purine metabolism,Focal adhesion,MAPK signaling pathway,GnRH signaling pathway,AGE-RAGE signaling pathway in diabetic complications,Ras signaling pathway,Leukocyte transendothelial migration and Platelet activation,etc..Molecular docking suggested that the target of Niga-ichigoside F1 had good binding ability with related diseases and key proteins of common pathways.Conclusion:According to the results of network pharmacology and molecular docking,Niga-ichigoside F1 has rich drug activity and may act on a variety of diseases.After comprehensive analysis, we proposed for the first time the high correlation between Niga-ichigoside F1 and cancer,as well as the possible association with acute myeloid leukemia and hypertension.It has the characteristics of multi-target and multi-pathway,which is worthy of further research,development and utilization.展开更多
To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new ...To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.展开更多
Based on the prediction results of over twenty new climate models provided by Intergovernmental Panel on Climate Change(IPCC) ,the climate change trends in Yangtze-Huaihe region during 2011-2100 were analyzed under th...Based on the prediction results of over twenty new climate models provided by Intergovernmental Panel on Climate Change(IPCC) ,the climate change trends in Yangtze-Huaihe region during 2011-2100 were analyzed under the SRES A1B scenario. The results showed that annual mean temperature in Yangtze-Huaihe region would go up gradually under the background of global warming,and temperature increase rose from southeast to northwest,while annual average temperature would increase by 3.3 ℃ in the late 20th century. Meanwhile,annual average precipitation would rise persistently,and precipitation increase would go up with the increase of latitude and the lapse of time,being obviously strengthened after 2041.展开更多
目的探讨幽门螺杆菌(Hp)阳性早期胃癌患者肿瘤组织中转化生长因子-β_(1)(TGF-β_(1))mRNA、金属基质蛋白酶-2(MMP-2)mRNA、青霉素结合蛋白1A(PBP1A)m RNA表达水平与复发的关系,并分析其对复发的预测价值。方法选取2018年3月至2023年7...目的探讨幽门螺杆菌(Hp)阳性早期胃癌患者肿瘤组织中转化生长因子-β_(1)(TGF-β_(1))mRNA、金属基质蛋白酶-2(MMP-2)mRNA、青霉素结合蛋白1A(PBP1A)m RNA表达水平与复发的关系,并分析其对复发的预测价值。方法选取2018年3月至2023年7月南阳市中心医院收治的214例Hp阳性早期胃癌患者进行前瞻性研究,所有患者均行内镜黏膜下剥离术(ESD),采用实时荧光定量聚合酶链反应(qRT-PCR)法检测肿瘤组织、癌旁组织中TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平,并分析其与临床病理特征相关性。依据ESD术后是否复发分为复发组、未复发组,采用q RT-PCR法检测两组患者的TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平。采用偏相关性分析肿瘤组织中TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平与复发的关系。采用受试者工作特征(ROC)曲线分析TGF-β_(1)m RNA、MMP-2 mRNA、PBP1A m RNA表达水平对复发的预测价值。结果肿瘤组织中TGF-β_(1)mRNA、MMP-2 m RNA表达水平分别为1.04±0.26、1.45±0.31,明显高于癌旁组织的0.85±0.14、1.18±0.25,PBP1A m RNA表达水平为0.31±0.10,明显低于癌旁组织的0.43±0.12,差异均有统计学意义(P<0.05);列联相关系数C分析显示,肿瘤组织中TGF-β_(1)mRNA、MMP-2 m RNA表达水平与临床分期、浸润深度、淋巴结转移呈正相关(P<0.05),与分化程度呈负相关(P<0.05),而PBP1A m RNA表达水平与临床分期、浸润深度、淋巴结转移呈负相关(P<0.05),与分化程度呈正相关(P<0.05);复发组患者的TGF-β_(1)mRNA、MMP-2 m RNA表达水平分别为1.31±0.25、1.74±0.31,明显高于未复发组的1.01±0.20、1.42±0.25,PBP1A mRNA表达水平为0.18±0.05,明显低于未复发组的0.32±0.10,差异均有统计学意义(P<0.05);偏相关性分析显示,肿瘤组织中TGF-β_(1)mRNA、MMP-2m RNA、PBP1A m RNA表达水平与复发显著相关(P<0.05);TGF-β_(1)mRNA、MMP-2 mRNA、PBP1A mRNA单项及联合预测复发的曲线下面积(AUC)分别为0.755、0.742、0.795、0.915,敏感度为75.00%、70.00%、75.00%、80.00%,特异度为72.25%、67.54%、76.95%、95.81%,且预测效能显著高于各指标单独预测价值(Z=2.376、2.413、1.997,P=0.018、0.016、0.046)。结论Hp阳性早期胃癌患者肿瘤组织中TGF-β_(1)m RNA、MMP-2 m RNA表达水平升高,PBP1A m RNA表达水平降低,且与临床病理特征、复发密切相关,联合检测其水平对复发具有较高的预测价值。展开更多
In this paper, an improved weighted least squares （WLS）, together with autoregressive （AR） model, is proposed to improve prediction accuracy of earth rotation parameters（ERP）. Four weighting schemes are develope...In this paper, an improved weighted least squares （WLS）, together with autoregressive （AR） model, is proposed to improve prediction accuracy of earth rotation parameters（ERP）. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.展开更多
Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the ...Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.展开更多
In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.B...In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.展开更多
基金Medical Research Project of Xi’an Science and Technology Bureau“Molecular Mechanism of miR-1305 Competitive Endogenous circRNA in the Development of Liver Cancer”(Project No.22YXYJ0134)General Project of Key Research and Development Program of Shaanxi Provincial Department of Science and Technology“Mechanism Study on the Inhibition of Liver Cancer Invasion and Metastasis by Downregulating METTL3 and Reducing the m6A Modification Level of MMP3 with Honokiol”(Project No.2023-YBSF-631)。
文摘Objective:To evaluate the predictive value of secreted phosphoprotein 1(SPP1)gene expression for postoperative survival in patients with advanced liver cancer undergoing hepatic artery interventional chemoembolization treatment.Method:Bioinformatics methods,including gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis,were used to identify genes related to survival prognosis in hepatocellular carcinoma(HCC)patients.A retrospective analysis of 115 advanced liver cancer patients treated between January 2016 and October 2017 was conducted.Patients were categorized into SPP1 high-expression(n=89)and low-expression groups(n=26).Additionally,115 healthy individuals served as the control group.The relationship between SPP1 expression and clinical pathological features was analyzed.A 60-month follow-up and logistic regression analysis identified risk factors affecting survival.Results:SPP1 mRNA expression was significantly higher in liver cancer patients compared to healthy controls(P<0.05).SPP1 expression levels were significantly associated with tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging(P<0.05).High SPP1 expression,along with tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging,were independent risk factors for survival(P<0.05).The 60-month survival rate was 17.39%,with a median survival of 40 months in the low-expression group versus 18 months in the high-expression group(P<0.05).Conclusion:SPP1 expression is significantly upregulated in advanced liver cancer patients and has predictive value for postoperative survival following hepatic artery chemoembolization treatment.SPP1,combined with clinical indicators such as tumor size,Child-Pugh grading,lymph node metastasis,and BCLC staging,may serve as a prognostic biomarker for interventional treatment outcomes.
基金supported by China Natural Science Fund,China(No.42004016)the science and technology innovation Program of Hunan Province,China(No.2023RC3217)+1 种基金Research Foundation of the Department of Natural Resources of Hunan Province(Grant No:20240105CH)HuBei Natural Science Fund,China(No.2020CFB329).
文摘Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNsS satellites.Pres-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)series.However,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC.In light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term prediction.By using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid methods.According to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LoD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28%.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20%.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further reduction.
基金supported by National Natural Science Foundation of China,China(No.42004016)HuBei Natural Science Fund,China(No.2020CFB329)+1 种基金HuNan Natural Science Fund,China(No.2023JJ60559,2023JJ60560)the State Key Laboratory of Geodesy and Earth’s Dynamics self-deployment project,China(No.S21L6101)。
文摘Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.
基金funded by the National Natural Science Foundation of China(General Program:No.52074314,No.U19B6003-05)National Key Research and Development Program of China(2019YFA0708303-05)。
文摘Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.
文摘AIM:To determine if serum inter-cellular adhesion molecule 1(ICAM-1)is an early marker of the diagnosis and prediction of severe acute pancreatitis(SAP) within 24 h of onset of pain,and to compare the sensitivity,specificity and prognostic value of this test with those of acute physiology and chronic health evaluation(APACHE)Ⅱscore and interleukin-6(IL-6). METHODS:Patients with acute pancreatitis(AP)were divided into two groups according to the Ranson's criteria:mild acute pancreatitis(MAP)group and SAP group.Serum ICAM-1,APACHEⅡand IL-6 levels were detected in all the patients.The sensitivity,specificity and prognostic value of the ICAM-1,APACHEⅡscore and IL-6 were evaluated. RESULTS:The ICAM-1 level in 36 patients with SAP within 24 h of onset of pain was increased and was significantly higher than that in the 50 patients with MAP and the 15 healthy volunteers(P<0.01).The ICAM-1 level(25 ng/mL)was chosen as the optimum cutoff to distinguish SAP from MAP,and the sensitivity,specificity,positive predictive value,negative predictive value(NPV),positive likelihood ratio and negative likelihood ratio were 61.11%,71.42%,0.6111,0.7142, 2.1382 and 0.5445,respectively.The area under the curve demonstrated that the prognostic accuracy of ICAM-1(0.712)was similar to the APACHE-Ⅱscoring system(0.770)and superior to IL-6(0.508)in distinguishing SAP from MAP. CONCLUSION:ICAM-1 test is a simple,rapid and reliable method in clinical practice.It is an early marker of diagnosis and prediction of SAP within the first 24 h after onset of pain or on admission.As it has a relatively low NPV and does not allow it to be a stand-alone test for the diagnosis of AP,other conventional diagnostic tests are required.
基金supported by Discipline Innovative Engineering Plan of Modern Geodesy and Geodynamics(grant No.B17033)NSFC grants(11673049,11773057)RFBR grant(N16-05-00753)
文摘As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ～1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO.
基金National Natural Science Foundation of China(No.82060855)。
文摘Objective:To study the drug activity and therapeutic targets of Niga-ichigoside F1 predicted based on network pharmacology and molecular docking.Methods:Download the 2D and 3D structures of Niga-ichigoside F1 from the PubChem database for target prediction and molecular docking,respectively.Target information was predicted by PharmMapper and swiss ADME databases,target gene names were extracted and rechecked by Uniprot database,and disease information corresponding to target was queried by TTD database.The enrichment analysis of GO and KEGG signal pathway was conducted by Metascape database.AutoDuck Vina was used for molecular docking of Niga-ichigoside F1 3D structure with key proteins of related diseases and common pathways.Finally,the conformation of molecular docking was visualized by PyMOL.Results:A total of 34 targets and 69 related disease information were obtained from the database screening.The targets with high degree of acquisition of the association network between target and disease were AR,F2,VDR,PDE10A,mTOR,and NR3C2,etc..Diseases with a high degree of relief were solid tumour,breast cancer, acute myeloid leukemia, hypertension, and thrombocytopenia,etc..The items with significance in GO analysis included positive regulation of transferase activity,protein autophosphorylation,negative regulation of cGMP-mediated signaling,intracellular receptor signaling pathway,regulation of cellular response to stress,blood vessel development,reactive oxygen species metabolic process,negative regulation of immune response,regulation of transcription from RNA polymerase Ⅱ promoter in response to stress,and nucleobase-containing small molecule metabolic process,etc..The items with significance in KEGG enrichment analysis(P<0.01) included Pathways in cancer,Purine metabolism,Focal adhesion,MAPK signaling pathway,GnRH signaling pathway,AGE-RAGE signaling pathway in diabetic complications,Ras signaling pathway,Leukocyte transendothelial migration and Platelet activation,etc..Molecular docking suggested that the target of Niga-ichigoside F1 had good binding ability with related diseases and key proteins of common pathways.Conclusion:According to the results of network pharmacology and molecular docking,Niga-ichigoside F1 has rich drug activity and may act on a variety of diseases.After comprehensive analysis, we proposed for the first time the high correlation between Niga-ichigoside F1 and cancer,as well as the possible association with acute myeloid leukemia and hypertension.It has the characteristics of multi-target and multi-pathway,which is worthy of further research,development and utilization.
基金Supported by Science Research Project of Department of Education of Hubei Province (B20092901)~~
文摘To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.
基金Supported by Research Fund Project of Nanjing University of Information Science & Technology(9922)
文摘Based on the prediction results of over twenty new climate models provided by Intergovernmental Panel on Climate Change(IPCC) ,the climate change trends in Yangtze-Huaihe region during 2011-2100 were analyzed under the SRES A1B scenario. The results showed that annual mean temperature in Yangtze-Huaihe region would go up gradually under the background of global warming,and temperature increase rose from southeast to northwest,while annual average temperature would increase by 3.3 ℃ in the late 20th century. Meanwhile,annual average precipitation would rise persistently,and precipitation increase would go up with the increase of latitude and the lapse of time,being obviously strengthened after 2041.
文摘目的探讨幽门螺杆菌(Hp)阳性早期胃癌患者肿瘤组织中转化生长因子-β_(1)(TGF-β_(1))mRNA、金属基质蛋白酶-2(MMP-2)mRNA、青霉素结合蛋白1A(PBP1A)m RNA表达水平与复发的关系,并分析其对复发的预测价值。方法选取2018年3月至2023年7月南阳市中心医院收治的214例Hp阳性早期胃癌患者进行前瞻性研究,所有患者均行内镜黏膜下剥离术(ESD),采用实时荧光定量聚合酶链反应(qRT-PCR)法检测肿瘤组织、癌旁组织中TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平,并分析其与临床病理特征相关性。依据ESD术后是否复发分为复发组、未复发组,采用q RT-PCR法检测两组患者的TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平。采用偏相关性分析肿瘤组织中TGF-β_(1)m RNA、MMP-2 m RNA、PBP1A m RNA表达水平与复发的关系。采用受试者工作特征(ROC)曲线分析TGF-β_(1)m RNA、MMP-2 mRNA、PBP1A m RNA表达水平对复发的预测价值。结果肿瘤组织中TGF-β_(1)mRNA、MMP-2 m RNA表达水平分别为1.04±0.26、1.45±0.31,明显高于癌旁组织的0.85±0.14、1.18±0.25,PBP1A m RNA表达水平为0.31±0.10,明显低于癌旁组织的0.43±0.12,差异均有统计学意义(P<0.05);列联相关系数C分析显示,肿瘤组织中TGF-β_(1)mRNA、MMP-2 m RNA表达水平与临床分期、浸润深度、淋巴结转移呈正相关(P<0.05),与分化程度呈负相关(P<0.05),而PBP1A m RNA表达水平与临床分期、浸润深度、淋巴结转移呈负相关(P<0.05),与分化程度呈正相关(P<0.05);复发组患者的TGF-β_(1)mRNA、MMP-2 m RNA表达水平分别为1.31±0.25、1.74±0.31,明显高于未复发组的1.01±0.20、1.42±0.25,PBP1A mRNA表达水平为0.18±0.05,明显低于未复发组的0.32±0.10,差异均有统计学意义(P<0.05);偏相关性分析显示,肿瘤组织中TGF-β_(1)mRNA、MMP-2m RNA、PBP1A m RNA表达水平与复发显著相关(P<0.05);TGF-β_(1)mRNA、MMP-2 mRNA、PBP1A mRNA单项及联合预测复发的曲线下面积(AUC)分别为0.755、0.742、0.795、0.915,敏感度为75.00%、70.00%、75.00%、80.00%,特异度为72.25%、67.54%、76.95%、95.81%,且预测效能显著高于各指标单独预测价值(Z=2.376、2.413、1.997,P=0.018、0.016、0.046)。结论Hp阳性早期胃癌患者肿瘤组织中TGF-β_(1)m RNA、MMP-2 m RNA表达水平升高,PBP1A m RNA表达水平降低,且与临床病理特征、复发密切相关,联合检测其水平对复发具有较高的预测价值。
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China (2007B51)Natural Science Foundation of China (41174008)
文摘In this paper, an improved weighted least squares （WLS）, together with autoregressive （AR） model, is proposed to improve prediction accuracy of earth rotation parameters（ERP）. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.
基金supported by the National Natural Science Foundation of China(Grants No.51009080 and 51179095)the Research Innovation Fund for Postgraduates in China Three Gorges University(Grant No.2012CX012)
文摘Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.
基金supported by the National Natural Science Foundation of China(7084001290924022)the Ph.D.Thesis Innovation and Excellent Foundation of Nanjing University of Aeronautics and Astronautics(2010)
文摘In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.
