BACKGROUND Thiopurine-induced leucopenia significantly hinders the wide application of thiopurines.Dose optimization guided by nudix hydrolase 15(NUDT15)has significantly reduced the early leucopenia rate,but there ar...BACKGROUND Thiopurine-induced leucopenia significantly hinders the wide application of thiopurines.Dose optimization guided by nudix hydrolase 15(NUDT15)has significantly reduced the early leucopenia rate,but there are no definitive biomarkers for late risk leucopenia prediction.AIM To determine the predictive value of early monitoring of DNA-thioguanine(DNATG)or 6-thioguanine nucleotides(6TGN)for late leucopenia under a NUDT15-guided thiopurine dosing strategy in patients with Crohn’s disease(CD).METHODS Blood samples were collected within two months after thiopurine initiation for detection of metabolite concentrations.Late leucopenia was defined as a leukocyte count<3.5×10^(9)/L over two months.RESULTS Of 148 patients studied,late leucopenia was observed in 15.6%(17/109)of NUDT15/thiopurine methyltransferase(TPMT)normal and 64.1%(25/39)of intermediate metabolizers.In patients suffering late leucopenia,early DNATG levels were significantly higher than in those who did not develop late leucopenia(P=4.9×10^(-13)).The DNATG threshold of 319.43 fmol/μg DNA could predict late leucopenia in the entire sample with an area under the curve(AUC)of 0.855(sensitivity 83%,specificity 81%),and in NUDT15/TPMT normal metabolizers,the predictive performance of a threshold of 315.72 fmol/μg DNA was much more remarkable with an AUC of 0.902(sensitivity 88%,specificity 85%).6TGN had a relatively poor correlation with late leucopenia whether in the entire sample(P=0.021)or NUDT15/TPMT normal or intermediate metabolizers(P=0.018,P=0.55,respectively).CONCLUSION Proactive therapeutic drug monitoring of DNATG could be an effective strategy to prevent late leucopenia in both NUDT15/TPMT normal and intermediate metabolizers with CD,especially the former.展开更多
This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamm...This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamming-assisted spec-trum monitoring scheme via spectrum monitoring data(SMD)transmission is proposed to maximize the sum ergodic monitoring rate at SM.In SWPC,the suspi-cious communications of each data block occupy mul-tiple independent blocks,with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster(symmetric scene)or randomly distributed(asymmet-ric scene)and the remaining blocks used for the in-formation transmission from suspicious transmitters(STs)to suspicious destination(SD).For the sym-metric scene,with a given number of blocks for SMD transmission,namely the jamming operation,we first reveal that SM should transmit SMD signal(jam the SD)with tolerable maximum power in the given blocks.The perceived suspicious signal power at SM could be maximized,and thus so does the correspond-ing sum ergodic monitoring rate.Then,we further reveal one fundamental trade-off in deciding the op-timal number of given blocks for SMD transmission.For the asymmetric scene,a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance.Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better perfor-mance than conventional passive spectrum monitor-ing,since the proposed schemes can obtain more accu-rate and effective spectrum characteristic parameters,which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.展开更多
Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are ...Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.展开更多
The mode of telecommunication network management is changing from“network oriented”to“subscriber oriented”.Aimed at enhancing subscribers’feeling,proactive performance monitoring(PPM)can enable a fast fault corre...The mode of telecommunication network management is changing from“network oriented”to“subscriber oriented”.Aimed at enhancing subscribers’feeling,proactive performance monitoring(PPM)can enable a fast fault correction by detecting anomalies designating performance degradation.In this paper,a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average(ARIMA).Furthermore,under the assumption that the training residual is a white noise process following a normal distribution,the associated confidence interval of prediction can be figured out under any given confidence degree 1–αby constructing random variables satisfying t distribution.Experimental results verify the method’s effectiveness.展开更多
基金Supported by the National Natural Science Foundation of China,No.82020108031,No.81973398,and No.82104290Guangdong Provincial Key Laboratory of Construction Foundation,No.2020B1212060034Guangdong Basic and Applied Basic Research Foundation,No.2022A1515012549 and No.2023A1515012667.
文摘BACKGROUND Thiopurine-induced leucopenia significantly hinders the wide application of thiopurines.Dose optimization guided by nudix hydrolase 15(NUDT15)has significantly reduced the early leucopenia rate,but there are no definitive biomarkers for late risk leucopenia prediction.AIM To determine the predictive value of early monitoring of DNA-thioguanine(DNATG)or 6-thioguanine nucleotides(6TGN)for late leucopenia under a NUDT15-guided thiopurine dosing strategy in patients with Crohn’s disease(CD).METHODS Blood samples were collected within two months after thiopurine initiation for detection of metabolite concentrations.Late leucopenia was defined as a leukocyte count<3.5×10^(9)/L over two months.RESULTS Of 148 patients studied,late leucopenia was observed in 15.6%(17/109)of NUDT15/thiopurine methyltransferase(TPMT)normal and 64.1%(25/39)of intermediate metabolizers.In patients suffering late leucopenia,early DNATG levels were significantly higher than in those who did not develop late leucopenia(P=4.9×10^(-13)).The DNATG threshold of 319.43 fmol/μg DNA could predict late leucopenia in the entire sample with an area under the curve(AUC)of 0.855(sensitivity 83%,specificity 81%),and in NUDT15/TPMT normal metabolizers,the predictive performance of a threshold of 315.72 fmol/μg DNA was much more remarkable with an AUC of 0.902(sensitivity 88%,specificity 85%).6TGN had a relatively poor correlation with late leucopenia whether in the entire sample(P=0.021)or NUDT15/TPMT normal or intermediate metabolizers(P=0.018,P=0.55,respectively).CONCLUSION Proactive therapeutic drug monitoring of DNATG could be an effective strategy to prevent late leucopenia in both NUDT15/TPMT normal and intermediate metabolizers with CD,especially the former.
基金the Natural Science Foun-dations of China(No.62171464,61771487)the Defense Science Foundation of China(No.2019-JCJQ-JJ-221).
文摘This paper studies the proactive spec-trum monitoring with one half-duplex spectrum moni-tor(SM)to cope with the potential suspicious wireless powered communications(SWPC)in dynamic spec-trum sharing networks.The jamming-assisted spec-trum monitoring scheme via spectrum monitoring data(SMD)transmission is proposed to maximize the sum ergodic monitoring rate at SM.In SWPC,the suspi-cious communications of each data block occupy mul-tiple independent blocks,with a block dedicated to the wireless energy transfer by the energy-constrained suspicious nodes with locations in a same cluster(symmetric scene)or randomly distributed(asymmet-ric scene)and the remaining blocks used for the in-formation transmission from suspicious transmitters(STs)to suspicious destination(SD).For the sym-metric scene,with a given number of blocks for SMD transmission,namely the jamming operation,we first reveal that SM should transmit SMD signal(jam the SD)with tolerable maximum power in the given blocks.The perceived suspicious signal power at SM could be maximized,and thus so does the correspond-ing sum ergodic monitoring rate.Then,we further reveal one fundamental trade-off in deciding the op-timal number of given blocks for SMD transmission.For the asymmetric scene,a low-complexity greedy block selection scheme is proposed to guarantee the optimal performance.Simulation results show that the jamming-assisted spectrum monitoring schemes via SMD transmission achieve much better perfor-mance than conventional passive spectrum monitor-ing,since the proposed schemes can obtain more accu-rate and effective spectrum characteristic parameters,which provide basic support for fine-grained spectrum management and a solution for spectrum security in dynamic spectrum sharing network.
文摘Ever since the research in machine learning gained traction in recent years,it has been employed to address challenges in a wide variety of domains,including mechanical devices.Most of the machine learning models are built on the assumption of a static learning environment,but in practical situations,the data generated by the process is dynamic.This evolution of the data is termed concept drift.This research paper presents an approach for predictingmechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment.The method proposed here is applicable to allmechanical devices that are susceptible to failure or operational degradation.The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation.The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature.It is observed that,in the settings without concept drift in the data,methods like SVM and Random Forest performed better compared to deep neural networks.However,this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift.In this perspective,DNN generated the stable drift detection method;it reported an accuracy of 84%and an AUC of 0.87 while detecting only a single drift point,indicating the stability to performbetter in detecting and adapting to new data in the drifting environments under industrial measurement settings.
基金supported by the National Key Technologies R&D program of China during the 11th Five-Year Plan Period (No.2006BAH02A03).
文摘The mode of telecommunication network management is changing from“network oriented”to“subscriber oriented”.Aimed at enhancing subscribers’feeling,proactive performance monitoring(PPM)can enable a fast fault correction by detecting anomalies designating performance degradation.In this paper,a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average(ARIMA).Furthermore,under the assumption that the training residual is a white noise process following a normal distribution,the associated confidence interval of prediction can be figured out under any given confidence degree 1–αby constructing random variables satisfying t distribution.Experimental results verify the method’s effectiveness.
